Hand Posture Recognition in a Body-Face centered space
在演讲中如何使用肢体语言,英语作文
在演讲中如何使用肢体语言,英语作文Body language is an essential aspect of public speaking because it can enhance the delivery of your message and help captivate your audience. 身体语言是公开演讲中一个重要的方面，因为它可以增强你传达信息的能力，帮助吸引你的听众。
First and foremost, maintaining good posture is crucial when giving a speech. 首先，当你演讲时保持良好的姿势是至关重要的。
Standing or sitting up straight not only conveys confidence and authority, but it also allows for better breathing, which can help you project your voice more effectively. 直立或坐直不仅传达出自信和权威，而且还有助于更好地呼吸，这可以帮助你更有效地发声。
Gestures such as hand movements can also add emphasis to your words and make your speech more engaging. 手势，例如手部动作，还可以强调你的话语并使你的演讲更具吸引力。
However, it is important to use gestures sparingly and purposefully, as excessive or random movements can be distracting and detract from your message. 然而，重要的是要节制地和有目的地使用手势，因为过多或随意的动作会分散注意力，削弱你的信息传达效果。
中美肢体语言介绍信英语作文
中美肢体语言介绍信英语作文【中英文版】Introduction Letter on Body Language Differences between China and the United StatesDear Readers,Body language plays a significant role in communication, yet it can vary drastically across different cultures.In this letter, I aim to shed light on the intriguing differences between Chinese and American body language.在中国和美国，肢体语言在交流中起着至关重要的作用。
然而，这两个国家的肢体语言差异却相当显著。
在此，我将为大家揭示中美肢体语言之间的有趣差异。
Firstly, when it comes to personal space, Americans tend to value a larger bubble of personal space compared to the Chinese.In the United States, invade someone"s personal space can be considered disrespectful, while in China, people are more accustomed to closer physical proximity.首先，在个人空间方面，美国人相较于中国人更注重较大的个人空间。
在美国，侵犯他人的个人空间可能被视为不尊重，而在中国，人们更习惯于较近的身体距离。
Secondly, eye contact is another interesting aspect to consider.Americans often view direct eye contact as a sign of confidence and honesty, whereas in China, excessive eye contact can be perceived asdisrespectful or confrontational, especially when speaking to someone of higher authority.其次，眼神交流也是一个有趣的方面。
手势识别中手分割算法
手势识别中手分割算法郭雷【摘要】Technical difficulties in hand segmentation as well as and the features that might be used in this process are generally analyzed at the beginning in this paper. After that, the ideas and features of existing hand segmentation algorithm are introduced and compared. At last, an introduction of deep learning technology and a conclusion of the future research direction of hand segmentation are carried out in this paper.%首先分析了手势分割存在的技术难点及人进行手势分割过程中可能使用的特征,然后分析比较了现有手势分割算法的基本思想和特点,最后介绍了深度学习技术并总结了手势分割未来的研究方向.【期刊名称】《软件》【年(卷),期】2015(036)004【总页数】3页(P81-83)【关键词】RGB-D;手分割;手势【作者】郭雷【作者单位】南京工业职业技术学院计算机与软件学院,江苏南京 210023【正文语种】中文【中图分类】TP317.4手势是一种不需要中间媒介的，非常人性化的人机交互方式。
手势识别已经成为人机交互的重要内容和研究热点。
基于视觉的手势识别系统通常包含手势分割、手势建模、手势形状特征提取、手势识别几个过程。
其中，手势分割就是将感兴趣的有意义区域，即手势从传感器获取数据中划分出来。
这是基于视觉的手势识别过程中的第一个步骤，也是关键的一步。
分割的准确度和实时性能直接影响到后期的识别效果以及整个交互系统的性能。
面部识别解锁的英语作文
The Embrace of Facial Recognition UnlockingTechnologyIn today's era of technological advancements, facial recognition unlocking has become a common sight, revolutionizing the way we interact with our devices. This technology, which has been in existence for quite some time, has made significant strides in recent years, thanks to the improvements in artificial intelligence and machine learning. Facial recognition unlocking not only enhances security but also adds a personal touch to our digital lives.The concept of facial recognition is based on theunique features of a person's face, such as the shape ofthe eyes, nose, mouth, and other distinguishing characteristics. This technology compares the facialfeatures captured by a camera with the pre-stored data to verify the identity of an individual. Once the match is confirmed, the device unlocks, providing无缝访问 to its features.The adoption of facial recognition unlocking in various devices, including smartphones, laptops, and even cars, hasbeen rapid. This is primarily due to its convenience and added security. Gone are the days where we had to remember complex passwords or fumble with physical keys. With facial recognition unlocking, all it takes is a glance, and you're in.However, the rise of this technology has not been without its controversies. One of the primary concerns is the privacy implications. With facial recognition becoming more widespread, there are fears that our privacy could be compromised. Governments and corporations could potentially misuse this technology, leading to a surveillance state where our every move is being watched.Moreover, the accuracy of facial recognition technology has also been questioned. There have been instances where the technology has failed to recognize faces correctly, leading to false positives or negatives. This could potentially lead to security breaches or inconvenient situations.Despite these concerns, the benefits of facial recognition unlocking far outweigh the risks. It has made our lives easier and more convenient, and it has alsoenhanced the security of our devices. However, it iscrucial that we are aware of the potential downsides and take necessary precautions to protect our privacy.In conclusion, facial recognition unlocking is a remarkable technology that has revolutionized the way we interact with our devices. Its convenience, personalization, and added security make it a valuable addition to ourdigital lives. However, we must also be vigilant about its potential privacy implications and strive to ensure thatour data remains secure.**面部识别解锁技术的拥抱**在当今科技飞速发展的时代，面部识别解锁已经成为一种常见现象，彻底改变了我们与设备的交互方式。
有用的肢体语言英语作文
有用的肢体语言英语作文Title: The Power of Effective Body Language。
Body language is a universal form of communication that often speaks louder than words. It encompasses facial expressions, gestures, posture, and even subtle movements that convey messages and emotions. In both personal and professional settings, understanding and utilizingeffective body language can significantly enhance communication and build stronger connections. In this essay, we will explore the importance of body language and how it can be utilized to convey confidence, empathy, and authenticity.First and foremost, body language plays a crucial rolein conveying confidence and credibility. Consider a job interview scenario: a candidate who sits up straight, maintains eye contact, and offers a firm handshake immediately appears more self-assured and capable than one who slouches, avoids eye contact, and fidgets nervously.Similarly, in public speaking engagements, a speaker who uses open gestures and maintains a relaxed posture is more likely to captivate and persuade their audience. By consciously adopting confident body language, individuals can exude assurance and competence, leaving a positive impression on others.Moreover, effective body language facilitatesempathetic communication by signaling attentiveness and understanding. Active listening, a key component of empathy, involves not only processing verbal information but also responding with appropriate nonverbal cues. Nodding in agreement, maintaining an open posture, and mirroring the speaker's expressions are all forms of nonverbal communication that convey empathy and validation. In interpersonal relationships, such as during a heartfelt conversation with a friend, the ability to convey empathy through body language fosters deeper connections and strengthens bonds of trust.Furthermore, authentic body language enhances interpersonal relationships by conveying sincerity andgenuineness. People are naturally drawn to those who are authentic and transparent in their communication. A genuine smile, a sincere nod, or a comforting touch can communicate warmth and sincerity far more effectively than words alone. In both personal and professional interactions,authenticity in body language builds rapport and fosters mutual respect. When individuals feel understood and valued, they are more likely to engage positively and collaborate effectively.In addition to its interpersonal benefits, effective body language can also enhance personal well-being andself-awareness. By paying attention to our own body language, we can gain insights into our thoughts, emotions, and intentions. For example, noticing tension in our shoulders or shallow breathing may indicate feelings of stress or discomfort. Through mindful awareness of our body language, we can regulate our emotions and project a senseof calm and confidence to others. Furthermore, cultivating self-awareness in body language empowers individuals toalign their nonverbal cues with their verbal messages, ensuring congruence and authenticity in communication.In conclusion, body language is a powerful tool for communication that transcends linguistic barriers and conveys messages with clarity and impact. Whether in professional settings, social interactions, or personal relationships, the ability to understand and utilize effective body language is invaluable. By conveying confidence, empathy, and authenticity through nonverbal cues, individuals can build stronger connections, foster trust, and enhance overall communication effectiveness. As we continue to refine our understanding and mastery of body language, we unlock new avenues for meaningful connection and mutual understanding in our interactions with others.。
人脸识别在中国的应用在哪里英语作文
人脸识别在中国的应用在哪里英语作文Facial recognition technology has been rapidly advancing in recent years and has found numerous applications across various industries in China. This powerful technology has the ability to identify and verify individuals by analyzing their unique facial features, making it a valuable tool in areas such as security, surveillance, and personalized services. In this essay, we will explore the diverse applications of facial recognition in China and examine its impact on the country's technological landscape.One of the primary applications of facial recognition in China is in the field of public security and law enforcement. The Chinese government has invested heavily in the development of large-scale surveillance systems that utilize facial recognition technology. These systems are integrated into the country's extensive network of CCTV cameras, allowing authorities to track and identify individuals in real-time. This technology has been particularly useful in identifying and apprehending criminal suspects, as well as in monitoring potential security threats.Furthermore, facial recognition has been increasingly adopted in the transportation sector in China. Many public transportation systems, such as subway and train stations, have implemented facial recognition-based payment systems. Passengers can simply walk through designated gates and have their identities verified, eliminating the need for physical tickets or cards. This not only enhances the efficiency of the transportation system but also provides a more convenient and seamless experience for commuters.Another area where facial recognition has found widespread application in China is in the retail and e-commerce industries. Retailers are leveraging this technology to enhance customer experience and improve operational efficiency. Facial recognition systems can be used to identify and greet regular customers, provide personalized product recommendations, and even detect and prevent theft. In the e-commerce sector, facial recognition is being integrated into mobile payment solutions, allowing customers to make purchases securely and conveniently using their facial features.The healthcare industry in China has also benefited from the advancements in facial recognition technology. Hospitals and clinics are using this technology to streamline patient check-in and identification processes, reducing wait times and improving overall patient experience. Additionally, facial recognition is being explored for use in remote healthcare consultations, where patients can beidentified and connected with their medical records seamlessly.Beyond these practical applications, facial recognition technology is also being utilized in the entertainment and social media sectors in China. Platforms like social media and online gaming are integrating facial recognition features to enhance user engagement and personalization. Users can, for example, unlock exclusive features or access personalized content based on their facial characteristics.The widespread adoption of facial recognition in China has not been without its challenges and controversies. Concerns have been raised about privacy and data security, as the collection and storage of biometric data can potentially be misused or abused. The Chinese government has faced criticism for its extensive use of facial recognition-based surveillance systems, which some consider a violation of individual privacy rights.Despite these concerns, the Chinese government has continued to invest heavily in the development and implementation of facial recognition technology. The country's large population, extensive infrastructure, and the government's focus on technological innovation have all contributed to the rapid growth of this industry.In conclusion, the application of facial recognition technology in China has been diverse and far-reaching. From public security andtransportation to retail and healthcare, this powerful technology has transformed numerous industries and has become an integral part of the country's technological landscape. As the technology continues to evolve and become more sophisticated, it will be crucial for policymakers, industry leaders, and the public to address the ethical and privacy concerns associated with its use, ensuring that the benefits of facial recognition are balanced with the protection of individual rights and freedoms.。
Bionic_Hand
Repetitive grasping with anthropomorphic skin-covered hand enables robust haptic recognitionShinya Takamuku,Atsushi Fukuda and Koh HosodaAbstract—Skin is an essential component of artificial hands. It enables the use of object affordance for recognition and control,but due to its intrinsic locality and low density of current tactile sensors,stable and proper manual contacts with the objects are indispensable.Recently,design of hand structure have shown to be effective for adaptive grasping.However, such adaptive design are only introduced to thefingers in existing works of haptics and their role in recognition remains unclear.This paper introduces the design of the Bionic Hand; an anthropomorphic hand with adaptive design introduced to the whole hand and fully covered with sensitive skin. The experiment shows that anthropomorphic design of hand structure enables robust haptic recognition by convergence of object contact conditions into stable representative states through repetitive grasping.The structure of the human hand is found to solve the issue of narrowing down the sensor space for haptic object recognition by morphological computation.I.INTRODUCTIONDevelopment of an artificial hand with the capability of the human hand is one of the grand challenges of robotics. Anthropomorphic hands suit the needs of humanoid hands and prostheses since most products found in our environment are made for the human hand.It is also likely to be easier to adapt for the amputees.While various dexterous hands have been unveiled till today[1][2],issues on the skin still remain as a bottleneck[3].Cutaneous sense provides rich information about manipulation states[4],affordances to lead tool use such as mobility[5],as well as unique information for object recognition;object properties such as stiffness[6] and heat characteristics[7].Furthermore,its deformation and friction properties play essential roles in manipulation[8]. Development of the skin and a method for haptic sensing are prerequisites for the next generation of artificial hands. An intrinsic difficulty of haptic sensing is due to its locality.Tactile sensors are local compared to vision,and its recognition depends heavily on the contact conditions1. The low resolution of current tactile sensors makes the problem even more critical since slight change in contact conditions can dramatically change the sensory output.It is then an important issue how to obtain proper and stable manual contact with the objects.Visual feedback is not always available for this purpose due to occlusions.Even if rich sensory feedback is provided,calculation of the proper manual contact is still a nontrivial problem.All authors are from Department of Adaptive Machine Systems,Graduate School of Enginnering,Osaka University,located at2-1Yamadaoka, Suita,Osaka,565-0871,JAPAN.Shinya Takamuku and Koh Hosoda are also members of the JST ERATO Asada Project {shinya.takamuku,hosoda}@ams.eng.osaka-u.ac.jp 1The term contact condition refers to the posture of the hand and itspositional relation with the object in touch.Recently,the design of hand structure have shown to be helpful for this issue.As Pfeifer et al.[9]states,careful design of hand morphology enables passive movements of the digits leading to ideal contact with the objects through interaction.Several studies have shown the suitability of anthropomorphic design for obtaining such adaptive grasps2. Underactuation,actuation of the hand with number of actu-ators smaller than the degrees-of-freedom,is now a popular design to benefit from their passive adaptation to object shape [10][11][1].Compliant joints[12][13]and softfinger tips [8]are also found to improve the adaptability and stability of the grasps respectively.However,such adaptive design are only applied to thefingers in existing works on tactual recognition[10][14].Consequently,they are in essence a gripper instead of a hand,and their adaptations to objects are limited to two dimension space.Furthermore,previous works lack comparative experiments to show the effect of adaptive design for robust haptic recognition.In order to investigate the role of hand adaptability for realistic haptic recognition,these limitations need to be overcome.This paper introduces the design of the Bionic Hand;an anthropomorphic hand with adaptive design introduced to the whole hand and fully covered with sensitive,deformable skin.The hand is developed by covering the Yokoi hand [15]with a soft anthropomorphic skin developed by Tada et al[16],and actuated with antagonistic pneumatic actuators. The Yokoi hand[15]in its own equip high adaptability by underactuatedfingers coming together when bent.We further improve the adaptability by covering the hand with deformable skins having anthropomorphic arch structure known to be essential for stable grasps[17],and actuating it with antagonistic pneumatic actuators to obtain large range of compliance in the joints.Experimental results are given showing that anthropomorphic design of hand structure enables robust haptic recognition by convergence of object contact conditions into stable representative states through repetitive grasping.Since products found in our environments are designed for the human hand,it is likely that proper grasps on the objects are found as stable states.The repetitive grasps on the object is found to help contact condition convergence into optimal states avoiding suboptimal ones. The paper is organized as follows.First,hand design is described.Then,experimental results showing the ability of adaptive grasp and robust haptic recognition follows.Finally, discussions,conclusions and future works are given.2The term adaptive refers to the ability of adjusting the grasping posture to obtain proper manual contact with the objects.II.T HE D ESIGN OF B IONIC H ANDThe Bionic Hand,shown in Fig.1,has an endo-skeletal structure similar to that of UB Hand[18].The hand obtains cutaneous sense from a modified version of the artificial sen-sitive skin introduced in[16].Anthropomorphic structure and antagonistic pneumatic actuation are introduced to enable adaptive grasps.Fig.1.Photograph of Bionic Hand.The hand is covered with sensitive skins and actuated with antagonistic pneumatic actuators to make the hand strong and compliant.A.Musculoskeletal StructureThe prosthetic hand developed by Yokoi et al.[15]is utilized as the skeletal structure of the hand.The overview is shown in Fig.2.The hand has16degrees of freedom(DOF) with the DIP(distal interphalangeal)and PIP(proximal interphalangeal)joints actuated with the same actuators.The underactuated digits,also found in the human hand,adapt to object shape by bending the joints from the proximal until the phalanges hit the object.The digits are aligned with the corpus plate so that thefingertips will come together when the hand is closed.This design,also inspired from human morphology[17],enables the digits to move the objects into stable condition on the palm.The hand is driven by22 air cylinders attached antagonistically.Although pneumatic actuators have drawback that the response speed is slow,it also has the advantage of high power-to-weight ratio and large range of compliance at the joints.Joint compliance can be obtained by lowering the pressure of the antagonistic actuators.B.Skin StructureThe anthropomorphic skin previously developed by our group is composed of multiple layers with sensing devices detecting strain and its velocity[16]as in the case of human skins[19].The anthropomorphic skin shows high sensitivity such as detecting textures[16]and slippage[4]. Our challenge for developing the hand was to improve our anthropomorphic skin so that it could be extended tothe Fig.2.Skeletal structure[15].The distal interphalangeal(DIP)joint and the proximal interphalangeal(PIP)joints are underactuated;acted with the same set of actuators.The carpal plate mounting thefingers together is bent between the digits to have thefingers come together when the hand is closed.TABLE IN UMBER OF SENSING DEVICES IN THE SKIN.finger/palm part#of PVDF#of strain gauge finger distal21proximal11middle/metacarpal11 palm66whole hand.In order to obtain ideal deformation properties for stable grasp and manipulation,we chose the skin structure shown in Fig.3.The construction process is as follows. First you attach a glove on the musculoskeletal structure described in the previous section to keep the skin and the bones apart.Then,finger sacks and palm sheets composed of relatively stiff polyurethane materials with strain gauges and PVDF(polyvinylidenefluoride)films inside are attached. PVDFfilms are capable of detecting the velocities of strain to obtain high sensitivity.A photograph of the hand at this development step is shown in Fig.4.Wiring for the sensor devices runs through the center of thefingers so that there are not so much stretches nor compressions.Finally, the hand is covered with another glove and relatively soft polyurethane material is poured in between the two gloves. Lines of the skin on the joints and on the palm,shown in Fig.1,are produced by binding the gloves with the bones during the last process.The last process also produces an artificial thenar eminence which forms arch structure on the palm considered to be essential for adaptive grasping[17]. Numbers of sensing devices in the skin is described in Table I.Sensors in the palm are distributed equally,and multiple sensors are mounted in different depth in thefingers.The multi-layer structure of polyurethane material with different stiffness provides high sensing abilities[16],and improves stability of manipulation;i.e.,fingers would be too soft to grasp objects properly without thefinger sacks.Fig.3.Skin structure.First,the skeletal structure is covered with a glove.Then,finger sacks and palm sheets with strain gauges and PVDF films mounted in relatively stiff polyurethane material are attached.Finally, another glove is put on the hand and soft polyurethane material is poured inbetween.Fig.4.Structuring of the inside skin.The photograph shows the inside structure of the skin.The wires for the sensing devices are winded down to avoid breaking by tension.C.Control SystemDigital outputs from the computer through DIO cards control on/off valves to supply/stop/exhaust air into/from the air cylinders by250Hz.The pressured air from a compressor is controlled into stable pressure with a regulator to be transmitted to the actuators through the valves.Sensor signals from the strain gauges,PVDFfilms,and pressure sensors are amplified and fed to the host computer via ADC cards at a rate of250Hz.The computer runs RTAI realtime Linux.III.A DAPTIVE G RASPFirst we conducted a preliminary experiment observing the ability of grasping various objects.We gave objects with various shape to the Bionic Hand controlled with constant grasping actuation.Fig.5shows the hand producing stable grasping posture through interaction even though there are no changes in the actuation.Rough control is enough to have proper grasps for various objects,and the skin is at least not disturbing thegrasps.(a)ball(b)book(c)bottle(d)prismFig.5.Adaptive grasping of various objects.Spherical,cylindrical,and flat grasping postures are generated through interaction with the objects even though the actuation is the same for all the objects.IV.H APTIC O BJECT R ECOGNITIONIn order to show that adaptation by hand morphology enables robust haptic recognition,we investigate the transi-tion of haptic sensory values during repetitive grasping.We expect that the contact condition for the same object will converge to few stable conditions after the repetitive grasps and the converging condition will differ when the objects are different,thus easing the recognition.A.SetupThe repetitive grasp motion is shown in Fig.6.First comes a large grasp ending after9s(a-c).Then,grasps of4.4s cycle with relatively smaller opening follows(d,e).The hand is placed so that the palm faces the gravity to avoid the object falling down.The hand was given the objects shown in Fig.7 varying the initial contact condition.The sizes of the objects are described in Table II.The length of thefinger from the MP joint is approx.80mm.The objects were grasped withfive different initial contact conditions each.The initial contact condition of the prism,cylinder,and bottle was varied by rotating the object on the palm plane within the range of approx.45degrees.The ball was put on different positions.TABLE IIS IZE OF THE OBJECTSdiameter/length of each side(mm)height(mm) prism38151cylinder50140bottle42.5(upper part)/55.5(bottom part)124.5ball7070Fig.6.Motion of repetitive grasps .Fig.7.Photograph of the objects used in the experiment.B.Transitions of cutaneous sensory valuesThe contact condition converged into discriminative con-ditions through repetitive grasps.An example of which is shown in Fig.8.Even though the initial contact condition differs between the two trials,they both converge into similar ones.The sensory outputs from the strain gauges and the PVDFfilms for the two trials are shown in Fig.9and Fig.10respectively.The sensory values are also converging into a common attractor through the grasping.Although the outputs of the PVDFfilms are found to detect the contact and release of the objects,it did not include so much data for this particular experiment and will not be used in the present work.C.Analysis of VarianceIn order to investigate if the cutaneous signals during the grasps are efficient for object recognition,analysis of variances for the strain gauge outputs during each grasp is given.Fig.11shows the transitions of mean variance within the trials of the same object(within class variance), the variance between the mean values of different objects (between class variance),and the ratio of the two(variance ratio).Note that only the strain gauge output at the grasping posture is used and the variance within the class is due to variations in initial contact conditions.The distancemeasure(a)case1(b)case2Fig.8.Adaptation through repetitive grasping by hand morphology. The leftfigure shows the contact condition in thefirst grasp,while the right figure shows the contact condition in the10th grasp.In the leftfigure,the blue dotted allow shows the initial angle,whereas the blue solid allow shows the converged angle.Even though the two cases differ in initial conditions, they both converge to common contact condition through repetitive grasps. is Euclidean.Thefigure shows that as the hand repeat the grasp on the object,within class variance decreases and the variance ratio rises.This shows that the cutaneous signals are converging into representative values for the objects to become features appropriate for object recognition.D.Analysis with a self-organizing mapA self-organizing map(SOM)is an artificial neural net-work that is trained using unsupervised learning to produce a low-dimensional,discretized representation of the input space of the training samples.Since the output of the net-work,called the map,preserves the topological properties of the input space,it is used for the visualization of the distances of the cutaneous signals during grasps within/between the objects.We used the software package SOM-pak version3.1[20],where the size of the map was32×32,topology wasa hexagonal lattice and neighboring function type used was bubble.Fig.12shows the clustering of the self-organizing map with strain gauge values from thefirst and10th grasps respectively.While data plots for the same object are mixed in the clustering with thefirst grasp signals,the objects are separated in the clustering with the10th grasp.(a)case1(b)case2Fig.9.Strain gauge values during repetitive grasping .Outputs from the strain gauges are converging into a common attractor.V.D ISCUSSION ,C ONCLUSION AND F UTURE W ORK The analysis of variances and visualization with the self-organizing map both show that the cutaneous sensory values obtained during grasping converges into values representative of each object.Since the actuation for the repetitive grasps is constant during the experiment,the convergence should be due to the morphological adaptability of the hand.Thus,the idea of robust haptic recognition by virtue of adaptability of anthropomorphic morphology of the hand is supported.Reduction of sensory space is essential for object recognition [21][22].Flies [23]and human infants [24]are found to actively reduce the space by moving themselves or the objects to constant relative position.The result of the present paper shows that the structure found in the human hand solves the same issue of narrowing down the sensor space for object recognition by morphological computation.Note that once the relative position of the object is limited,any modality can be utilized for recognition.The effect is not only for tactilerecognition.(a)case1(b)case2Fig.10.PVDF film values during repetitive grasping .The PVDF film is detecting the contact and releasing of the objects.Several questions remain to be investigated as future work.We combined several ideas for improving the adaptability of the hand.However,the role of each idea is not clear;i.e.,what role does each design have in the overall adaptability of the hand?Only the role of repetitive grasping to help the convergence was investigated in the current paper.Another question is the conditions that should be fulfilled to obtain proper grasps with our method.Clearly,the hand should have roughly correct hand shapes for obtaining proper grasps.The shaping of the hand before grasping,often refered to as preshaping,should be considered.Finally,the issue of affor-dance remains.Since the hand has humanlike morphology and most products found in our environment are designed for the human hand,it is likely that the proper grasps for those products can be found as stable states by our method.Once such grasp is be obtained,stable,reproducible sensory feedback could be obtained to lead tool use.The possibility would be explored in the near future.2 4 6 8 10 12 500010000v a r i a n c etime [sec]within-class variance between-class variancevariance ratioFig.11.Variance analysis .The within class variance reduces and the variance ratio rises through the repetitive grasps.R EFERENCES[1]M.C.Carrozza,G.Cappiello,S.Micera,B.B.Edin,L.Beccai,andC.Cipriani.Design of a cybernetic hand for perception and action.Biological Cybernetics ,95,2006.[2]Shadow Robot Company.Design of a dextrous hand for advancedclawar applications.CLAWER Conference ,2003.[3]Goran Lundborg and Birgitta Rosen.Sensory substitution in prosthet-ics.Hand clinics ,17(3):481–488,2001.[4]Yasunori Tada and Koh Hosoda.Acquisition of multi-modal ex-pression of slip through pick-up experiences.Advanced Robotics ,21(5):601–617,2007.[5]Yasuo Kuniyoshi,Ryo Fukano,Takuya Otani,Takumi Kobayashi,andNobuyuki Otsu.Haptic detection of object affordances by a multi-fingered robot hand.International Journal of Humanoid Robotics ,2(4):415–435,2005.[6]Shinya Takamuku,Gabriel Gomez,Koh Hosoda,and Rolf Pfeifer.Haptic discrimination of material properties by a robotic hand.Proc.of International Conference on Development and Learning ,76,2007.[7]David Siegel,Inaki Garabieta,and John M.Hollerbach.An integratedtactile and thermal sensor.Proceedings of the International Conference on Robotics and Automation ,3:1286–1291,1986.[8]K.B.Shimoga and A.A.Goldenberg.Soft robotic fingertips.TheInternational Journal of Robotic Research ,15(4),1996.[9]Rolf Pfeifer,Fumiya Iida,and Gabriel Gomez.Morphological com-putation for adaptive behavior and cognition.International Congress Series ,1291:22–29,2006.[10]Lorenzo Natale,Giorgio Metta,and Giulio Sandini.Learning hapticrepresentation of objects.Proc.of International Conference on Intelligent Manipulation and Grasping ,2004.[11]Shigeo Hirose and Yoji Umetani.The development of soft gripper forthe versatile robot hand.Biological Cybernetics ,95,2006.[12]Aaron M.Dollar and Robert D.Howe.Towards grasping in unstruc-tured environments:grasper compliance and configuration optimiza-tion.Advanced Robotics ,19(5),2005.[13]Aaron M.Dollar and Robert D.Howe.A robust compliant graspervia shape deposition manufacturing.IEEE/ASME Transactions on Mechatronics ,11(2),2006.[14]Kenshi Watanabe,Sumiaki Ichikawa,and Fumio Hara.A multi-linkrobotic finger-type soft touch-sensor system.Proceedings of IEEE Sensors ,3:1530–1533,2004.[15]Hiroshi Yokoi,A Arieta,Ryu Katoh,W.Yu,I.Watanabe,andM.Maruishi.Mutual adaptation in a prosthetics application in embodiedartificial intelligence.Lecture Notes in Computer Science ,3139:146–159,2004.[16]Koh Hosoda,Yasunori Tada,and Minoru Asada.Anthropomorphicrobotic soft fingertip with randomly distributed receptors.Robotics and AUtonomous Systems ,54(2):104–109,2006.[17]Adalbert I.Kapandji.The Physiology of the Joints,5th edition,UpperLimb .Churchill Livingstone,1982.(a)firstgrasp(b)10th graspFig.12.Som clustering .Clustering of the self-organizing map is given where units most active for each object trial data is plotted.The graph shows the distance of sensory data between/within each object.Points with line crossings represent prisms,squares represent cylinders,circles represent balls and triangles represent bottles.While data plots for the same object are mixed in the clustering with the first grasp signals,the objects are separated in the clustering with the 10th grasp.[18]Luigi Biagiotti,Fabrizio Lotti,Gianluca Palli,Paolo Tiezzi,GabrieleVassura,and Claudio Melchiorri.Development of ub hand 3:Early results.Proceedings in IEEE International Conference on Robotics and Automation ,pages 4488–4493,2005.[19]ler,H.J.Ralson,and M.Kasahara.The pattern of cutaneousinnervation of the human hand.American Journal of Anatomy ,102:183–197,1958.[20]T.Kohonen,J.Hynninen,J.Kangas,and aksonen.Som pak:The self-organizing map program package.Helsinki University of Technology Technical Report ,A31,1996.[21]Randall Beer.Toward the evolution of dynamical neural networksfor minimally cognitive behavior.From animals to animats:Proc.of International Conference on Simulation of Adaptive Behavior ,1996.[22]Rolf Pfeifer and Christian Scheier.Understanding Intelligence ,chap-ter 12.MIT Press,Bradford Books,1999.[23]Marcus Dill,Reinhard Wolf,and Martin Heisenberg.Visual patternrecognition in drosophila involves retinotopic maching.Nature ,365:751–753,1993.[24]Emily bushnell and Paul Boudreau.Motor development and the mind:The potential role of motor abilities as determinant of aspects of perceptual development.Child Development ,64(4):1005–1021,1993.。
关于求教实用的肢体语言的英语作文
关于求教实用的肢体语言的英语作文The Essentials of Effective Body Language.Body language, often referred to as nonverbal communication, plays a crucial role in everyday interactions. It can convey messages, emotions, and intentions that words alone cannot express. Mastering the basics of effective body language can significantly enhance communication skills, leading to more successful outcomesin various situations.Eye Contact.Eye contact is one of the most essential elements of body language. It establishes a connection between individuals, expressing interest, attention, and sincerity. When making eye contact, it's important to maintain a steady gaze without being too intense or avoiding eye contact altogether. Proper eye contact can foster trust and rapport, while avoiding it can communicate lack of interestor dishonesty.Facial Expressions.Facial expressions are another powerful form of nonverbal communication. They can instantly convey a wide range of emotions, from happiness and sadness to anger and surprise. Smiles, for instance, are universally recognized as signs of friendliness and warmth. On the other hand, furrowed brows or tight lips may communicate displeasure or displeasure. Understanding and using facial expressions appropriately can greatly enhance the clarity and effectiveness of communication.Posture.Posture refers to the way one holds oneself while sitting, standing, or moving. It can communicate confidence, openness, or defensiveness. An upright posture with shoulders back and head held high conveys confidence and openness. By contrast, slumped shoulders or a hunched posture may communicate lack of confidence or discomfort.Maintaining a positive posture not only improves one's appearance but also affects how others perceive and respond to them.Hand Gestures.Hand gestures are another crucial aspect of body language. They can enhance speech by adding emphasis or clarity, or they can communicate entirely separate messages. For instance, waving one's hand in the air may express excitement or impatience, while folding one's hands behind their back may communicate confidence or authority. It's important to use hand gestures naturally and appropriatelyto avoid miscommunication or appearing awkward.Personal Space.Personal space refers to the area around an individual that they consider theirs. Different cultures andindividuals have different comfort levels with regards to personal space, and respecting these boundaries is crucial for effective communication. Intruding on someone'spersonal space can communicate disrespect or insensitivity, while maintaining a respectful distance can foster trust and comfort.The Importance of Cultural Awareness.It's important to note that body language can vary significantly across cultures. For example, what may be considered appropriate and respectful body language in one culture may be considered disrespectful or inappropriate in another. Therefore, it's crucial to be culturally aware and sensitive when communicating with people from different backgrounds. Understanding and adapting to these differences can significantly improve cross-cultural communication.In conclusion, effective body language is an essential skill for successful communication. By mastering the basics of eye contact, facial expressions, posture, hand gestures, and personal space, individuals can significantly enhance their communication skills and build stronger relationships with others. Additionally, being culturally aware andsensitive to differences in body language across cultures is crucial for effective cross-cultural communication. By continuously practicing and observing body language, one can become a more skilled and effective communicator.。
姿态识别方法及装置与流程
姿态识别方法及装置与流程Posture recognition method and device play a significant role in various fields, such as healthcare, sports, and security. By accurately identifying individuals' body postures, these methods and devices help monitor health conditions, improve athletic performance, and enhance security measures. In the field of healthcare, posture recognition assists in assessing patients' physical condition and tracking their progress during rehabilitation. Additionally, in sports training, these methods can provide real-time feedback to athletes, helping them correct their postures and prevent injuries. Moreover, in security systems, posture recognition can be used to identify suspicious behavior and enhance surveillance measures.姿态识别方法和装置在健康、体育和安全等多个领域发挥着重要作用。
通过准确识别个体的身体姿势，这些方法和装置有助于监测健康状况、提高运动员的表现，并增强安全措施。
中国学校常用的肢体建议英语作文
中国学校常用的肢体建议英语作文In Chinese schools, body language plays an important role in communication between teachers and students. It is a way for both parties to express themselves and understand each other better. Here are some common body language tips used in Chinese schools:1. Eye contact: In Chinese culture, maintaining eye contact isa sign of respect and attentiveness. When talking to teachers or classmates, students are encouraged to make eye contact to show that they are listening and engaged in the conversation. Avoiding eye contact can be seen as disrespectful or disinterested.2. Hand gestures: Hand gestures are commonly used in Chinese schools to emphasize a point or convey emotions. Students often use hand gestures to ask questions, express opinions, or share ideas during class discussions. However, it is important to use gestures appropriately and avoid making overly exaggerated movements.3. Posture: Sitting up straight and maintaining good posture is also important in Chinese schools. Slouching or leaning back in your chair is considered disrespectful and shows a lack ofinterest in the lesson. Teachers often encourage students to sit up straight to show that they are attentive and focused.4. Personal space: In Chinese culture, personal space is important and should be respected. When interacting with teachers or classmates, it is important to maintain an appropriate distance and avoid invading someone's personal space. Being too close can make the other person feel uncomfortable or intimidated.5. Facial expressions: Facial expressions are another important aspect of body language in Chinese schools. Smiling, nodding, or frowning can convey different emotions and help to communicate thoughts or feelings. It is important for students to be aware of their facial expressions and use them appropriately in different situations.6. Bowing: In some Chinese schools, students may also show respect to their teachers by bowing slightly when greeting them or expressing gratitude. Bowing is a traditional gesture of respect in Chinese culture and is often used to show humility and politeness.Overall, body language plays a significant role in communication in Chinese schools. By being aware of and using appropriate body language, students can improve theircommunication skills, show respect to teachers and classmates, and create a positive learning environment.。
Gesture Recognition System
Hand Gesture Recognition SystemMohamed Alsheakhali, Ahmed Skaik, Mohammed Aldahdouh, Mahmoud AlhelouComputer Engineering Department, The Islamic University of GazaGaza Strip , Palestine, 2011msali@.ps, ahmskaik@, mhmd1986@,mmh.1989@Abstract —The task of gesture recognition is highly challenging due to complex background, presence of nongesture hand motions, and different illumination environments. In this paper, a new technique is proposed which begins by detecting the hand and determining its canter, tracking the hands trajectory and analyzing thevariations in the hand locations, and finally recognizing thegesture. The proposed technique overcomes background complexity and distance from camera which is up to 2.5 meters. Experimental results show that the proposed technique can recognize 12 gestures with rate above 94%.Keywords — Hand gesture recognition, skin detection, Hand tracking.I. I NTRODUCTIONHuman gestures constitute a space of motion expressed by the body, face, and/or hands. Among a variety of gestures, hand gesture is the most expressive and the most frequently used. Gestures have been used as an alternative form to communicate with computers in an easy way. This kind of human-machine interfaces would allow a user to control a wide variety of devices through hand gestures. Most work in this research field tries to elude the problem by using markers, marked gloves or requiring a simple background [1-6]. Glove-based gesture interfaces require the user to wear a cumbersome device, and generally carry a load of cables that connect the device to a computer. A real-time gesture recognition system which can recognize 46 ASL letter spelling alphabet and digits was proposed [7]. The gestures that are recognized by [7] are static gestures without any motion.This paper introduces a hand gesture recognition system to recognize ‘dynamic gestures’ of which a single gesture is performed in complex background. Unlike previous gesture recognition systems, our system neither uses instrumented glove nor any markers. The new barehanded proposed technique uses only 2D video input. This technique involves detecting the hand location, tracking the trajectory of the moving hand, and analysing the hand-position variations. Then the obtained motion information is been used in the recognition phase of the gesture.The present paper is organized as follows: Section I introduces an overview of system components. Hand gesture detection and tracking the trajectory of the moving hand approach are presented in Sections II and III. SectionIV demonstrates the proposed recognition method. The results of the proposed system are discussed in Section V. Conclusion and future work are given in Section VI. II. SYSTEM COMPONENTS A low cost computer vision system that can be executed in a common PC equipped with USB web cam is one of the main objectives of our approach. The system should be able to work under different degrees of scene background complexity and illumination conditions. Fig 1shows an overview of our hand gesture detection, tracking and recognition framework.The proposed technique depends on the following approach:1) Acquisition: a frame from the webcam is captured.2) Segmentation and detection: the image is segmented into two parts, both of them are manipulated simultaneously before analysing the resultant data, skin pixels and moving patterns are detected. A new image is created containing the location of the center of the moving hand.3) Tracking: 10 latest consecutive frames are tracked continuously, in each frame the centers of the moving hands are detected.4) Pattern Recognition: through the user's hands motion, the features are compared with those stored in the database, the maximum likelihood correspondence is chosen. III. H AND DETECTIONThe technique is built around the idea that Splits the input video screen into two parts and processes each part separately, so that the processing in the two parts is similar and simultaneous. The operators used for image processing must be kept low time consuming in order to obtain the fast processing rate needed to achieve real time speed. The detection steps are:Hand Center Detect Center Region Tracking Hand Gesture Recognition Fig. 1. Proposed System Framework.1) Skin tone detection: Skin can be ea the color tone information. RGB-ba classified as skin if:0.08 3R G B 1.51R G B R G B2) Motion detection: The used skin form wide range of colors. Therefore, m appear other than the skin tone regio non-skin regions will be exclude moving objects only. The moving detected by subtracting two consecut thresholding the output.3) Combination of motion and skin gestures information consist of mo pixels, therefore the logic ‘AND’ is a them: , , ^ Where , and , indicate th tone pixels. Figure 2 illustrate the idea. (a)(b) Fig. 2. The hand detection: (a) Original imagregion, (c) combination of motion and skin p4) Region identification: The center of t determined as follows:i. Midpoint of each moveme row will be calculated as sh stored in a collection (e.g. row's number is stored in address. Midpoint manipul be illustrated in Figure 3. Light and dark grey and pink pixels tone and movement pixels (hit pixels).Rows that contain more than 15 skin pixels, the pixels in it are colored da pixels).The appendant left table consists of t column shows number of pixels satisfy motion in each row, and left column sh each rows that contains taken hit pixels.The pink and red pixels refer to th which these pixels are found.asily detected using ased color is 0.12.8 B 1.4mula may include amany regions will ns. However, these ed by considering g objects can betive frames [8] andcolor: The hand ovement and skin applied to combine , he moving and skin(c) ge, (b) skin colorpixels.the desired hand isent and skin color hown in Eq. 1, and Array), so that the n this array as an lation process will are all satisfy skinn tone and movingark grey (taken hittwo columns. Rightying skin tone andhows the center ofe center of row inii. S u mming prestored m dividing the result stored in the evenhanded lines, aAlgorithm I shows the details algorithm.A LGORITHM I. Hand center d for i ←0 to imageHeightfor j ←0 to imageWidthif Px(i,j) satisfiy Moti AND inSkinRange t TruePixel ← Tru XTruePx ← XTrue end ifend for if TruePixel > 15 then MPArray[i] ← ( XTru TruePixel ) else MPArray[i] ← 0 end if end for Fig.3 Hand cen midpoints values and thenby number of rows that are array which gives the as shown in Eq. 2 and Eq. 3. ∑ ∑s of hand center detectiondetectionion then uePixel + 1 ePx + j uePx / nter determination Eq(1) Eq(2) Eq(3)for i←0 to ImageHeightif MPArray[i] 0 thenMPSum ← MPSum + MPArTrueRow ←TrueRow + 1YTrueRow ←YTrueRow + iend ifend forif TrueRow > 0 AND YTrueRow >AND MPSum > 0 thenX_Center ← MPSum / TrueRowY_Center ← YTrueRow / TrueRoend ifFigure 4 shows the results of handusing different backgrounds.IV.H AND GESTURE TRAJECTORY TThe overall system for hand regionstages: the first stage focuses on the mand stores the latest 10 transitions of thepoints on queue as shown in Figure 5, wstage is focused on analysing and proceas follows:Select from centroidin and repeat this work forneglecting the value of the point atshown in Table 1.Fig. 4. Hand region detection ima(a)(b)Fig. 5 (a),(b) illustrate the latest manipulatedrray[i]> 0wowposition detectionTRACKINGn tracking has twomotion informatione predefined centralwhereas the secondessing these points, and store itas with0,0 as(a)(bBeing impossible for a persgestures in a second [9], it is fframes in a second; a traimplemented in order to avoideach frame. Anyway, the usewould ensure a real time procesof the proposed approach in realTo measure the distance anconsecutive center in the previoPythagoras's formulas as shownFig. 6 (a),(b) Equations used tobetween each two consecutive poinconsecutive points withThen we specify four variablmajor directionsIn order to avoid erroneous removement more accurate we attas shown in Fig. 7.Table 1,(a) The original array0,0(a) , ∆(b)(c)ages.d points.))son to make more than 25feasible to consider only 10cking system should bed the complete analysis ofe of the system currentlyssing, which allows the usel time video applications.nd the angle between eachous midpoints array, we usen in Figure 6.find the angle and distancent, (c) variable position of twoh different angles.les associated with the foursults and make the desiredtach every angle with 15°(b) the processed array with∆yAfter that, we ignore the movements angles latitude and those that did not r specified ranges, then counting the d predefined variables that belongs to the s For the last step in hand tracking p create two variables for ea follows, to evaluate num related to the given angle, distances that that travelled by the hand a If the angle between each pres midpoints belongs to one of the pred direction extents, then increment given angle and add the distance consecutive midpoints to Thus, we have obtained sufficient every movement and in which direction and varia clarify the hand tracking method.A LGORITHM II. Hand movement tracki for i ← 0 to array.Lengthif -15 < θ <15 then rtl ← rtl + 1 Sum_rtl ← Sum_rtl + d else if 75 < θ <105 then dtu ← dtu + 1 Sum_dtu ← Sum_dtu + d else if 165 < θ <195 then ltr ← ltr + 1 Sum_ltr ← Sum_ltr + d else if 255 < θ < 285 then utd ← utd + 1 Sum_utd ← Sum_utd + d end if end forFig. 7. Four major angles with1A LGORITHM IV. Both Hand if Sum_utd > 200 AND S AND utd > 3 AND utd * DoEvent(Both hand u else if Sum_dtu > 200 AND Sum_dtu * > 200 A AND dtu* > 3 then DoEvent(Both hand D else if Sum_ ltr > 150AND Sum_rtl * > 150 AN AND rtl* > 3 then DoEvent(Zoom In ) else if Sum_ rtl > 150AND Sum_ltr * > 150 AN AND ltr* > 3 then DoEvent(zoom Out )that exceeded the relate to any of thedistances for eachsame extent. phase, we need toach angle asmber of movements to sum up the at a given angle.stored consecutivedefined four basicfor the between the two . information about through the related ables. Algorithm II ingV. H AND GESTURESince the considered gestures we need a mechanism to reco axial movement. The recogniti and should accommodate varia gesture. At this stage, we divide th modules. (a) One hand moveme analysis. The angle and distanc the counters of the movements to recognize the gestures as sh algorithms. Algorithm III shows one ha method:A LGORITHM III . One H if Sum_rtl > 150 AND r AND ltr* < 2 thenDoEvent(Right To Lelse if Sum_ltr > 150 AN AND rtl* < 2 then DoEvent(Left to righelse if Sum_utd > 200 A AND utd* < 2 then DoEvent(Up to down else if Sum_dtu > 200 A AND dtu* < 2 then DoEvent(down to upAlgorithm IV. shows bot recognition method:15° d recognition Sum_utd * > 200 > 3 then up ) AND dtu > 3 Down ) ND ltr > 3ND rtl > 3 E RECOGNITION s are processed dynamically, ognize gestures using their ion scheme must be robust ations in the attributes of a he analysis phase into two ent analysis, (b) Both hands ce of the hand centroid, and in each direction were used hown in the following two and movements recognition Hand recognitionrtl > 4Left ) ND ltr > 4 ht ) AND utd > 4 n ) AND dtu > 4 p ) th hands movementsVI. E XPERIMENTAL RESULTSThe proposed method was implemented using an optimized C# code and without any auxiliary library. The experimental results are illustrated in Table 2.We have tested 12 different hand gestures. Each gesture is tested 40, 80 and 100 times with three different persons. There are 12 different gestures, and 2640 image sequences used. The recognition rate of this system is 94.21%.VII. C ONCLUSIONSA new technique has been proposed to increase the adaptability of a gesture recognition system. We have implemented a real-time version, using an ordinary workstation with no special hardware beyond a video camera input. The technique works well under different degrees of scene background complexity and illumination conditions with more than 94% success rate.R EFERENCES[1] T. Baudel, M. Baudouin-Lafon, Charade: remotecontrol of objects using free-hand gestures, Comm. ACM 36 (7) (1993) 28–35.[2] D.J. Sturman, D. Zeltzer, A survey of glove-basedinput, IEEE Computer Graphics and Applications 14 (1994) 30–39.[3] J. Davis, M. Shah, Visual gesture recognition. IEEProc. Vis. Image Signal Process ,141(2) :101-106, 1994.[4] A. Bobick, A. Wilson, A state-based technique for thesummarization and recognition of gesture. In Proc.IEEE Fifth Int. Conf. on Computer Vision, Cambridge, pp. 382-388, 1995.[5] E. Hunter, J. Schlenzig, and R. Jain.Posture Estimationin Reduced- Model Gesture Input Systems. Proc. Int´l Workshop Automatic Face and Gesture Recognition, pp. 296-301, 1995.[6] C. Maggioni. Gesturecomputer. New Ways ofOperating a Computer, Proc. Int´l Workshop Automatic Face and Gesture Recognition, 1995.[7] R. Lockton, A.W. Fitzgibbon, Real-time gesturerecognition using deterministic boosting, Proceedings of British Machine Vision Conference (2002). [8] R. Gonzales, and E. Woods, “Digital ImageProcessing,” Prentice Hall, Inc, New Jersey, 2002. [9] E. Sanchez-Nielsen, L. Anton-Canalis, M. Hernandez-Tejera, Hand gesture recognition for human machine interaction, Journal of WSCG, Vol.12, No.1-3, 2003.Table 2. Experimental results of the gesturesrecognition system* dtu = down to up; utd = up to down; rtl = right to left; ltr = left to right.。
肢体语言求职信英语作文
Dear Hiring Manager,I am writing to express my interest in the [position/role] at your esteemed organization. I believe that my skills, experience, and passion for this field make me a strong candidate for the job. However, I would like to take a unique approach in showcasing my qualifications by using body language in this cover letter.As I write this letter, I am sitting up straight, maintaining good posture, and making eye contact with you through the screen. These non-verbal cues convey my confidence, sincerity, and respect for your time and consideration. I am also using hand gestures to emphasize key points and demonstrate my enthusiasm for the opportunity.In terms of my professional experience, my body language reflects a strong background in [relevant industry/role]. My hands are open and palms facing up, indicating transparency and honesty. My gestures are purposeful and controlled, showcasing my ability to communicate effectively and efficiently. I am also using a calm and composed demeanor to demonstrate my stability and reliability.As I discuss my skills and qualifications, my body language reflects a confident and capable individual. My shoulders are relaxed and my hands are moving smoothly to illustrate my adaptability, creativity, and problem-solving abilities. I am also maintaining a positive and engaged expression, highlighting my enthusiasm and dedication to the role.In addition to my professional qualifications, my body language also showcases my interpersonal and teamwork skills. My gestures areinclusive and inviting, indicating my ability to collaborate effectively with colleagues and clients. I am maintaining a friendly and approachable demeanor, demonstrating my empathy and ability to build strong relationships.Throughout this cover letter, my body language consistently conveys professionalism, confidence, and a genuine interest in the position. I am dressed in business attire, sitting up straight, and maintaining good eye contact, reflecting my commitment to making a positive impression.In conclusion, I believe that my body language in this cover letter effectively showcases my qualifications, skills, and passion for the [position/role]. I am confident that my non-verbal cues communicate my confidence, sincerity, and dedication to excellence. I would be honored to have the opportunity to bring my unique blend of skills and enthusiasm to your team.Thank you for considering my application. I look forward to the possibility of discussing my qualifications further with you.Sincerely,[Your Name]。
中美肢体语言介绍信英语作文
中美肢体语言介绍信英语作文Introduction Letter on Chinese and American Body LanguageDear readers,In the fascinating realm of non-verbal communication, body language reigns supreme as a universal, albeit nuanced, form of expression.The purpose of this letter is to provide an intriguing glimpse into the distinct yet subtly interconnected world of Chinese and American body language.亲爱的读者们，在非言语交际的迷人领域中，肢体语言作为一种普遍却微妙的表达形式，占据了至高无上的地位。
这封信的目的是带您领略中美两国肢体语言的独特性以及它们之间微妙的联系。
When Americans shake hands, for instance, a firm grip and direct eye contact are often seen as signs of confidence and respect.Conversely, Chinese individuals may prefer a lighter handshake and minimal eye contact, reflecting a more reserved and humble demeanor.例如，在美国，当人们握手时，坚定的握力和直视的眼神通常被视为自信和尊重的标志。
相反，中国人在握手时可能更喜欢轻轻握住，并且避免过多的眼神交流，这反映出更为保守和谦逊的态度。
Cross-cultural variations extend to gestures as well.While thumbs-up is widely recognized as a positive sign in the States, it can be consideredoffensive in certain Middle Eastern countries.Similarly, the "OK" hand signal is commonly used in America, yet it bears a vulgar connotation in Greece.跨文化差异也延伸到手势上。
写肢体语言的英语作文
写肢体语言的英语作文Body language is a powerful form of non-verbal communication that conveys messages without the use of words. It includes gestures, facial expressions, posture, and eye contact, all of which can significantly impact how we are perceived by others.Gestures are a universal language that can be understood across cultures, although their meanings can vary. For example, a thumbs up in many Western cultures signifies approval, while in some Middle Eastern countries it can be seen as offensive. A simple wave of the hand can be afriendly greeting or a signal of farewell.Facial expressions are another critical component of body language. A smile can convey happiness and warmth, while a frown can indicate displeasure or concern. The eyes are often referred to as the "windows to the soul" and can communicate a range of emotions from joy to sadness, from interest to disinterest.Posture, too, plays a significant role in body language. Standing tall with shoulders back can project confidence and authority, while slouching can suggest a lack of confidence or low energy. Crossing one's arms can be a sign of defensiveness or closed-off attitude, whereas open arms can indicate openness and approachability.Eye contact is perhaps one of the most powerful forms of non-verbal communication. Maintaining eye contact can show that you are engaged and interested in the conversation, while avoiding eye contact can be interpreted as disinterest or dishonesty.Understanding and being aware of your own body language can help you communicate more effectively and build stronger relationships. It's also important to be mindful of cultural differences in body language to avoid misunderstandings.In conclusion, body language is an essential tool for communication. It can enhance or detract from the message you are trying to convey. By being conscious of your gestures, facial expressions, posture, and eye contact, you can ensure that your non-verbal cues are working in harmony with your verbal messages.。
介绍人脸识别英语作文
介绍人脸识别英语作文Title: The Role of Facial Recognition Technology in Modern Society。
In recent years, facial recognition technology has emerged as a significant advancement in the field of biometric security and identification. This technology utilizes facial features to identify individuals, providing a wide array of applications ranging from security systemsto social media platforms. In this essay, we will explorethe functions, benefits, concerns, and ethical implications surrounding facial recognition technology in modern society.Facial recognition technology operates by capturing and analyzing patterns based on a person's facial features. These features include the distance between the eyes, the shape of the nose, and the contours of the face. Once these unique characteristics are identified, they are compared to a database of known faces to determine a match.One of the primary applications of facial recognition technology is in security systems. Airports, banks, and government agencies utilize this technology to enhance security measures and prevent unauthorized access. For instance, airports employ facial recognition to match passengers with their passport photos, reducing the likelihood of identity fraud and improving the efficiency of security checks.Moreover, facial recognition technology has found its way into everyday devices such as smartphones and laptops. Many smartphones now feature facial recognition as a means of unlocking the device, replacing traditional methods such as PIN codes or fingerprint scanners. This convenience has made facial recognition a popular choice among consumers, leading to its widespread adoption in the tech industry.Another area where facial recognition technology is making an impact is in law enforcement. Police departments use facial recognition to identify suspects captured on surveillance cameras or in photographs. This capability has aided in solving crimes and locating missing persons byquickly matching faces to individuals in criminal databases.Despite its numerous applications and benefits, facial recognition technology also raises significant concerns regarding privacy and civil liberties. Critics argue thatthe widespread use of facial recognition poses a threat to individual privacy rights, as it allows for the constant monitoring and tracking of individuals without their consent. Furthermore, there are concerns about the accuracy and reliability of facial recognition algorithms,particularly when it comes to recognizing individuals with darker skin tones or non-conventional facial features.Ethical considerations surrounding facial recognition technology are also a topic of debate. There are concerns about the potential for discrimination and bias in the algorithms used to train facial recognition systems. If these systems are not properly calibrated, they may inadvertently perpetuate existing societal biases, leadingto unjust outcomes for certain demographic groups.In addition to privacy and ethical concerns, there arealso worries about the potential misuse of facialrecognition technology by authoritarian regimes or oppressive governments. In countries with limited freedoms and human rights abuses, facial recognition technologycould be used as a tool for surveillance and social control, further eroding civil liberties and stifling dissent.In response to these concerns, there have been callsfor increased regulation and oversight of facialrecognition technology. Some jurisdictions have implemented laws to restrict its use in certain contexts, such as inlaw enforcement or public surveillance. Additionally, there are ongoing efforts to develop more transparent and accountable facial recognition algorithms that mitigate biases and protect privacy rights.In conclusion, facial recognition technology has become an integral part of modern society, with applications ranging from security systems to consumer devices. While it offers numerous benefits in terms of security and convenience, it also raises significant concerns regarding privacy, ethics, and civil liberties. As we continue tograpple with the implications of this technology, it is essential to strike a balance between innovation and safeguarding fundamental rights and freedoms. Only through thoughtful regulation and responsible implementation can we ensure that facial recognition technology serves the interests of society as a whole.。
身体体态评估流程
身体体态评估流程As we go through our daily lives, we may not always pay close attention to our body posture and how it affects our overall health and well-being. However, maintaining good posture is crucial for preventing back pain, reducing the risk of injury, and promoting proper alignment of the spine. It is important to regularly assess our body posture to ensure that we are not putting unnecessary strain on our muscles and joints.当我们度过日常生活时，我们可能并不总是密切关注我们的身体姿势以及它是如何影响我们的整体健康和福祉的。
然而，保持良好的姿势对预防背部疼痛、减少受伤风险以及促进脊柱正确对齐至关重要。
定期评估我们的体态非常重要，以确保我们没有给我们的肌肉和关节施加不必要的压力。
One way to assess body posture is to stand in front of a mirror and observe how your body is positioned. Are your shoulders slouched forward? Is your back straight or hunched over? Are your hips aligned properly? By taking a few moments to visually assess your posture, you can start to make adjustments to improve your alignment and reduce strain on your muscles.评估身体姿势的一种方法是站在镜子前，观察你的身体位置。
英语作文介绍手势语音
英语作文介绍手势语音Title: Exploring the World of Sign Language。
Introduction:Sign language, a visual-spatial language utilizing hand movements, facial expressions, and body gestures, is a fascinating means of communication that bridges linguistic gaps. Originating from various cultural and linguistic backgrounds, sign languages possess their own grammatical rules and vocabulary. In this essay, we delve into the intricacies of sign language, its significance, and its impact on society.Historical Context:Sign language has a rich history, evolving alongside spoken language. Historically, sign language was often marginalized, perceived as inferior to spoken language. However, it gained recognition as a legitimate linguisticsystem in the 20th century, leading to increased research and advocacy for its inclusion in education and public services.Components of Sign Language:Sign language encompasses a variety of components, each contributing to its expressive nature:1. Handshapes: Different hand configurations represent letters, words, or concepts. These handshapes are crucial in conveying meaning accurately.2. Movements: The movement of hands, arms, and body plays a significant role in sign language grammar. Movements indicate tense, aspect, and other grammatical features.3. Facial Expressions: Facial expressions complement signs, conveying emotions, tone, and intensity. They provide context and nuance to the conversation.4. Space and Directionality: Sign language utilizes space around the signer to represent spatial relationships, pronouns, and verb agreement.5. Non-Manual Signals: These include head movements, eye gaze, and body posture, which contribute to the grammatical structure and meaning of signs.Importance of Sign Language:Sign language serves as a vital mode of communication for deaf and hard-of-hearing individuals. It grants them access to information, education, and social interaction, fostering inclusivity and equality. Moreover, sign language benefits hearing individuals, promoting cultural understanding and linguistic diversity.Sign Language in Education:In recent years, there has been a growing recognition of the importance of incorporating sign language into educational settings. Bilingual education programs, whichintegrate sign language and spoken language, have proven effective in facilitating academic success and linguistic development for deaf students. Additionally, sign language classes are increasingly offered to hearing students, promoting communication skills and cultural awareness.Challenges and Advocacy:Despite its significance, sign language still faces challenges, including limited recognition, insufficient resources, and societal misconceptions. Advocacy efforts seek to address these issues by promoting sign language as a fundamental human right, advocating for its inclusion in legislation, education, and public services.Conclusion:Sign language is not merely a mode of communication but a vibrant expression of culture, identity, and community. As we strive for a more inclusive society, embracing sign language is essential in ensuring equal access and participation for all individuals, regardless of theirhearing status. Through continued advocacy, education, and awareness, we can create a world where sign language is celebrated and valued as a vital component of human diversity.。
一种新颖的手部运动跟踪系统
一种新颖的手部运动跟踪系统马英红;杨家玮;惠蕾放;李烨;MAO Zhihong;SUN Mingui【摘要】Most existing hand tracking systems have restrictions on human motion. This paper presents a wireless, wearable, and unobstructive wrist-finger tracking system, which uses a small magnet affixed on each fingernail as a position marker and a set of small magnetic sensors attached to an electronic wristband as a detector array. As the wrist and the finger move, the combined magnetic field from all magnets is detected by each sensor at a specific .wrist location. The detected data are fed into a hand posture estimator to inversely calculate the hand posture based on a mathematical system model. The measurability and trackability of hand movements in the system are validated, respectively, by measurement and hand tracking experiments.%目前已有的手部运动跟踪系统大多在一定程度上限制了人体运动自由.基于此,提出一种无线、可穿戴、无障碍的腕关节、指关节运动跟踪系统.在人体每个手指甲上粘贴一轻小永磁体,用以产生标示腕关节、指关节运动的信号；若干磁传感器置于手腕处的电子腕带上,作为标示信号(磁信号)检测器.当腕关节、指关节运动时,永磁体在各传感器所在位置处的合成磁场发生变化,传感器对该磁场信号进行测量,所检测到的磁场信号送入手部姿势估计器,估计器基于系统数学模型计算手部姿势,从而实现对手部运动的跟踪.【期刊名称】《西安电子科技大学学报（自然科学版）》【年(卷),期】2012(039)001【总页数】7页(P79-85)【关键词】人机交互;手部运动;永磁【作者】马英红;杨家玮;惠蕾放;李烨;MAO Zhihong;SUN Mingui【作者单位】西安电子科技大学综合业务网理论及关键技术国家重点实验室,陕西西安710071;匹兹堡大学计算神经外科实验室,匹兹堡宾夕法尼亚州美国 15260;匹兹堡大学电子与计算机工程系,匹兹堡宾夕法尼亚州美国 15260;西安电子科技大学综合业务网理论及关键技术国家重点实验室,陕西西安710071;西安电子科技大学综合业务网理论及关键技术国家重点实验室,陕西西安710071;西安电子科技大学综合业务网理论及关键技术国家重点实验室,陕西西安710071;匹兹堡大学电子与计算机工程系,匹兹堡宾夕法尼亚州美国 15260;匹兹堡大学计算神经外科实验室,匹兹堡宾夕法尼亚州美国 15260;匹兹堡大学电子与计算机工程系,匹兹堡宾夕法尼亚州美国 15260【正文语种】中文【中图分类】TP391计算机微型化、随身化和人性化近年来已经成为一个重要的发展趋势(以手持电脑和智能手机为代表),而基于键盘/鼠标的传统人机交互方式则成为这一趋势的瓶颈技术.一方面,键盘、鼠标等输入工具均需占据大量的设备空间或操作空间;另一方面,人机交互操作无法摆脱这类机械型输入工具的束缚,这种非自然的、方式单一的交互操作往往导致许多慢性病的发生,例如颈椎病和腕管综合症等.近几年,为迎合微型化的趋势,输入设备(尤其是键盘)越来越小且紧凑,然而,人手尺寸不可改变,致使输入操作变得更加困难.解决上述问题和矛盾的理想方案无疑是抛弃键盘/鼠标等机械设备,而将人体本身转化为人机交互的输入工具.手是人体最灵活的器官之一,高机械自由度及其与人体神经系统的连接,使其能够做出大量姿势和动作[1],从而传递多种信息.因此,人手本身便是一种潜在的自然输入/交互工具.如果人手在自由空间中的运动能够用一种简单、可穿戴且无障碍的装置进行跟踪,那么,人将有可能通过手与计算机进行自由交互.目前,手部动作识别/跟踪技术主要有两类[2-6]:数据手套技术和基于图像处理的手势识别技术.数据手套技术[2-3]利用力传感器和加速度计直接测量手部各关节的位置和角度来得到手部动作.该技术可以满足实时处理的要求并可获得可靠数据,但手套本身及其电路连线使其较为笨重,不方便使用,且昂贵的成本往往令普通用户难以接受.基于图像处理的技术[2,4-6]是一种更自然的、非接触的手部姿势识别方法.然而,该技术存在难以解决的自遮挡/拍摄死角问题.而且,人手必须在拍摄设备所能覆盖的范围内运动,这仍在一定程度上限制了操作自由.笔者提出了一种基于磁场的手部运动跟踪系统.该系统由粘贴于各个手指甲上的轻小永磁体和一个置有磁传感器的电子腕带构成.永磁体随手部运动在手腕处产生变化的合成磁场信号,腕带上的磁传感器检测该信号,并将测得数据送入手部姿势估计算法器进行手势识别和跟踪.这是一低成本、简单、可穿戴、无障碍的手部运动跟踪系统.适当的软件设计可使其应用于电脑输入、相机操控以及其他多种应用,例如,轮椅/机器人控制、手语交流以及帕金森等疾病的检测与控制.图1为笔者提出的手部运动跟踪系统的结构图.人手的手指甲上各贴附一小的永磁体,手腕处戴一电子腕带,腕带由若干磁传感器、数据处理单元以及手势估计器组成.永磁体在各传感器所在位置形成的合成磁场随手部运动而变化.各磁传感器检测其所在位置处的磁场强度,传感器输出信号经数据处理单元采集处理后得到磁通密度数据.手部姿势估计器利用磁通密度数据对手部姿势进行计算,从而跟踪手部运动.图2给出了系统的信号传输及数据处理流程.2.1 一般化系统方程(1)永磁体模型:设一小的永磁体位于(a,b,c)处,其归一化磁化方向矢量H=[m,n,p]T,见图3.该磁体在离它足够远的任一空间位置(x, y,z)处产生的磁通密度为[7]其中,和分别为介质相对磁导率和空气磁导系数,M为永磁体的磁矩;Bx,By和Bz分别表示3个相互正交的系统坐标轴方向上的磁通密度分量,可表示为[7](2)手部模型:人手是一个多关节、多自由度(Degree Of Freedom, DOF)的运动器官.令θ1,θ2,θ3分别表示关节的屈曲/伸展角度、内收/外展角度以及旋转角度,则人体手部运动的自由度如图4所示[6].图中,MCPJ,PIPJ和DIPJ分别表示手指根关节(Metacarpophalangeal Joints),手指中间关节(Proximal Interphalangeal Joints)以及手指指端关节(Distal Interphalangeal Joints);WJ表示腕关节(Wrist Joints);CMCJ和IPJ分别为拇指的掌指关节(Carpometacarpal Joint)和指间关节(Interphalangeal Joint).人手拇指自由度个数为5,食指、中指、无名指和小指自由度的个数均为4,腕关节自由度的个数为6(包括4个屈曲/伸展自由度,1个内收/外展自由度以及1个旋转自由度),因此,人手具有27个自由度.手部姿势定义为这些自由度状态的组合,可用手势向量表示.其中,θwr为手腕自由度向量,依次为拇指,食指,中指,无名指以及小指的自由度向量.(3)系统方程:现设系统有K个永磁体是集合的子集,则第k个永磁体的位置和方向信息,可由手势向量通过手部几何模型映射获得,再将所得映射关系式代入式(1)～(4),则可以得到永磁体k在磁传感器l处产生的磁通密度表达式:式中,;Mk为永磁体k的磁矩;L为系统中磁传感器的个数;表示由到的映射关系(相差一个常数因子ηk).L个磁传感器处的瞬时磁通密度为其中,,为传感器l的位置矢量(已知),为磁矩矢量.方程(6)称为一般化系统方程.2.2 系统方程具体化示例基于假设1至假设3,可构建一种简化的手部模型(拇指除外),如图5所示.假设1 手指仅MCPJ作有限幅度的屈曲和伸展运动,从而DIPJ角度和PIPJ角度可视为固定值.这样,固定于手指k(k=1,…,4,依次代表食指、中指、无名指和小指)上的磁体将在以MCPJ为中心、r0k为半径的圆上运动.假设2 手腕关节亦仅考虑其屈曲和伸展运动.假设3 每个永磁体的磁化方向垂直于其所在手指甲的切平面,如图5所示.图5中,和表示手指k的相应骨长(固定值);dk为手指k的腕关节的y坐标(固定值);ωk表示手指运动平面与x Oz平面的夹角(固定值);ϕk和ψk分别为腕关节屈曲/伸展角度和手指k的MCPJ屈曲/伸展角度.该模型中,手部姿势向量由ϕk和ψk(k=1,…,4)决定,表示为利用该几何模型,位于手指k上的永磁体的位置和方向信息可表示为手指k的姿势参量的函数,即其中,通常,和满足以下关系[7-8]:因此,系统方程可写成式中,磁矩向量手部运动跟踪即指由磁通密度向量M反向计算手势向量Θ.然而,高阶非线性系统方程(6)不存在一个闭型解[7,9],而且方程的复杂性使得对于方程解的个数难以分析.因此,笔者采用非线性数值优化算法对其进行求解,总能求得优化准则意义上的一个手势向量最优解.考虑噪声的存在,则实际应用中,系统方程应为表示磁通密度的测量值;w为L×1噪声向量.采用最小平方(Least Squares,LS)优化算法[10],由测量值对Θ进行估计.定义最小平方代价函数这里,表示弗罗贝尼(Frobenius)范数.为消除未知量η,进行最优修正:其中,F+表示矩阵F(Θ)的伪逆.那么,可利用数值优化算法最小化J0得到手部姿势向量Θ.若当前时刻的磁通密度测量值为,则优化算法的一般过程为:由一初始点Θ0(j)开始,沿代价函数下降方向迭代更新手势向量,直至算法认为代价函数J0达到最小时,输出手势向量Θ(j)的估计值(j).一般而言,手部运动为连续运动.因此,时刻j的手势估计值可用作j+1时刻优化算法的初始点,即首先通过测量数据验证手部运动的可测性,然后利用上述系统模型进行手部运动跟踪试验4.1 手部运动可测性验证由方程(1)可见,磁通密度随测量点与磁体之间距离的增大而迅速减小.因此,有必要验证永磁体在手腕处产生的磁场的可测性以及该磁场对不同手部运动的灵敏性.测量实验中,将一个2轴磁传感器(HMC1002,Honeywell International Inc.)置于手腕处.该传感器可测量两个相互正交的方向上的磁场信号(分别记作x轴和y轴),其测量范围、分辨率及灵敏度分别为±2×T以及).两个圆盘形(如图1所示)永磁体(K&J Magnetics,Inc.)分别粘贴在食指和中指的指甲上.实验分别测量了食指和中指做周期性屈曲/伸展运动(手腕固定不动)时传感器检测到的磁场信号,如图6所示. 图6的测量数据清晰表明,手指甲上的永磁体在手腕处能够产生足够强的磁场.图6(a)和图6(b)信号的明显差别则表明,永磁体在手腕处形成的磁场对不同手部运动具有足够的灵敏性.4.2 手部运动跟踪试验利用上述的系统模型,对两种手部运动进行跟踪:(1)食指的屈曲/伸展运动;(2)食指和中指的同步屈曲/伸展运动.试验中,系统的磁矩向量η=(1,1,0,0)T.试验采用简单的模式搜索数值优化方法[11]计算手势向量Θ的最优解,该方法无须目标函数的导数信息,并且假设已知零时刻的手势向量Θ(0).试验1 令食指做一次屈曲/伸展往返运动:ψ1由0增大到π4,再由π4减小到0.其他手指和手腕关节固定不动:.图7(a)是该运动过程中,手腕处6个磁传感器处的加噪磁场信号(信噪比为20 dB).图7(b)给出了该信号下,手部运动的跟踪结果.试验2 令食指和中指做一次同步屈曲/伸展往返运动:ψ1和ψ2同时由0增大到π4,再由π4减小到0.其他手指和手腕关节固定不动:.图8(a)给出了手腕处的6个磁传感器处的加噪磁场数据(信噪比为20 dB),图8(b)是相应的手部运动跟踪结果.从试验结果可以看到,尽管仅采用了简单的跟踪算法并考虑了噪声的存在,手部运动仍能利用该系统得到跟踪.为考查传感器数量对系统跟踪性能的影响,在手腕处均匀放置不同数量的传感器,对试验1和试验2的手部运动进行跟踪(无噪).图9给出了手势跟踪误差随传感器数量变化的情况.图中,平均手势跟踪误差E定义为图9表明,系统的手部运动跟踪精度随传感器数量的增加而提高,但当传感器增加到一定数量后,跟踪精度的提高不再明显.这是因为,增加传感器个数L将为手部运动跟踪提供更多数据信息,有利于提高优化算法中手势向量更新方向的正确性,从而提高算法精确度.但传感器的密集程度随L的增加而增大,各传感器测得的磁场数据之间的相关程度越来越高,因此,增加传感器所带来的信息的增加量越来越小.笔者提出一种低成本、无障碍、便于使用的手部运动跟踪系统.该系统仅利用粘贴于手指甲上的轻小永磁体和位于手腕处的磁传感器来感知手部动作.通过分析永磁体的数学模型和手部模型,给出了一般化系统数学描述方程,并基于描述方程构造了最小平方手部运动跟踪算法.实测数据和跟踪试验验证了系统手部运动的可测性与可跟踪性.目前,该系统的研究和实验尚处于初步阶段,仍有许多问题有待在后续工作中进一步探索,例如,如何建立完整的手部几何模型,使其能够适用于任意复杂的手部运动;多个手指不规则运动时,各手指磁场间的互扰情况如何;如何选择和改进手部运动跟踪算法,使其能够较迅速、较精确且较灵敏地跟踪各种复杂的手部运动.[4]Garg P,Aggarwal N,Sofat S.Vision Based Hand GestureRecognition[J].World Academy of Science,Engineering and Technology,2009,49:972-977.[5]Ho M F,Tseng C Y,Lien C C,et al.A Multi-view Vision-based Hand Motion Capturing System[J].Pattern Recognition,2011,44(2):443-453.[6]Vaezi M,Nekouie M A.3D Human Hand Posture Reconstruction Using a Single 2D 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Vancouver:IEEE,2008:4222.[10]Baillet S,Mosher J C,Leahy R M.Electromagnetic Brain Mapping[J].IEEE Signal Processing Magazine,2001,18 (6):20-21.[11]Torczon V.On the Convergence of Pattern Search Algorithms[J].SIAM Journal on Optimization,1997,7(1):1-25.【相关文献】[1]Mao Z H,Lee H N,Sclabassi R J,et rmation Capacity of the Thumb and the Index Finger in Communication [J].IEEE Trans on Biomedical Engineering,2009,56(5):1535-1545.[2]Wu Y,Lin J Y,Huang T S.Capturing Natural Hand Articulation[C]//Proceedings of IEEE International Conference on Computer Vision(ICCV’01):Vol 2.Vancouver:IEEE,2001:426-432.[3]Won D,Lee H G,Kim J Y,et al.Development of a Wearable Input Device Based on Human Hand-motions Recognition [C]//Proceedings of IEEE/RS International Conference on Intelligent Robots and Systems.Sendai:IEEE,2004:1636-1641.。
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Hand Posture Recognition in a Body-Face centered spaceSébastien MarcelFrance Telecom CNET2 avenue Pierre Marzin22307 Lannion, FRANCEsebastien.marcel@cnet.francetelecom.frABSTRACTWe propose to use a neural network model to recognize ahand posture in an image. Hand gestures are segmentedusing a space discretisation based on face location andbody anthropometry.KeywordsNeural networks, hand posture recognition, discrete space.INTRODUCTIONLISTEN is a real-time computer vision system whichdetects and tracks a face in a video image [2]. In thissystem, faces are detected, within skin color blobs, by amodular neural network. This paper deals with a LISTEN based system using hand posture recognition to execute a command. In order to detect the intention of the user to issue a command, "active windows'' are investigated in the body-face space. When a skin color blob enters an "active window'', hand posture recognition, using a specific neural network for each hand posture, is triggered.THE BODY-FACE SPACEWe map over the user a body-face space based on a "discrete space for hand location" [4] centered on the face of the user as detected by LISTEN.Figure 1. Body-face spaceThe body-face space (Figure 1) is built using the anthropometric body model expressed as a function of the total height (Figure 2) itself calculated from the face height.Figure 2. Anthropometric body modelIn our images, faces and hands have small sizes. Therefore, hand posture recognition becomes a hard task.THE NEURAL NETWORK MODELNeural networks, such as discriminant models [5] or Kohonen features maps models [1], have previously been applied to hand posture recognition. In this work, we propose to use a neural network model already applied to face detection: the constrained generative model (CGM) [3] (Figure 3).Figure 3. Constrained generative modelThe goal of the constrained generative learning is to closely fit the probability distribution of the set of hands using a non-linear compression neural network and non-hand examples. Each hand example is reconstructed as itself and each non-hand example is constrained to be reconstructed as the mean neighbourhood of the nearest hand example. Then, the classification is done by measuring the distancebetween the examples and the set of hands.RESULTS ON OUR DATABASEA small set of hand postures was selected: A, B, C, Five,Point and V. A database of thousands various images with both uniform and complex backgrounds was built. The window sizes for each posture are: 20x20 for A, 18x20 for C and Five, and 18x30 for B, Point and V (Figure 3). Then images are tested at different position and scale in order to frame the hand posture.Figure 3. Images form our test databaseMost of the database was used for training and the remainder was used for testing. Although the images with complex backgrounds are more difficult to learn, the CGM achieves a good recognition rate and a small false alarm rate (Table 1 and 2).The false alarm rate is of prime necessity to evaluate the performance of the system, and must be small comparing to the number of tests in a image. At the present time, the false alarm rate on various images containing no hands is around 1 false alarm for 27777 tests.RESULTS ON A BENCHMARK DATABASEFigure 4. Images form Jochen galleryThe CGM was also tested on the Jochen hand posture gallery [6]. This database contains 128x128 grey-scale images of 10 hand signs performed by 24 persons against uniform light, uniform dark and complex backgrounds. We only tested the A, B, C and V postures (Figure 4).The CGM applied to hand posture recognition gives satisfactory recognition results (Table 3 and 4) on this benchmark database. These results can be improved by adding more non-hand examples in order to lower the false alarm rate.CONCLUSIONWork is in progress to integrate the hand posture detection and the body-face space in a new LISTEN based system.A future work is to supply LISTEN with a hand gesture recognition kernel based on stroke detection and motion analysis.REFERENCES[1] Boehm, K. and Broll, W. and Sokolewicz, M., Dynamic Gesture Recognition Using Neural Networks: A Fundament for Advanced Interaction Construction.SPIE, Conference Electronic Imaging Science and Technology (1994).[2] Collobert, M., Feraud, R., Le Tourneur G., Bernier, O.,Viallet, J.E., Mahieux, Y. and Collobert, D. LISTEN: A System for Locating and Tracking Individual Speakers .2nd Int. Conf. on Automatic Face and Gesture (1996),283-288.[3] Feraud, R. PCA, Neural Networks and Estimation for Face Detection. NATO ASI , Face Recognition: from Theory to Applications (1997), 424-432.[4] McNeill, D. Hand and Mind: What gestures reveal about thought. Chicago Press (1992).[5] Murakami, K. and Taguchi, H. Gesture recognition using Recurrent Neural Networks. CHI '91, 237-242.[6] Triesch, J. and Malsburg, C. Robust Classification of Hand Postures against Complex Backgrounds. 2nd Int.Conf. on Automatic Face and Gesture (1996), 170-175.。