control strategies for hand prostheses using myoelectric patterns

7/27/2019 Control Strategies for Hand Prostheses Using Myoelectric Patterns

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CONTROL STRATEGIES FOR HAND PROSTHESES USINGMYOELECTRIC PATTERNS

M. Reischl, R. Mikut, C. Pylatiuk, S. Schulz

Forschungszentrum Karlsruhe GmbH, Institute of Applied Computer Science (IAI)

P.O. Box 3640, D-76021 Karlsruhe, GermanyPhone: +49-7247-825731, Fax: +49-7247-825786, Email: [email protected]

1 Introduction

In the year 1999 in Germany 26661 persons were registered who had an amputation of an armor a complete hand [1]. The main factors for a loss of an upper extremity are accidents followed

by general diseases and injuries from war. Although the loss of an upper limb results in drasticrestriction of life quality only few amputees are provided with artificial hands. The main factors

for the rejection of conventional prosthetic hands are low functionality and robot-like movement

of these artificial limbs.Arm prostheses are usually controlled by myoelectric sensors measuring electric muscle ac-

tivities. These sensors detect the level of muscle contraction and therefore give amputees thepossibility to control a mechanical prosthesis by muscle activity. The main problems analyzingmyoelectric patterns are noisy signals so that data cannot be processed reliably. Common my-

oelectric systems use simple thresholds to detect the state of muscle contraction and to classify

two to three movements [2].Recently, the Institute for Applied Computer Science of the Forschungszentrum Karlsruhe

(FZK) presented an artificial hand with the ability to move all finger joints independently (sec-

tion 2)[3]. To convert this high number of degrees of freedom into movement possibilities new

control strategies have to be developed. Several teams are trying to improve a corresponding

classification process by using complex preprocessing algorithms and artificial neural networks

[4, 5, 6, 7].

This paper presents a fuzzy-based approach to increase movement possibilities of prosthesesusing algorithms that can be implemented in portable environments (microcontroller). The aims

of this paper are

to present a concept for movement detection (section 3) and

to demonstrate an implementation strategy for fuzzy classifiers on microcontrollers (sec-

tion 4).

2 The FZK-Hand

The movement of the FZK-Hand is based on the so called flexible fluidic actuators having the

following advantages:

a high flexibility designed into their mechanical construction,

realization of very complex movements,

a lightweight construction and

low manufacturing costs.

The miniaturized actuators are integrated in the fingers of the new artificial hand (see Fig. 1).

This enables the construction of a lightweight hand with high functionality and human-like

movements.

The flexible fingers of the artificial hand are able to wrap around objects of different sizes

and shapes. Because of the elastic properties of the actuators the contact force is spread over a

greater contact area. Additionally the surface of the fingers is soft and the friction coefficient

is increased by the silicone-rubber glove, that covers the artificial hand. The result is a reducednecessary grip force to hold an object [8]. As a side-effect from the softness and elasticity ofthe hand it feels more natural when touched than a hard robotic hand and the risk of injury in

direct interaction with other humans is minimized [3, 9].


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Fig. 1: New ultralight anthropomorphic FZK-Hand (left) in comparison to a common prosthetic

hand (Otto Bock) (right)

3 Movement detection

Usually prostheses can be controlled by preprocessing and classifying myoelectric data (Fig. 2).

Optionally sensory feedback can be integrated using feedback actuators to give the amputeemore detailed information about the gripping process beyond a pure visual feedback. Due to

different kinds of injuries and amputation surgeries there is no general control strategy that can

be used with all amputees. Rather different strategies have to be developed and individually

fitted to the amputee. The aim of these strategies is to give the amputee a combination ofmaximum comfort and high functionality especially adapted to his individual anatomy.

Fig. 2: Control scheme of a prosthesis

One strategy is to automatically determine the users wish of movement by continuouslyscanning muscle activation. The advantage of this concept is the adaption of a prosthesis to theuser, not vice-versa. Additionally, various grip types can be implemented (see Fig. 3). To make

use of the large variety of possible hand and finger movements of the FZK-Hand, five different


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movements (hook grasp, pincer-like grasp, lateral pinch, spherical grasp , pointing index finger)

are executed in the following experiments.

Fig. 3: Examples of hand movements to be executed by a prosthesis: Hook grasp (left), Pincer-

like grasp (right)

The quality ofsensor data is restricted by the following items:

Myoelectric signals are very low in amplitude and therefore noise effects like electromag-

netic rays have to be considered [10].

The properties of myoelectric data are very sensible to changes of measurement condi-tions, e.g. temperature, moistness, etc.

As an effect of amputation surgery myoelectric signals derived from amputees are corre-

lated with each other.

In a forearm stump a maximum of two different muscle signals (tensors and extensors of

the forearm) can be contracted independently.

An experiment serves to scan a forearm for myoelectric activation with a sampling rate of100Hz, using Otto Bock, MYOBOCK-sensors with analog preprocessing. Normally, only two

myoelectric sensors are used. Here, up to eight sensor signals have been recorded and the

algorithm has automatically chosen the three to four most important sensor positions for graspdetection.

In order to increase classification accuracy additional features have to be derived from the

raw sensory signal (data preprocessing). In a first step Infinite Input Response filters (IIR) areapplied on the raw data to reduce noise effects:

(1)

The parameter describes the properties of the filter and is defined by:

(2)

is used as a filter constant, defines the sampling time. Using (2) the filter parameter canbe adapted to time-variant sampling times.

For each sensor two filters

(slow filter),

(fast filter) with different filter constants

(

for

,

for

,

) are introduced. Additionally to noise reduction these

filters serve to gain trend information

through

(3)

[11]. Fig. 4 shows the result of these operations applied on the raw sensory signal.In the next step indicators are developed to give information about the state of muscle con-

traction [12] (start-stop-detection). These indicators define start and stop signals for the clas-sification routine. In particular, three indicators are introduced: absolute value indicator, trendindicator and maximum-minimum-indicator. Start signals will be generated by one of the fol-

lowing options:


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0 500 1000 15000.2

0

0.2

0.4

0.6

0.8

1

1.2

Time in 0.01s

Voltage

inV

raw sensorysignal

slow filtered

signal

fast filteredsignal

trend

0 500 1000 15000

0.5

1

1.5

2

2.5

3

3.5

4

Time in 0.01s

VoltageinV/classification

raw sensorysignal

classification by absolutevalue indicator

classification by

trend indicator

classification bymaximumminimumindicator

classification

error

Fig. 4: Filter functions (left), raw sensory signal and activation levels of the indicators (right)

A certain threshold voltage has been passed (absolute value indicator).

A lower threshold has been passed in interrelation with positive trend signals (trend indi-

cator).

The difference between a sliding minimum and a sliding maximum about the past

time

steps exceeds a certain threshold (see Fig. 4).

Stop signals are mainly given by an AND-combination of the following conditions:

negative trend signal

,

minimum of the signal and

sliding maximum that is located below a certain threshold.

0 0.5 1 1.5 20

0.2

0.4

0.6

0.8

1

1.2

ZE PS PM PB PVB

Sensor 4 [V]

MBF

0 0.1 1 2

00.1

0.5

1

2

Sensor 1 [V]

Sensor4[V]

Hook GraspPincerlike Grasp

Fig. 5: Generated membership functions above data distribution (left) with ZE=zero,

P=positive, S=small, M=medium, B=big and V=very; Example for a generated fuzzy rule

(right)

After recognition of an existing muscle contraction data is handed out to a fuzzy rule base

serving as classifier for grasp classification. In order to consider differences in muscle posi-

tion, contraction level, etc. this rule base has to be developed for each amputee individually.

Therefore an off-line learning algorithm [13] is used


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to reduce the number of features by information theoretical methods [14],

to automatically generate membership functions by information theoretical methods [15],

to automatically generate rule bases by generating decision trees, extracting rules, pruning

these rules and searching for small rule bases [16, 17].

As a result of this algorithm, the rule bases are small (here: 10 rules) and contain disjunctionsof linguistic terms in their premises. One typical rule:

IF (Sensor1=PB OR PVB) AND (Sensor4=PM OR PB) THEN y=HookGrasp,

is shown in Fig. 5 (right). It processes two features (sensor 1 and sensor 4) and decides for hook

grasp if both sensors have high voltages according to figure 5 (right).

For healthy individuals, the fuzzy rule base is able to classify appr. 75%-90% (depending

on individual differences, training, sensor positions and pre-processed features) of the measured

five movements correctly. Caused by similar contraction patterns errors occur discriminatingsimilar movements (e. g. pincer grasp and spherical grasp). Furthermore the classification accu-

racy can be improved by exact reproduction of corresponding patterns. This can be achieved by

a training unit, which gives the amputee feedback of the activated rules while he is contracting

his muscles. Experiments using signals of amputees have been started in July 2001.

4 Microcontroller implementation

To avoid runtime problems and guarantee a good performance, modifications for an implemen-

tation on microcontrollers are necessary:

Algorithms have to be implemented recursively.

Constants have to be adapted to changing sampling rates.

A fast SUM-PROD fuzzy implementation is necessary to achieve high sampling rates and

therefore good classification results.

In conventional implementations, the fuzzification of all features and terms requires the maincomputation effort. Using the small rule bases of section 3 only few linguistic terms occur inthe rule premises.

0 0.5 1 1.5 20

0.5

1

1.5

2

ZEPS PM PB PVB

Sensor 4 [V]

MBF

g(x) f(x)

(x)

Fig. 6: Fast fuzzification of trapezoidal or triangular MBFs,

,

)

As a consequence, the computation effort can be reduced by combining fuzzification and

aggregation (computation of rule premises membership values). Disjunctions of terms (e. g.

Sensor4 = PM OR PB) are handled as new linguistic terms with trapezoidal membership


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functions. The membership values of all resulting trapezoidal or triangular MBFs are computed

by using the left ( ) and right ( ) lines of the MBFs (see Fig. 6). The membership value

of the resulting term results from minimum value of both functions and will be limited toa range between 0 and 1:

for

for

for

with

(4)

The aggregation is continued by multiplication of the results of Eq. (4) for all specified featuresin the rule premises. The activation and accumulation are performed by an addition of all mem-

bership values of rule premises with the same conclusion. For defuzzification, the maximum

method is used to get a crisp decision.This algorithm leads to a very compact ANSI C source code which can be included into

a microcontroller program. The maximum runtime for sampling and preprocessing data and

computing a rule base with 10 rules is 10 ms using a SIEMENS C164 microcontroller with a

clock frequency of 20 MHz.

5 Conclusions

This paper describes a fuzzy-based concept for grasp detection in prostheses. The aim is to adapt

a control unit to an amputees anatomy and therefore classify the amputees natural contraction

patterns or artificial patterns chosen by the amputee. There is no need for the amputee to learn

the handling of certain control schemes. In comparison to conventional threshold algorithms

used in prosthesis, different preprocessing algorithms enable a faster detection of movements.

Additionally it is shown that an implementation of the developed routines on microcontrollers

is possible. At the moment, the concept has been tested on healthy persons using similar sensorposition as for amputees. Clinical tests for amputees have been started in July 2001. Further

research is concentrated on modified features, alternative control strategies and practical tests.

References

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control strategies for hand prostheses using myoelectric patterns

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