[Skolkovo Robotics 2015 Day 1] Терашима К. Modeling and Taylormade Training Method Using...

System and Control Lab., Toyohashi University of Technology System and Control Lab., Toyohashi University of Technology

Modeling and Taylormade Training Method Using Neural Network for Specific Muscle of the Upper Limb

2015/3/19

Kazuhiko Terashima

Toyohashi University of Technology , Japan

DzmitryTsetserukou’s round table “Smart Robotic Interfaces:

Designing the Future of Human»

System and Control Lab., Toyohashi University of Technology

Contents 1.Introduction

2.Experimental Construction of Training

System

3.Muscular Model using Neural Network

4.Optimal Motion Path Design for

Training Specified Muscle

5.Experimental Results

6.Discussion

7.Rehabilitation using grasping support by

low freedom of degrees

8.Conclusion

1


1.Intoroduction

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「TEM LX2 TypeD 」:Yaskawa

「Reo go」 : motorika

A lot of research is being done on rehabilitation robotics that is pertinent to strength training. Lum et al. indicated that, compared with conventional therapy techniques, robot-assisted training is more efficient for improving muscle strength and path-

following capability. Past research aims to regain a muscle strength of an entire arm or leg. Therefore, the robots are unable to apply a load to

specific muscles. However, the degree of muscle weakness differs according to each muscle. Thus, the application of a load to specific muscles that require strengthening is expected to lead to more efficient and safer

training

A lot of research is being done on rehabilitation robotics that is pertinent to strength training. Lum et al. indicated that, compared with conventional therapy techniques, robot-assisted training is more efficient for improving muscle strength and path-following capability. Past research aims to regain a muscle strength of an entire arm or leg. However, the degree of muscle weakness differs according to each muscle. Application of a load to specific muscles that require strengthening is expected to lead to more efficient and safer training.


1.Introduction(author’s research)

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In this paper, the effectiveness of optimization method using neural network model is presented, and validity of the proposed method is demonstrated by comparing with FEM Model.

Muscle Strength Estimation Using Musculo-Skeletal Model proposed by Kumamoto et al. in which upper limbs are simplified into six Functionally different Effective Muscular strengths (FEM) T. Okada、K.Terashima et al , Journal of Robotics and Mechatronics,vol. 20,

no. 6, pp. 863–871, 2008.

This approach does not consider the coordinated motion of an antagonistic muscle due to simplification of model.

Motion Path Design for Specific Muscle Training Using Neural Network,” K. Itokazu、K.Terashima et al, Journal of Robotics, Vol. 2013,Article ID

810909, 2013.

The validity of the neural network model by comparing with our previous musculo – skeletal model has not been clarified.


3.従来研究における筋活動レベル推定法

Output of FEM

Measuring the muscle activity and direction of the force at the end-point (W), exert a force in the direction of maximize the activity of target FEM and minimize the activity of other.

Isometric exercise at fixed point Isotonic exercise(trajctory)

e1 e3 e2

f3 f2 f1

‘Revolution in Humanoid Robotics’ Minayori Kumamoto Tokyo Denki University Press

FEM（Functionally different Effective Muscular strengths) model

The objective of the research was to strengthen a muscle by isometric exercise.

4


5

2-Link arm

Monitoring system

The position of end point

Range of motion of the arm

The motion path

EMG signal

・the motion paths , force , position of the end point are displayed on monitor.

・The displayed force and end point position are updated in a real time.

2.実験装置概要

Subject : Training of upper limbs by horizontal motion

2.Experimental Construction of Training System

During the experiment

Equation of motion (End-point)

http://maru.bonyari.jp/texclip/texclip.php?s=/beginalign*a =/left( /beginarraylca_/textrmx//a_/textrmy/endarray/right)=/left( /beginarraylc/fracf_x-D/cdot v_xm///fracf_y-D/cdot v_ym/endarray/right)/endalign*

http://maru.bonyari.jp/texclip/texclip.php?s=/beginalign*v_(t+1)=a/cdot dt+v_(t)/endalign*


実験風景 2 . E x p e r i m e n t a l C o n s t r u c t i o n o f Tr a i n i n g S y s t e m

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4.ニューラルネットワーク

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The EMG sensor are attached to the subject of muscle. During experiment EMG signal of all subject is measured.

Subject of muscle

2.Experimental Construction of Training System

Back side | front side

FEM function FEM function

f1 Flexion of

shoulder

e1 Extension of

shoulder

f2 Flexion of elbow e2 Extension of elbow

f3 Flexion of

shoulder and

elbow

e3 Extension of

shoulder and elbow

e2≅e3

e2 e3 e1

f 1

f 2 f 3

Maximum muscle force is proportional to maximum muscle cross section.

f ：0.72［N/mm2］，e：0.65［N/mm2］

2.Experimental Construction of Training System －MRI Image Analysis－

Measurement of maximum muscle force, link length, joint radius

（Shoulder position） Upper lim position

r

l


INPUT T1, T2：Joint torque θ1,θ2：Joint angle

OUTPUT 𝛼 1~5：the muscle activation the number of unit：5

Three – layer Artificial Neural Network

Sigmoid

function


An input layer

A hidden layer

An output layer The number of

hidden units：10

9

F


3.Muscular Model using Neural Network - Units number of hidden layer in Neural network


0.25 0.2 0.15 0.1 0.05 0 0.05 0.1 0.15 0.2 0.250.25

0.2

0.15

0.1

0.05

0

0.05

0.1

0.15

0.2

0.25

x [m]

0.25 0.2 0.15 0.1 0.05 0 0.05 0.1 0.15 0.2 0.250.25

0.2

0.15

0.1

0.05

0

0.05

0.1

0.15

Xaxis [m]

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The training data sets are obtained through exploratory experiment for each subject.

The ANN is trained by a backpropagation algorithm.

To evaluate neural network after learning , the motion path different from learning case as shown in ‘motion paths for verification’ is used.

Motion paths for exploratory experiments

Motion paths for verification

Training of Neural Network




Learning data 0.04048

Evaluation data 0.04496

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Estimation result (M1)

RSME(M1~M5)

Good estimation results are obtained

3.Muscular Model using Neural Network Training of Neural Network

Conjugate gradient ;Conjugate gradient method gradient


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3.Muscular Model using Neural Network Training of Neural Network

Estimation result (M2) Estimation result (M3)



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Training of Neural Network

Estimation result (M4) Estimation result (M5)

System and Control Lab., Toyohashi University of Technology 15/14

Design by using multiobjective optimization

Maximize the activity of target muscle

Minimize the activity of other muscles &

Algorithm

・Algorithm for designing a motion path that has fixed initial position

4. Optimal Motion Path Design for Training Specified Muscle

Initial position θ1 = π/4 [rad] θ2 = π/2 [rad]

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Neural Network

multi objective optimization method

Pose Computation

i:1~5, j:1~10

w：weight ,b：biases

x：inputs

sigmoid：Sigmoid function

α：the level of muscle activation

T1,T2：Shoulder & elbow torque

θ1,θ2：Joint angle

F: Force of end-point

X,Y：Position of end-point

αi

θ

T1, T2, θ1, θ2

2-Link model

4.Optimal Motion Path Planning

θ:Direction of force at end-point （Quadratic programming)

αn(θ)：Level of muscle activation of target muscle

α(θ), σ2(θ):Average and variance of other muscles


One example of motion path 17

・𝑃𝑖(𝑀𝑖) shows the best path such as maximizing 𝑀𝑖 and minimizing other

𝑀𝑗 (j=1,…,5, j ≠ i)

These paths depend on the function of each muscles , and on the each person.

4.Optimal Motion Path Planning


Objective of the experiment

1. In order to check the effectiveness of the proposed method

Subject： 3persons [healthy person]

The paths of all subject is respectively designed by using ANN trained , and differs every person.

3 subjects have respectively three path for each subject.

Numbers of trials are three for each path.

outline

Body dimension 18

5.Experimental Results (Comparison of proposed method and previous method)


Proposed method has achieved ,

1. Increasing the activity of the target muscle

2. Controling the activity of the non-targeted muscle 19

Experimental result (target: M2) Experimental result (target: M1)

5.Experimental Results (Comparison of proposed method and previous method)


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Proposed method achieves better results in the muscle (M1~M4 one-joint muscle) that was obtained effect

5.Experimental Results RESULTS

Experimental result (target: M3)




Subject： 10 persons [healthy person]

age：21~24 , height：161~179[cm] , weight：48~94[kg]

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Objective of the experiment

outline

1.To evaluate an influence exerted by personal experimental data.

5.Experimental Results (Comparison between personalized and non-personalized results)

The paths of all subject is respectively designed by using ANN trained , and differs every person.

The ten subjects have respectively nine non-personalized path for each subject.

Numbers of trials are three for each path.


Level of muscle activation (Sub. A)

Own experimental data has achieved ,

1. Increasing the activity of the target muscle

2. Controling the activity of the non-targeted muscle

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Own experimental data achieves better than

the non – personalized path.

5.Experimental Results (Comparison between personalized and non-personalized results)


6. Discussion - Comparison of Proposed Method and FEM method

proposed method (NN model /an example)

previous method (Six Functionally different

Effective Muscular strengths ) 23

m o t i o n p a t h


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6.Discussion Manual Muscle Test(MMT) (Expert Theraist)

参)‘徒手筋力検査法改訂第9‘DANIELS&WORTHINGHAM協同医書出版社

Pectoralis Major(m1)

1. Patient sits with shoulder in neutral and supported, shoulder is 90 degrees abducted, elbow flexed 90 degrees. Arm on table.

2. Bring arm across chest in horizontal and downward direction

Latissimus Dorsi(m2) 1. Patient lies prone with shoulder flexed and IR

over edge of table. Arm on table.

2. Extend shoulder, allowing elbow to flex (start in extended position)

Brachioradialis(m3)・Biceps Brachii(m5)

1. Subject is sitting with arm supported in 90 degrees of abduction; shoulder is neutral, elbow fully extended, and forearm supinated , Arm on table.

2. Flex elbow to 90 degrees from extended position by sliding across table.

Triceps Brachii(m4)

1. Sitting, arm on table. Lateral & posterior surfaces of proximal one half of body of humerus & lateral intermuscular septum

2. Extends the elbow joint, with the long head assisting in adduction and extension of the shoulder joint

ＭＭＴ m o t i o n p a t h


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Proposed method (NN model /an example)

MMT method

6.Discussion Comparison of Proposed Method and MMT Method


On-Going Problem

1. Improvements of NN model by using optimal path data.

2. Adequate impedance for training by person’s muscle condition and adequate guidance speed in monitor.

3. By clastering of some characteristics of human such as arm length, arm thickness, and hand force at tip in two link arm, Neural network should be prepared beforehand. Because neural network learning takes 15 minutes to idetify model.

4. Extention to 3D upper Lim and Lower Limb.

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To introduce rehabilitation robot in home, small size and low cost with flexible motion is developed in this research by himself or easy assist of family.

Present: Complicated motion is possible, but Machine is big and high cost. ⇒ To use in home, rehabilitation robot of small size and low cost with flexible motion is highly demanded.

7. Rehabilitation using grasping support by low freedom of degrees

Tanabess bider

Power assist hand

Finger rehabilitation systems

Simple motion by switch on-off.

High flexibility , but High cost,

and Large scale

Assist for finger ‘s extention

Complicated motion,

Small size and low

cost

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Design Concept

Restriction to important motion in daily life.

Three joint of a finger is usually moved together.

Grasp and Release、 Picking Independent motion of

Thumb Abbreviated motion

Each joint can not be controlled independently.

Reduction of number of actuator, but high degrees of freedom

Required Motion

Limitation of degrees of freedom for one finger

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Developed Device

Winding force : 19.2N Weight : 224ｇ

First finger ～little finger: Motor 1, Thumb: Motor 1,

Winding trunk: Motor 1 ⇒ Total 3 motors.

40ｍｍ

Bending

direction by gum

Extention direction by

Winding force

Winding trunk

Movable guide

Fixed guide

Feeding

screw

Length of wire can be changed by making movable guide move

towards left and right direction.

Various finger shape can be reapearanced by 3

motors. example: grasping motion, Peace sign , etc.

Change of wire length by movable guide

Time[s]

指先角度

[deg

ree]

Response for sine wave.[0.25Hz] at position 5mm.

ガイド間距離

[mm

]

Position by movable guide[mm]

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Rehabilitation Method

Following to healthy hand

・rehabilitation according impaired person’s

wishes

→ Mirror therapy

System construction

Monitor

ｌ

Amp.

Healthy hand

Impaired hand

motion capture

LEAP MOTION

Counter

PC

D/A

Impaired hand is followed to motion

obtained by healthy hand from motion

captured.

Intuitive operation for ideal rehabilitation

instead of complicated programming

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Bending and stretching exercise Twisting motion

Picking motion Peace sign

Rehabilitation application

Following for many hand posture could be controlled.

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Monitor

ｌ

D/A

Amp.

Impaired hand

motion capture

LEAP MOTION

Counter

PC

Real ball

Virtual ball

Camera

Image recognition

Rehabilitation Program

When hand approaching to objects,

Palm opens. When hand touches

Objects, Hnad grasp and transfers.

By using actual objects, repulsive force

and sensatinal touch can be feeled

compared with virtual objects.

Monitor

ｌ

D/A

Amp.

Impaired hand

motion capture

LEAP MOTION

Counter

PC

Real ball

Virtual ball

Camera

Image recognition

Real ball

Healthy hand

Grasping assist Grasping by Mirroring


1． Neural Network has been used to estimation of muscle activity.

2．The motion paths has been achieved by using a multi objective optimization method for the obtained Neural network

3． Comparison of proposed Neural network and previous musculo-skeletal(FEM) model on the efficiency of specific muscle training

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Own experimental data has achieved better than the non- personalized path.

Proposed method has achieved ,Proposed method achieves better results in the muscle (M1~M4 one-joint muscle) that was obtained effect

4． Comparison between personalized and non-personalized results

8.Conclusion

5. Rehabilitation using grasping support of low freedom of degrees

555

Professor:

Toyohashi university of Technology（TUT), Vice -president

・System and control engineering

laboratory

Tel: 0532-44-6699 Email: [email protected]

Thank you for your kind attention

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