biomechaytronic

8/2/2019 biomechaytronic

1/6

AbstractThe development of a biomechatronic hand with

multi-FSRs (Force Sensitive Resistor) interface is presented.

The cambered palm is specially designed to enhance the stabilitywhile grasping. The opposite thumb could grasp along a cone

surface, while maintaining its function. By taken the advantage

of coupling linkage mechanism, each finger with three

phalanges could fulfill flexion and extension independently.

Besides, each finger is equipped with torque and position

sensors. Thus, the cosmetics and dexterity are improved

remarkably compared to commercial prosthesis. The hardware

architecture is hierarchically organized as control system and

human-machine interaction system. To evaluate the

performances of the hand, the pattern reorganization

experiment based on support vector machine (SVM) was

conducted. The results show that FSR sensing technology is

suitable for muscle activity detection; Compared to nearest

neighborhood classifier, SVM method improves the predictsuccess rate. Moreover, it also demonstrates that the hand is

dexterous and manipulates like human.

I. INTRODUCTIONROSTHETIC hand as a branch of humanoid hand

research is widely addressed in the field of rehabilitation

technologies. Commercial prostheses usually have simple

structure and rarely no sensory system. These limitations

deeply affect the functionality of hand. Due to the drawbacks

of commercial prostheses, many experimental prostheses

with more degree of freedoms (DOFs) and sensors have been

developed, such as FZK Hand [1], RTRII Hand [2], IOWA

hand [3], and Smarthand [4]. But these hands are still faraway from being accepted as part of the body by the

amputees.

Despite of several research effort aimed at innovating

humanoid hand technology, surveys on the relevant

functionality of the hand prosthesis with regard to daily use

activities show that the main concerns of the amputees are

aesthetics (62%), excessive weight (58%), lack of functional

capabilities (50%); further more, the opinions from

rehabilitation professionals reveal that tactile feedback is

preferred largely [5].

Due to the current status, it is particularly urgent to develop

a highly integrated prosthetic device that meets therequirements listed above. Meanwhile, there is an increased

Manuscript received April 5, 2010. This work was supported by the

National Natural Science Foundation of China under Grant 50435040, and

the National High Technology Research and Development Program of China

under Grant 2008AA04Z203, 2009AA043803.

Xinqing Wang, Jingdong Zhao, Dapeng Yang, Nan Li, and Chao Sun are

with State Key Laboratory of Robotics and System, Harbin Institute of

Technology, Harbin 150001, China. (e-mail: [email protected])

Hong Liu is with the Institute of Robotics and Mechatronics, German

Aerospace Center, Munich 82230, Germany. (e-mail: Hong. [email protected])

demand from intelligent control; traditional sEMG control

methods can acquire a higher success rate over prosthetic

hand control [6], [7]. However the surface electrodes requirevery good contact with human skin to keep D/C voltage

potential low [8], and the price of the electrodes is expensive.

Considering that human activities such as grasping can be

detected by contractions of forearm muscles, we propose to

use simple, low cost FSR sensors to capture the pressure of

muscles as source of control, the intention of human could be

realized through pattern reorganization method [9]. In order

to improve prehensile posture and success rate, advanced

machine learning algorithm is required, such as SVM which

is a useful technique for data classification.

The present work of this project focuses on the

biomechatronic approach to develop a human-like prosthesis.The designed hand is a light weight and simple constructed

multi-DOF hand and can accomplish motions such as nip and

grasp. The proprioceptive sensory system and control unit are

embedded in mechanical structure. Besides, the hand can be

controlled by pattern reorganization method on the basis of

multi-FSR sensors in the real-time. Its function and

appearance are very close to the human hand.

II. MECHANICAL STRUCTUREThe comparison between the prosthetic hand and human

hand is shown in Fig. 1.

The hand is 79mm wide, 159mm long and 21mm thick

(when in full outspread), weighs about 420g including circuit

boards. It is about 90% to a human hand out of consideration

for inconspicuous. The hand is composed of five active

fingers. Each finger is actuated by a DC motor independently.

By taking the advantage of coupling mechanism, each fingeris capable of rotate around the metacarpophalangeal (MCP),

proximal interphalangeal (PIP), and distal interphalangeal

(DIP) joints for a total of 15 movable joints of the entire hand.

The specifications of each finger are shown in Table I.

Compared with those of fixed shape fingers; this kind of

design enhances the functionality of the hand and mimics the

motion of human finger.

The sphere grasp range is 50-90mm, the cylinder grasp

range is 15-90mm, and these were designed to fulfill daily

Biomechatronic Approach to a Multi-Fingered Hand Prosthesis

Xinqing Wang, Jingdong Zhao, Dapeng Yang, Nan Li, Chao Sun, Hong Liu

P

Fig. 1. Comparison between the prosthetic hand and human hand

Proceedings of the 2010 3rd IEEE RAS & EMBSInternational Conference on Biomedical Robotics and Biomechatronics,The University of Tokyo, Tokyo, Japan, September 26-29, 2010

978-1-4244-7709-8/10/$26.00 2010 IEEE 209


2/6

manipulation requirements. The hand could also complete

other grasp modes, such as precision grasp and hook

prehension. The research ideology is described in detail in the

following sections.

A. Configuration of PalmAccording to traditional anatomical definitions, human

palm is comprised of three arches providing the necessary

stability and mobility in the hand [10], as shown in Fig. 2.

The cambered palm is designed which is far different from

that of plain ones. The placement of each finger is shown in

Fig. 3. The MCP joint of middle finger is the reference both in

vertical and horizontal position. The index, the ring, and the

little finger have rotary angles of 3, 3, and 6 around the

axis of middle finger to realize the distal transverse arch. Thesimulation experiments show that there is better envelope

space because of the arches.

The opposability of the thumb is very important for

dexterity and grasping stability. The oppose angle between

the thumb and the middle finger is defined as . It is designed

to ensure the minimum distance larger than 95mm. By means

of the anti-trigonometric function, the value of can be

calculated as 69.

A performance index [11] of the thumb opposability is

defined by (1)

31

1 k

i iiJ w vd =

=

(1)

Where iv denotes the volume of intersection between themobility space of the thumb and that of the i-th finger, each

iv is the function of , and can be obtained by using theMechanica module of Pro-E. kdenotes the number of the

finger except the thumb, dis the length of the thumb, andiw is a weighting coefficient, simply, each iw equals to 1.00.

The biggerJ becomes, the stronger the hand performs.

According to the simulation results, the value ofJ reaches

maximum, when is 28.6.

B. Multi-Sensory Integrated Design of FingersCommercial prosthetic hands usually use fixed shape

fingers, which may bring two limitations: Firstly, the motion

of the finger is unnatural; secondly, the sensory information is

poor. In order to overcome these disadvantages, the

physiology of human finger needs to be studied.

The multi-functionality of the prosthetic hand depends on

the actuators performance. We adopted a DC motor to drive

the finger because it is more balanced in the efficiency,

response time, and reliability. The actuation unit consists of a

DC motor, a planetary gearheads and a magnetic encoder.

A tendon drive mechanism was first being considered to

actuate the finger, but it was abandoned due to small grasping

forces. Thus, the coupling linkage mechanism is applied to

transmit the power. The linkage mechanism has 9 bars, as

shown in Fig. 4.

Where 1l is the driving bar, 2l is the seat, 3l is the drivenbar, 4l is the transmission bar. 4l is driven by 1l through

3l during grasp. Thus a 4-bar loop ADBC has beenestablished. The finger mechanism consists of two groups of

planar four bar linkage mechanism; D and G are the

intersection points.

Compared to the underactuated linkage mechanism [12], it

does not contain elastic parts which are a consumption of

battery energy. The motion is transmitted from the motor unit

to the coupling linkage mechanism by means of bevel gear. In

Fig. 2. Three arches of human hand

Fig. 4. Schematic structure of index finger

Fig. 3. Structure of the hand

Parameters Thumb Index finger

Middle finger

Ring finger

Little finger

Operating angle [-5 40] [0 87] [0 87]

Angular velocity 68 (/s) 68 (/s) 118 (/s)Output power 1.5 (W) 1.5 (W) 0.75 (W)

Fingertip force 10 (N) 10 (N) 4.3 (N)

TABLEI

SPECIFICATIONS OF EACH FINGER

210


3/6

order to better imitate the shape of each finger in relax, an

initial inflexed angel of 15 was adopted between proximal

phalanx and medial phalanx.

The hand needs as a minimum a set of force and position

sensors to enable intelligent control schemes. The

configuration of sensors and disposal circuits are shown in

Fig. 5. As the finger is compact, the signal transfers among

the disposal boards through flex-PCB to avoid enlace.

The motor encoder is applied to measure the relative angle

of the motor. A contact-less joint position sensor is applied to

measure the position of MCP joint. It can provide more

accurate information of joint position to decrease the

influence of elasticity and hysteresis of the transmission

system. Its measurement principle can be seen in [13]. The

torque sensor located in base joint is embedded in the driving

bar and can precisely measure the external torque. The

measurement principle is based on the induced mechanical

strains on elastomers. Shapes and dimensions of elastomers

are determined by utilizing ANSYS FEM software. The

analog signals were converted to digital by Wheatstone

bridge; the processing circuit is shown in Fig. 6.

SG1 and SG2 are strain gauges; the signals obtained are

transformed into two channel voltage outputs though the half

bridge circuit. The difference of the outputs indicates the

strain of the elastomers. As the amplitude is small, the

precision amplifieris adopted to amplify the signal; the gain(G) of the amplifier is decided by (2). The output of the

amplifier is then sent to the A/D converter. The sampling time

of A/D is 500Hz.

5

6 7

2

(1/ 1 )/

RG

R R

+= (2)

C. Characteristics of ThumbThe humans thumb usually inclines to the palm when

relaxed and grasp along a cone surface, which is far different

from that of most previous prosthetic hand [14], [15].

Based on the position of thumb mentioned above, the

geometry is specially designed to mimic the motion of human

thumb when grasp. The metacarpal of the thumb is placed at

30 deflexion to the base joint of the middle finger. In

addition, there is an initial abduction of 30 to the palm, as

shown in Fig. 3. By doing so, the thumb of the hand can grasp

along a cone surface which is similar to human hand.

In order to realize such an effect, an RSRRR spatial linkage

mechanism was designed, as shown in Fig. 7. It evolves from

the RSSR mechanism. The RSSR mechanism has two DOFs

which means motion uncertainty with only one actuator. Thus,

one spherical joint is replaced by two revolute joints to

eliminate one extra DOF. By doing so, the planar finger

motion is extended to three dimensional spaces and only one

motor is needed to drive the thumb. The last two joints are

coupled as other four fingers. The thumb, 85mm long, 17mm

wide and 14mm thick, weighs about 80g.

D. Packaging of the HandThe skin-like glove fulfils the patients emotional needs

and provides protection from dust and corrosion. Besides, this

silicone polymer gives the necessary friction which is useful

in sliding contact [16].

Two simple gloves with different thickness have been

manufactured by utilizing fast prototype technology.

According to our experiment, the wrinkle is unnatural and

doesnt fit the hand well. Besides, there is interference

between the hand and the glove, as shown in Fig. 8. Due to

the impenetrable of the finger, there will be resisting force

which may lead to unstable grasp and cost additional energy.

Basically, with the 2mm thick silicone polymer glove, there isup to 20% motor energy wasted due to the tensile force of the

glove. So it is important to optimize the shape and dimension

of the glove.

We propose to make the wrinkle as a camber. Because of

the non-uniformity and the anisotropy of the material, it is

hard to calculate the radius of the camber. According to

bending theory, the position of neutrosphere is moving

according to the curly angle, the radius of the neutrosphere

is defined as (3)

Fig. 6. Torque sensor disposal circuit

Fig. 5. Sensory configurations of the index finger

Fig. 7. Simple view of the thumb

211


4/6

r x = + (3)Where ris the radius of the wrinkle;x is the coefficient, and is

decided by rand , is the thickness of the glove. Thus the

length of the neutrosphere abl is given by (4).

180/abl = (4)Thus the shape and dimension of the camber is decided, as

shown in Fig. 8.

Based on the idea mentioned above, the improved glove

was made, as shown in Fig. 9. Compared with the previous

ones, the resistance has a decrease of 8%. Besides, an

additional advantage is that the noise of the hand could be

lowered by the glove to a certain extent.

III. HARDWARE ARCHITECTUREThe currently developed prosthetic hand is controlled by

multi-processor controller based on digital signal processor

(DSP). The architecture of the control system is shown in Fig.

10. The control system of the prosthetic hand is hierarchically

organized: the human-machine interface allows the prosthesis

to communicate with human in an alternate way. Namely:

record muscle contraction signals through multi-FSR sensors

and realize contact force feedback to human through

electrical stimulator; a high-level controller is in charge of

implementing FSR signal processing and pattern recognition.

The lower-level controller is designed to coordinate the

movements of the fingers and accomplish grasp.

A. Motion Control BoardThe motion control board is embedded in the prosthetic

hand, and its main function is to actuate and control finger

motors, acquire and process the signals from torque &

position sensors and motor encoders. The core of motor

control is realized by TMS320F2810. It has 6 channel

independent pulse width modulation (PWM) outputs. In

addition, it has 16-channel 12 bit A/D converters to realize

signal sampling from sensors and motor encoders. The

direction of motor is discriminated through complex

programming logic device (CPLD). The controller area

network (CAN) module is applied to communicate with FSR

signal processing board. The control cycle can reach to

800 s without any code optimization.

B. FSR Signal Processing BoardThe hand is controlled by muscle contraction signals in

real-time. In order to realize the real-time control scheme,

TMS320F2812 of TI is used in the signal processing board. It

is a new 32 bit DSP executing 150 MIPS (million instructions

per second). The muscle contraction signals obtained by

multi-FSR sensors are transferred through serial peripheral

interface (SPI) to the signal processing board. The signals are

then classified by SVM in real-time. The outputs of the

classifier then send to the motion control board as command.

IV. PERFORMANCE OF THE HANDTo evaluate the performances of the control system and

finger mechanism, the pattern recognition control was

conducted. Pattern reorganization is the most commonly used

bionic control method. Research on human han

ds grasp function [17] indicated that, the thumb, index and

middle finger play a relatively important role than the othersin daily life. For the convenience of classification, we

configure the thumb, the index and the rest fingers into one

DOF each. The state of finger was classified at first, that is,

1 means the finger motion to the extend position, -1

means the motion to flex position and 0 means motion to

middle position. Every hand gesture corresponds to one mode.

We take 9 basic modes for pattern reorganization, as shown in

Fig. 11.

Fig. 10. Hardware architecture of control system

Fig. 8. Wrinkle of the finger

Fig. 9. Simple view of the artificial skin

212


5/6

A. Experiment SetupControl signals are recorded from 5 muscles of the

operators forearm through 10 FSR-149NS [18] sensors. The

muscles are musculus flexor carpi ulnaris, extensor carpi

ulnaris muscle, extensor digitorum muscle, flexible flexor

carpi, musculus palmaris longus. The force sensitivity of

FSR-149NS is optimized for use in human touch control ofelectronic devices. Typically, the single part repeatability

tolerance held during measuring ranges from 2% to 5% of

an established nominal resistance. The number and placement

of the FSR sensors seriously affects the recognition of gesture

patterns. So, the quantity and array of FSR sensors are

optimized combining correlation coefficient analysis and

principal component analysis.

Fig. 12(a) shows the displacement of the sensors before

optimization. The sensors with a crossing in the shape of an X

are disposed according to the optimization result, as shown in

Fig. 12(b).

The FSR signals were classified by SVM. SVM algorithms

separate the training data in feature space by a hyperplane

defined by the type of kernel function used. Thus, the

selection of an appropriate kernel function plays an important

role. Generally, the kernel function is chosen if it fitsparticular data better than any other kernel. The Radial Basis

Function (RBF) kernel is chosen at first [19], maybe it is not

the best one. The kernel function is given by (5)2

( , ) exp( )i j i jK x x x x= (5)

Where is the kernel parameter, 0 >

By taken the advantage of Lagrange optimal method, the

optimal hyperplane can be transformed into dual problem.

Hence, the indicator function is now given by (6)

1

( ) ( , )l

i i i

i

f x sgn y K x x b=

= +

(6)

Where i are Lagrange multipliers, the upper bound is C, forthose i >0 are called support vectors; , i arevectors,

iy are labels, might be -1 or 1; b is the thresholdWe use one-against-one method to solve multi-modes'

classification. For the regression operation, we useepsilon-SVR method. For the optimization of the penalty

parameterCand kernel function coif gamma, we will make a

grid research combined with cross validations. The

experiment devices are shown in Fig. 13.

We take 50 samples for every mode. The sampling time of

PCI card is 8s; the procedure is relax-contract-hold-relax by

following the instruction of the DSP board indicator. The

hold condition must take up 50% of the sampling time or

more. After the training progress, every mode must repeat 20

times to calculate the success rate.

B. Experiment ResultsCompared to nearest neighborhood classifier, SVM

method improves the predict success rate, the recognition of 9

gesture patterns can be achieved at 75%, as shown in Fig. 14.

The decision frequency of current hand gesture can reach

to 40Hz in 9 modes, which is adequate for real time control of

the hand. The success rate is a bit lower than traditional

sEMG control method; this may be improved by carefully

choosing the kernel function of SVM, or by taking the

quantum behaved particle swarm optimization (QPSO)

algorithm.

The multi-FSR control scheme can be adopted in the

control of multi-DOF prosthetic hands and other life-like

artificial appendages, such as limbs, legs, and foot.

Fig. 14. Pattern recognition experiment result

Fig. 11. Hand gesture

Fig. 13. Experiment devices

Fig. 12. Before and after the optimization of sensors

213


6/6

Fig. 15 displays that the prosthetic hand performs pinch,

three jaw chawk, cylinder grasp, etc. respectively. It shows

that the hand is able to grasping objects stably.

V. CONCLUSIONS AND FUTURE WORKThe concept of the novel anthropomorphic prosthetic hand

and an intuitive control scheme via forearm muscle

contraction signals are presented. This approach can be

synthesized by the term biomechatronic design. The hand

has cambered palm and five active fingers. Each finger is

integrated with position and torque sensors. This feature

offers the hand more grasping patterns and complex control

methods. On the other hand, by carefully configuring the

human hand gestures, 9 patterns of grasp modes can be

recognized with 10 FSR sensors. There may be some

prosthetic hands that have more DOFs or more sensors, but

considering the small size, low weight, human-like cosmetics,

biomimetic controllability and perception in all; the hand that

we developed is more capable of being an intimate extension

of human body. In fact, prosthetic hands as substitute for

human hand cannot be achieved by existing artificial

technologies.

The prosthetic hand can be applied in two aspects. Firstly,

it can be treated as an end operator of a multi-DOF robot arm

to interact with the environment. Second, with a human likeglove, it can be utilized for the prosthetic uses.

Furthermore, more work should be done to study

intelligent force control methods which can be applied for

regulating the grasp force. Besides, the bio-feedback control

strategy is being researched, which will communicate a sense

of touch back to the user. These features enable the hand to

respond seamlessly to the intent of human user.

REFERENCES

[1] C. Pylatiuk, S. Mounier, A. Kargov, S. Schulz, G. Bretthauer, Progressin the development of a multi-functional hand prosthesis, inProc. 26th

IEEE Annu. Int. Conf. on Engineering in Medicine and Biology Society,

San Francisco, USA, 2004, pp. 4260-4263.[2] L. Zollo, S. Roccella, R. Tucci, et al. Biomechatronic design and

control of an anthropomorphic artificial hand for prosthetics hand

robotic applications,IEEE Trans. on Mechatronics, vol. 12, no. 4, pp.

418-429, Aug.2007.

[3] J. Z. Yang, E. P. Pitarch, K. Abdel-Malek, A. Patrick, L. Lindkvist, Amulti-fingered hand prosthesis,Mechanism and Machine Theory, vol.

39, no. 6, pp. 555-581, 2004.

[4] C. Cipriani, M. Controzzi, M. C. Carrozza, Progress towards thedevelopment of the smarthand transradial prosthesis, inProc. of 2009

IEEE Int. Conf. on Rehabilitation Robotics, Japan, 2009, pp. 82-687.

[5] J. L. Pons, E. Rocon, R. Ceres, et al. The MANUS-HAND dextrousrobotics upper limb prosthesis: mechanical and manipulation aspects,

Autonomous Robots, vol. 16, no. 2, pp.143-163, March 2004.

[6] S. Bitzer, P. Van Der Smagt, Learning EMG control of a robotic hand:towards active prostheses, in Proc. of the 2006 IEEE Int. Conf. on

Robotics and Automation, Orlando, 2006, pp. 2819-2823.

[7] C. Castellini, P. Van Der Smagt, Surface EMG in advanced handprosthetics,Biological Cybernetics, vol. 100, no. 1, pp. 35-47.

[8] O. Amft, H. Junker, P. Lukowicz, G. Troster, C. Schuster, Sensingmuscle activities with body-worn sensors, inProc. of Int. Workshop on

Wearable and Implantable Body Sensor Networks, vol. 2006, pp.

138-141.

[9] Y. Honda, S. Weber, T. C. Lueth, Intelligent recognition system forhand gesture, in Proc. of the 3rd Int. IEEE EMBS Conf. on Neural

Engineering, Hawall, USA, 2007, pp. 611-614.

[10] A. P. Sangole, M. F. Levin, Arches of the hand in reach to grasp,Journals of Biomechanics, vol. 41, pp. 829-837, 2008.

[11] M. Tetsuya, K. Haruhisa, Y. Keisuke, Anthropomorphic robot hand:Gifu hand III, ICCAS2002, pp. 1288-1293.

[12] B. Massa, S. Rosella, M. C. Carrizozo, P. Dario, Design anddevelopment of an underactuated prosthetic hand, inProc. of the 2002

IEEE Int. Conf. on Robotics and Automation, Washington. DC, 2002,

pp. 3374-3379.

[13] S. Schulz, C. Pylatiuk, G. Bretthauer, A new ultra lightanthropomorphic hand, in Proc. of the 2001 IEEE Int. Conf. on

Robotics and Automation, Seoul, Korea, May.2001, pp. 2437-2441.

[14] D. Rajiv, Y. Clement, L. Maurice, The design and development of agloveless endoskeleton prosthetic hand, Journal of Rehabilitation

Research and Development, vol. 35, no. 4, pp. 388-395, 1998.

[15] L. Biagiotti, F. Lotti, C. Melchiorri, G. Vassura, Mechatronic design ofinnovative fingers for anthropomorphic robot hands, in Proc. of the2003 IEEE Int. Conf. on Robotics and Automation , 2003, vol. 3, pp.

3187-3192.

[16] D. J. Montana, The condition for contact grasp stability, inProc. ofIEEE Int. Conf. on Robotics and Automation, 1991, vol. 1, pp. 412-417.

[17] S. Schimizu, M. Shimojo, S. Sato, et al. The relationship betweenhuman grip types and force distribution pattern in grasping, inProc. of

8th International Conf. on Advanced Robotics, 1997, pp. 299-304.

[18] Force sensing resistor integration guide and evaluation parts catalog.Interlink Electronics. [Online]. Available:

http://www.interlinkelectronics.com.

[19] S. S. Keerthi, C. J. Lin, Asymptotic behaviors of support vectormachines with Gaussian kernel, Neural Computation, vol. 15, no. 7,

pp. 1667-1689, 2003.

Fig. 15. Grasp pattern of the hand

214

biomechaytronic

Documents