外骨骼机器人

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Advanced Robotics
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Exoskeletal meal assistance system (EMAS II) for
patients with progressive muscular disease
Yasuhisa Hasegawa a, Saori Oura a & Junji T akahashi a
a Graduate School of Systems and Information Engineering, University of T sukuba 1-1-1
T ennodai, 305-8573, T sukuba, Japan.
Published online: 09 Oct 2013.
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Advanced Robotics ,2013
V ol.27,No.18,1385–1398,/10.1080/01691864.2013.841311
FULL PAPER
Exoskeletal meal assistance system (EMAS II)for patients with progressive muscular disease
Yasuhisa Hasegawa ∗,Saori Oura and Junji Takahashi
Graduate School of Systems and Information Engineering,University of Tsukuba 1-1-1Tennodai,Tsukuba 305-8573,Japan
(Received 28March 2012;accepted 28June 2012)
Patients with muscle weakness such as muscular dystrophy usually need someone’s assistance in their daily activities.In order to reduce the caregiver burden and to improve quality of life (QOL)of the patients,various robotic technologies have been developed.This paper presents an exoskeletal assistance system EMAS II for the patients,which assists the upper extremity for the purpose of daily activities such as eating,writing,or other desk works.The EMAS II assists four DOF;shoulder flexion-extension,shoulder abduction-adduction,shoulder medial-lateral rotation,and elbow flexion-extension.The EMAS II has three kinds of user interfaces which are operated by residual functions of the patients,because it is important for patients’health and initiative to use the residual functions.In order to control the four DOFs exoskeleton system using the interfaces with less DOF,the EMAS II simulates upper limb motion patterns of healthy people.The patterns are modeled by extracting correlations between the height of the wrist joint and that of the elbow joint.Therefore,users have only to control the position of their wrist joint to do tasks at a table.Through an experiment with a healthy subject,the feasibility of meal assistance by the EMAS II was confirmed.Furthermore,the system was applied to a spinal muscular atrophy patient in a clinical trial to check the usability.The experimental results indicated that the EMAS II could support the patient’s upper extremity to do tasks at a table.
Keywords:assistive device;exoskeleton;upper limb;muscular dystrophy;muscular disease;eating;motion support
1.Introduction
Patients with neuromuscular disease are limited in activities of daily living (ADL).For instance,patients with progres-sive muscular dystrophy (PMD)or with spinal muscular atrophy (SMA)have difficulty in walking,lifting their arms against gravity,and changing overall posture,because of muscle weakness of their trunk,extremity,particularly of their proximal parts.Thus,the patients require caregiver’s support appropriate to their symptoms in many aspects of everyday lives.As a result,the caregiver burden and sacri-fice of the patient’s independence have been a problem for a long time.In order to avoid the issue,robotic devices have been developed for life-supporting or rehabilitation.
Assistive devices for upper limb disorders can be divided into robot arm type and exoskeleton type.Examples of the robot arm type are the MySpoon,[1]the Handy 1,[2]and the iARM (Assistive Robotic Manipulator).[3]These robots re-place the patients’arm in a variety of tasks such as eating and picking up distant objects with high positioning accuracy.They can be used by the advanced stage patients with limited residual functions.However,disuse of the sections with or without impairment is not desirable from the viewpoint of prevention of joint contracture,encouragement of the patients’initiative,and improvement of the quality of life (QOL).Therefore,the assistive device for those patients
∗Corresponding author.Email:hase@iit.tsukuba.ac.jp
should be the one that assists the user’s upper limb,and that is the exoskeleton type.The Armon,[4]which is an example of the exoskeletal system,is a spring-balanced arm support system and a user can lift up his/her arm without being affected by the force of gravity.It has no actuators so that the patient with severe symptoms cannot carry out works such as reaching task even if he/she uses this device.On the other hand,there are exoskeleton type devices using actu-ators such as the ARMin,[5]the ABLE,[6]the BONES,[7]and the Muscle Suit.[8]Although they are developed for rehabilitation of disabled people or assistance for healthy people to do heavy work,each of the devices are too large to be applied in daily lives.When we develop assistive systems for people with progressive muscle weakness or paralysis,we should design a versatile support system which has adjustable support level and can improve the patients’physical condition and initiative.
The EMAS I shown in Figure 1is an exoskeletal meal assistance system which our laboratory has developed for the patients with muscle weakness.There are two DOFs at the shoulder (flexion-extension and medial-lateral rotation),and one DOF at the elbow (flexion-extension).Each joint is light and small because of the wire driven system and is operated by a foot controller as shown in Figure 2.All the parts except a user interface are mounted on a chair.
©2013Taylor &Francis and The Robotics Society of Japan
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Figure 1.EMAS I.
Through an experiment,where a healthy subject regarded as a muscular dystrophy patient ate a meal with the EMAS I,we confirmed the possibility of the meal assistance using the EMAS I.However,there were two problems:one was that the elbow joint hit table according to the height of the table,and the other was that the foot controller was not easy to use for the muscular dystrophy patient because he/she did not have enough capability of movement on his/her leg.In this paper,we propose the EMAS II which has four actuated joints (shoulder flexion-extension,shoulder medial-lateral rotation,shoulder adduction-abduction,and the elbow flexion-extension)and one unactuated joint for the shoulder girdle and upper body motions.While the EMAS I is mounted on a chair or a wheelchair,this EMAS II is mounted on a special stand so as to apply to all types
of wheelchairs.Therefore,a caregiver has only to put the EMAS II next to the wheelchair and put the care receiver’s arm into the orthosis.In addition,we propose three user interfaces,trackball type,joystick type,and BEP type in-terface so that the patient can choose more preferable one according to his/her physical condition or residual func-tions.Because the user interfaces have only three DOFs at most while the exoskeleton has four actuated joints,the controller of the EMAS II needs a condition of constraint to control each joint angle.The constraint condition is set find-ing corresponding between the height of the elbow position and that of the wrist position of healthy people.
The purpose of this study is to develop an assistive system EMAS II for patients with muscle weakness in order to reduce the caregiver burden and to improve the initiative and QOL of the patients.In this study,the system applies to the patients with muscle weakness of proximal parts such as shoulder and elbow.
2.EMAS II
2.1.System architecture
The EMAS II consists of an exoskeleton part,a user interface,a motor unit,a control device,and a power supply as shown in Figure 3.The motor unit contains four gearhead motors,a motor driver circuit,and encoders.Every com-ponent except for the user interface is fixed on a special stand which is a remodeled walker in order to enable the patients to use the EMAS II remain seated on a wheelchair because the most common early symptom of the disease is lower-extremity muscle weakness and many of the patients spend most of their time on a wheelchair (Figure 4).The exoskeleton part is small and light because of the wire
drive
Figure 2.Foot controller of EMAS I.
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Figure 3.Sideview of the EMAS
II.
Figure 4.The EMAS II with a trackball type interface.
system.Moreover,there are several user interface devices so that the user can choose most preferable one according to his/her physical conditions.The user controls the right upper limb operating the user interface with the opposite hand.
2.2.Mechanism of joints
When healthy people do tasks on a table,they use not only shoulder and elbow rotation motions but also shoulder girdle motions and upper body motions.Therefore,mech-anisms which are given no regard to such motions bring a sense of discomfort to a user and reduction of range of motion.There have been researches of shoulder mechanism
Table 1.Arrangement of joint ID,motion,θdirection,power source.
Joint ID Motion θdirection Power source
J 1
flexion θ+1motor 1extension
θ−1gravity shoulder
J 2medial rotation θ+2motor 2lateral rotation θ−2spring J 3
abduction θ+3motor 3adduction θ−3gravity elbow
J 4
flexion θ+4motor 4extension
θ−4
gravity
with assistance of shoulder girdle movements for exoskele-ton robot.[9]However,the mechanism is too large to apply for assistive devices for ADL.In this study,the assistive system has four actuated joints (shoulder flexion-extension,shoulder medial-lateral rotation,shoulder abduction-adduction,and elbow flexion-extension)and one unactuated joint for shoulder girdle and upper body movements.The four joints are driven via wires as shown in Figure 5.Here joint numbers,J 1...J 5,and the positive rotation arrow are assigned to each joint as shown in Figure 6.During the assistance with the EMAS II,shoulder flexion,medial rotation,abduction,and elbow flexion motions are driven by motor traction.The shoulder extension,adduction,and elbow extension are driven using gravity force,and the shoulder lateral rotation is driven by torsion spring because the gravity force does not affect that motion.Table 1summa-rizes the joint numbers,the motions,the rotation direction,and the power sources.
The minimum angles,the maximum angles,the range of motions of all the joints and the range of motions for human ADL are summarized in Table 2.[10]The maximum torques of actuated joints of the system and the that of unimpaired arm are summarized in Table 3.The origin of angular vector [th 1,th 2,th 3,th 4,th 5]=[0,0,0,0,0]is the arm’s neutral position.The minimum angle of the J 4is set at 10degrees in order to avoid the singularity.The range of motions of the EMAS II is narrower than that for ADL,because the EMAS II takes into account the use only for activities which are done at a table.In addition,the maximum joint torques of the EMAS II are smaller than that of healthy upper limb.However,it has enough torque to support a man lifting 0.5[kg ]of bottle on his hand.The feasibility of assistance with limited range of motions and joint torques is discussed in chapter 3.
The unactuated slider mechanism is shown in Figure 7to allow for upper body and shoulder girdle motions.The slide rail passively moves 200mm on a slider,while a slider generally slides on a slide rail.The weight of EMAS II with a user’s arm is directory loaded to the special stand through an attachment which is tilting within a range of 0to 50degree to the floor.Here,a coordinate system is defined as shown in Figure 8.Then,the working surface of wrist joint,
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Figure 5.Wire
arrangement.
Figure 6.Definition of positive direction of each joint.
Table 2.Raneg of motion.
The EMAS II The EMAS II The EMAS II Human for ADL Min.Angle Max.Angle Range of Motion
Range of Motion
[degree]
[degree]
[degree]
[degree]
θ107070100θ208080110θ309090135θ4
10130110150
where the height of the wrist joint is fixed at 200[mm]below the shoulder joint and the elbow joint is located above the level of wrist joint,is shown in Figure 9.
Table 3.Maximum joint torques.
The EMAS II [Nm]
Unimpaired Arm [Nm]
θ147.353.5θ222.953.9θ315.053.6θ4
38.5
81.0
2.3.Derivation of joint angles
When a user uses the EMAS II,he/she inputs three-dimensional wrist position through a user interface while the number of active joint is four.Therefore,at least one condition of constraint is required to control all joints.The
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Figure 7.Unactuated slider for trunk and shoulder
girdle.
Figure 8.Coordinate system of working space.The origin of this coordinate system corresponds approximately to the center of user’s right shoulder
joint.
Figure 9.Range of motion of the EMAS II at a table.The origin is the center of the left shoulder.
condition of constraint is derived from motion measurement of upper body.Although there have been similar researches for motion measurement and analysis of upper limb,[11,12
]
Figure 10.Arrangement of markers for the motion capture.
none of them refer to arm support.We consider eating a meal to be one of the most important tasks to help the patients to lead more independent lives and to reduce the caregiver burden.Therefore,in this study,upper limb mo-tions while people eat a meal without dropping food are measured in order to develop the expression for the condition of constraint.
2.3.1.Meal motion measurement
The meal motions were captured by the PhaseSpace IMPULSE system [13]of PhaseSpace Inc.Nine markers were attached to the EMAS II and a subject,and eight cameras tracked them (Figure 10).The capturing rate was 120frames per second.During the measurement,a subject who was wearing the EMAS II picked up foods from two plates on a table and ate them in five times each.The plates were put in a row in front of the subject and the height of the table was 690[mm].This measurement was done by three subjects (Subjects 1,2,and 3),one was a woman aged twenty-two and two were men aged twenty-two and thirty,respectively.All subjects were physically unimpaired and right dominant.
2.3.2.Measurement results
Figure 11shows one example of the result of the motion measurement,where the second subject scooped food (the gray area)and carried it to the mouth (the white area).From here,we focus on the motion for carrying the food to the mouth for getting the condition of constraint.
One example of the scatter diagrams of the wrist posi-tion (x w ,y w ,z w )and the elbow position (x e ,y e ,z e ),and their approximated curves are shown in Figure 12.Then,it is found that all R-squared values of the approximated curves representing the relationship between z w and z e of
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all subjects are over 0.9.Here,Figure 13shows the scat-ter diagrams and approximated curves of the relationship between z w and z e of all
subjects.
Figure 11.Example of result of the meal motion analysis.
The mean curve of these approximated curves are shown in Figure 14,which is expressed by
z e =−0.0008z w 2+0.5316z w +171.23.
(1)
Using this correlation model,the z e is fixed from the wrist position.y e and x e are also fixed as follows:
y e =
−Q ±
Q 2−P R
P
,(2)P =x w 2+y w 2,(3)
Q =z w z e y w −Ay w ,
(4)R =(A −z w z e )2+x 2w (z 2e −L 21),
(5)A =
L 21+L 23−L 22
2,(6)L 3=
x 2w +y 2w +z 2w
,(7)
x e =⎧⎨⎩L 23+L 21−L 2
2−2(y e y w +z e z w )2x w
,
if x =0
L 21
−y 2e −z 2e ,
if x =0
,(8)
where L 1is length of user’s upper arm and L 2is that of
forearm,respectively.In addition to the above
expressions,
Figure 12.Example of relationships between the wrist position and the elbow position.
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D
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S
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a
n
g
h
a
i
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i
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r
s
i
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2
N
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Figure 14.Approximation curves of relationship between the
height of the wrist joint and that of the elbow joint.
Table 4.Error of elbow position.Subject Plate RMS of Error [mm]
Subject 1Left 33.12Subject 1Right 19.66Subject 2Left 58.58Subject 2Right 27.79Subject 3Left 51.51Subject 3Right
30.31Mean
36.83
considering the range of motion (Table 2),we obtain the elbow position.
Finally,each joint angle θ1...θ4is given by,
θ1=cos −1
y w − 1+L 2
L 1
cos θ4 y e L 2cos θ2sin θ4
,(9)θ2=sin
−1
x w − 1+L 2
L 1cos θ4 x e L 2sin θ4
,(10)θ3=sin −1 −
x e
L 1cos θ2
,and (11)θ4=cos −1 −L 21+L 22
−(x w 2+y w 2+z w 2)2L 1L 2
.
(12)The precision of the model is evaluated by comparing the measured elbow position of an elbow joint and the predicted position based on the model.RMS of errors of them are shown in Table 4.The results show that an mean error is 37[mm].In order to make up for the personal difference,the third term is adjusted according to the user’s shape or height of the table used.
2.4.Control approach and user interfaces
When we develop assistive devices,it is also necessary to develop easy-to-use user interfaces which take advantage of
patients’residual functions.In this section,the user inter-faces and control approach of the EMAS II are mentioned.2.4.1.Control approach
The EMAS II has two control modes:manual control and semi-automatic control.A user can control three-dimensional coordinate of his/her wrist position through the user interface with the manual control.On the other hand,the semi-automatic control is developed in order to improve the utility and efficiency of the system operation.In this control mode,the user’s upper limb is automatically moved to pre-established position by pressing a switch.For example,if the user set his/her mouth position,she/he can bring food to his/her mouth smoothly and finish the meal in a shorter time.The control flows of each mode are shown in Figures 15and 16.
2.4.2.Trackball type interface
We propose three kinds of user interfaces.When user uses the trackball interface shown in Figure 17,the controller measures the pulses of the trackball interface at every 5.0[ms ]and determines the wrist position.
During low-speed rotation,the controller determines the
target wrist position,W re f (x ref w ,y re f w ,z ref
w )in the coordi-nate system shown in Figure 8,as shown below:
x ref w =x w +C 1x rot ,(13)
y ref w =y w +C 1y rot ,
and
(14)z ref w =z w +C 2z rot ,
(15)
where W (x w ,y w ,z w )is the current wrist position,
R (x rot ,y rot ,z rot )is the amount of change of the rotation of the trackball,and C 1,C 2are the arbitrary constants.On the other hand,during high-speed rotation,the controller continues only to measure the pulses until the rotation speed becomes lower than a threshold.Meanwhile,the arm is controlled to be held in the same position.Then,the con-troller determines the target wrist position and straight-line
trajectory.Let R t (x t rot ,y t rot
)denote the number of total rotations of the ball,a denote the arrival time and C 3,C 4denote an arbitrary constant.Then,the target position and arrival time is calculated as below:
x p =x w +C 3x n x t
rot ,
(16)
y p =y w +C 3y n y t rot ,and (17)
t =C 4|n |
.(18)
Finally,change of velocity v (v x ,v y )with time is deter-mined by the expression shown below:
|˙v |=−4b
t
2 x −t 2
2+b ,(19)
b =3l
2t
,and
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Figure 15.Control flow of the manual
control.
Figure 16.Control flow of the semi-automatic
control.
Figure 17.Trackball type interface.l =
(x p −x w )2+(y p −y w )2.(21)
In this way,switching the action at low-speed and high-speed rotation,the user can move the arm over a long distance at once or move the arm delicately depending on the situation.Moreover,pressing the switch at the corner of the trackball interface,the control mode is shifted to the semi-automatic mode,and the wrist position is moved to the pre-established position without any operation.When the EMAS II is finished moving the arm,the control mode is shifted to the manual mode again.
2.4.
3.Joystick type interface
Figure 18shows the joystick type interface which has two
joysticks.
Figure 18.Joystick type interface.
The controller measures the voltages V (x V ,y V ,z V )from each joystick which denote the tilt of the joysticks.Then,the target wrist position is determined as below:
W re f =W +C 5V ,
(22)
where C 5is the arbitrary constant.If the voltage which denotes the right-and-left tilt of the right joystick exceeds a threshold,the control mode is shifted to the semi-automatic mode.
Operations of the trackball type interface and the joystick type interface in x and y directions similarly corresponds to a wrist motion in x and y,respectively,so that a user could control positions of the wrist joint intuitively through less training iterations.
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Figure 19.BEP type interface
2.4.4.BEP based interface
The Bio-Electric-Potential (BEP)based interface is devel-oped.BEP signals are often used as an instinctive user interface for assistive devices.There have previously been researches about motion discrimination of upper limb and control of assistive arm by BEP.[14]However,in case where a patient with muscle weakness operates assistive devices using only BEP signals,the control of three-dimensional position of the arm is of great difficulty because it requires a model to decode arm motion from BEP signals of multiple muscles.In many cases,BEP signals of the patient are too weak to use for a user interface.However,in some cases,BEPwith large signal-to-noise ratio may be measured from part of the muscles.In this research,we propose a BEP based interface intended for the patient whose del-toid and biceps can give off BEP signals with relatively large S/N ratio.A user is supposed to have capability to control the anteroposterior position of the wrist joint with this BEP based interface.When he/she controls the three-dimensional position by using BEP signals,the trackball type or joystick type interface is used as a supplementary interface.The sheet shown in Figure 19is consisting of matrix of six active electrodes.Again of the active electrode is 10,000.
One of the sheets is attached to the skin surface above deltoid,and the other is above biceps.Although strength of the BEP signals vary depending on the position of the electrodes,positioning of the sensor sheet does not require high accuracy because of the grid-like arranged multiple electrodes.For the control of EMAS II,the controller mea-sures the BEP signals and integrates the signals (IBEP).Let ch (ch =1...12)denote the channel number of electrode and T (=300[ms ])denote an integration time,and then the IBEP is defined by
I B E P ch (t )=
t
t −T
|B E P ch (τ)|d τ.(ch =1...12).(23)Table 5.Relationship between motion and magnitude of IBEP.
I B E P b sum <T H b
I B E P b sum >T H b
I B E P d sum <T H d
Stop
Back I B E P d sum >T H d
Forward
Back
Figure 20.Knobs for threshold adjustment.
Next,the sum of all channels of deltoid,I B E P d sum
,and that of all channels of biceps,I B E P b sum
,are calculated.They are compared with threshold T H d and T H b ,respectably.Finally,the arm motion is determined according to the mag-nitude relation of each value and the threshold as shown in Table 5.The thresholds are adjustable according to the strength of the BEP signals of each user using two knobs (Figure 20).The thresholds and the sum of current bioelec-tric potential are displayed on a monitor shown in Figure 21.The direction of the motion is selected by the above approach,however,the moving velocity is constant with
the value of I B E P d sum and I B E P b sum
.3.Experiments
3.1.Position accuracy
3.1.1.Experimental settings
In order to evaluate the position accuracy of EMAS II,the wrist position is measured using the motion capture system.During this experiment,two bottles filled up with one liter of water were loaded to the EMAS II in order to mimic weight of user’s arm (Figure 22).The EMAS II repeated reciprocation of the wrist part between table and mouth for 10times.As the index,the position error of the wrist joint is considered.Let (x enc ,y enc ,z enc )denote the wrist position calculated from the motor encoders,(x cap ,y cap ,z cap )de-note the wrist position calculated using data from the motion capture system,then the E enc and E cap were defined as follows:
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Figure 21.Display for BEP:From ch.1to ch.6:IBEPs of deltoid.From ch.7to ch.12:IBEPs of biceps.Heavy line of D is sum of IBEPs of deltoid.Thin line of D is the thresholds,T H d .Heavy line of B is sum of IBEPs of biceps.Thin line of B is the thresholds,T H b
.
Figure 22.The EMAS II with bottles.Two bottles work as weights of user’s upper limb.
E enc = (x ref −x enc )2+(y ref −y enc )2+(z ref −z enc )2,
(24)
E cap =
(x ref −x cap )2+(y ref −y cap )2+(z ref −z cap )2.
(25)
3.1.2.Measurement of position accuracy
Figure 23shows the three kinds of wrist joint trajectories,reference wrist position,the actual wrist position
calculated
Figure 23.Wrist joint position.
from the encoders of the motors,and the actual wrist po-sition measured by the motion capture system.The wrist position data from the motion capture system were offset so that their mean values correspond to each other because the coordinate origins of the EMAS II and the motion cap-ture system were different.Rapid change of the reference position can be found at intervals,but that comes from the change of the control mode and is not a problem.Figure 24shows that the wrist joint positioning error E cap was 39.0[mm]at most,the mean of E cap was 17.6[mm],and standard deviation of E cap was 11.0[mm].Meanwhile,the error E enc was 39.6[mm]at most,the mean of E enc was
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Figure 24.Position error of the wrist joint.
Table 6.Foods for the experiment.Curry 130[g]Rice 200[g]Potato salad
120[g]
Yoghurt
70[g]
Water
200[ml]
16.6[mm],and standard deviation of E enc was 13.0[mm].The sources of errors were steady-state error of the PD control and the effect of friction and expansion of the cables.However,it will be little problem to eat a meal because the user can adjust the position by manual control mode.3.2.Meal assistance experiment with healthy person 3.2.1.Experimental settings
A healthy subject regarded as a muscular dystrophy patient ate a meal with the EMAS II.List of foods for an experiment was shown in Table 6
.
Figure 25.Each action of eating.
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