DEVELOPMENT OF A MYOELECTRIC CONTROLLER

BASED ON KNEE ANGLE ESTIMATION

Alberto López Delis, João Luiz Azevedo de Carvalho, Adson Ferreira da Rocha

Francisco Assis de Oliveira Nascimento and Geovany Araújo Borges

Department of Electrical Engineering, Universidade de Brasilia, Brasilia, DF, Brazil

Keywords: Electromyographic signal, Prosthesis control, Microcontrolled bioinstrumentation, Feature extraction,

Dimensionality reduction, Neural network.

Abstract: This paper presents the development of a bioinstrumentation system for the acquisition and pre-processing

of surface electromyographic (SEMG) signals, as well as the proposal of a myoelectric controller for leg

prostheses, using algorithms for feature extraction and classification of myoelectric patterns. The

implemented microcontrolled bioinstrumentation system is capable of recording up to four SEMG channels,

and one electrogoniometer channel. The proposed neural myoelectric controller is capable of predicting the

intended knee joint angle from the measured SEMG singals. The controller is designed in three stages:

feature extraction, using auto-regressive model and amplitude histogram; feature projection, using self

organizing maps; and pattern classification, using a Levenberg-Marquadt neural network. The use of SEMG

signals and additional mechanical information such as that provided by the electrogoniometer may improve

precision in the control of leg prostheses. Preliminary results are presented.

1 INTRODUCTION

The use of microprocessors in myoelectric control

has grown notably, benefitting from the functionality

and low cost of these devices. Microprocessors

provide the ability to employ advanced signal

processing and artificial intelligence (AI) methods as

part of a control system, while easily conforming to

control options, and adjusting to the input

characteristics. They also provide the ability to

implement pattern-recognition-based control

schemes, which increases the variety of control

functions, and improves robustness.

Surface electromyographic (SEMG) signals

provide a non-invasive tool for investigating the

properties of skeletal muscles (Sommerich et al,

2000). The bandwidth of the recorded potentials are

relatively narrow (50-500 Hz), and their amplitude is

low (50 µV - 5 mV) (De Luca, 2006). These signals

have been used not only for monitoring muscle

behavior during rehabilitation programs (Monseni-

Bendpei et al, 2000), but also for the mechanical

control of prostheses. In this context, it is important

to be able to correctly predict which movement is

intended by the user. The SEMG signal is very

convenient for such application, because it is non-

invasive, simple to use, and intrinsically related to

the user’s intention. However, there are other useful

variables, especially those related to proprioception,

for example: the angle of a joint, the position of the

limb, and the force being exerted.

This project is supported under the development

of an active leg prosthesis prototype (Figure 1). The

prosthesis has three degrees of freedom: one for the

knee (sagittal plane), and two movements for the

foot (sagittal and frontal plane). The three degrees of

freedom are associated to the angles θ

, θ

and θ

controlled by DC reduction motors.

The prototype will be fixed to the patient’s upper

leg through a fixing capsule, where the SEMG

sensors will be located. The prosthesis will receive

control commands through digital signal processing,

feature extraction, and pattern classification.

Specifically, for the development of an active leg

prosthesis that also possesses ankle and foot axes, it

is necessary to use other sources of information

besides SEMG (Ferreira et al, 2005). Thus, the use

of myoelectric signals combined with other variables

related to proprioception may improve the reliability

in closed-loop control systems. In addition, the

bioinstrumentation system should be as immune to

noise and interference as possible. This can be

Delis A., de Carvalho J., da Rocha A., Nascimento F. and Borges G. (2009).

DEVELOPMENT OF A MYOELECTRIC CONTROLLER BASED ON KNEE ANGLE ESTIMATION.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 97-103

DOI: 10.5220/0001550600970103

 SciTePress

achieved by proper board and shielding design, as

well as the use of filters whenever they are

necessary.

Figure 1: Mechanical structure of the prosthesis prototype.

Figure 2 presents the typical main components of

a general myoelectric controller based on pattern

recognition. The SEMG signals are acquired by

surface electrodes placed on the skin over muscle(s)

of the user. The signals originating from the

electrodes are pre-amplified to differentiate the

small signals of interest, and then are amplified,

filtered and digitized. Finally, the information is

transferred to a myoelectric controller (Asghari and

Hu, 2007).

Figure 2: Typical main components of a general

myoelectric controller based on pattern recognition.

In the design and implementation of a myoelectric

controller, the system’s precision is essential for a

realistic accomplishment of the user’s intention. The

precision is an important factor on the development

of multi-sensory controllers, and can be improved by

extracting more information from the muscle’s state,

and using a classifier that is capable of improving

this information. The controller should be capable of

learning the muscular activation patterns that are

used in natural form for typical movements. It also

needs robustness against the condition variations

during the operation. The response time cannot

create delays that are noticeable to the user.

This article presents a micro-controlled

bioinstrumentation prototype system as part of the

development of an active leg prosthesis structure

that allows the acquisition and processing of

electromyographic signals and other data related to

the articulate movement, specifically the angle of the

knee. The information obtained is processed in order

to obtain appropriate myoelectric patterns for

prosthesis control. Preliminary results on the design

of algorithms for the estimation of the knee angle,

based on patterns recognitions techniques, are

presented.

2 METHODS

The front end stage of the designed

bioinstrumentation system acquires up to four

SEMG channels. The SEMG are measured on a pair

of agonist and antagonist muscles of the leg (Fig. 3).

An electrogoniometer is used to measure the flexion

and extension angles of the knee joint (Fig. 3c).

(a) (b) (c)

Figure 3: Experimental setup. Surface electrodes are

placed over a pair of agonist and antagonist muscle groups

of the leg: (a) vastus intermedius, (b) semitendinosus. An

electrogoniometer is used to measure the flexion and

extension angles of the knee joint (c).

Differential amplifiers, used in the bipolar

configuration, significantly reduce the common

mode interference signals (CMRR > 110 dB). A

band-pass filter between 10 Hz and 500 Hz

frequency range is used. It is composed by a low-

pass filter and high-pass filter with a programmable

gain stage from digital potentiometers, controlled by

the microcontroller. These elements allow the setting

of the SEMG amplitude levels based on the

measurements from the patient. To minimize power

consumption and increase noise immunity,

operational amplifiers with JFET inputs were used.

To obtain adequate myoelectric amplitude, an

overall gain of up to 20000 can be programmed at

the front end (De Luca, 2006).

A second block, micro-controlled and optically

isolated from the front end (Figure 4), centralizes all

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

the functions associated with the analog/digital

conversion process, implementing the digital gain

control for the front end amplifiers and synchronized

sampling of SEMG signals. The microcontrollers

belong to the ARM SAM7S64 ATMEL family of

high performance processors, based on 32-bit RISC

architecture with an integrated group of peripherals

that minimize the number of external components.

Figure 4: Block diagram of the bioinstrumentation system.

A 13-bit A/D converter with Serial Peripheral

Interface (SPI) is used for signal sampling, and

allows discriminating small amplitude levels. The

electrogoniometer channel is coupled to the system,

and generates an electric signal corresponding to the

angular position ranging from 30 to 240 degrees.

The sampling frequency of each channel is 1744.25

Hz. Figure 5 presents example data acquired during

an experimental measurement.

Figure 5: Recorded SEMG signals (rectus femoris and

opposite muscles) and angle of the knee joint during a

10-second experiment.

The microcontroller is linked through RS-485

protocol to the central processor of the prosthesis,

which is responsible for coordinating the tasks in the

control process. Besides the RS485 protocol, which

provides the interaction of the block with the central

processor, RS-232C and USB interfaces are

available for the communication with a PC when the

system is configured in stand alone mode (Figure 6).

In this mode, the system allows the visualization of

the state of the experiments during their realization

using a LCD display. The instrumentation system is

designed using low power consumption components,

which increases the system’s portability.

Figure 6: Bioinstrumentation module (with accessories)

configured in stand alone mode.

2.1 Adaptive Filter Implementation

The power line interference usually has its first

harmonics (60 Hz, 120 Hz, 180 Hz, and 240 Hz) in a

portion of the spectrum with major SEMG energy

concentration. The use of an analog notch filter may

distort the signal; therefore it should only be used

when really necessary. Generally, the best option is

to use an adaptive notch filter. An embedded

subroutine in the ARM-SAM7S64’s core

implements an adaptive notch filter in real time. This

filter maintains a running estimate of the 60 Hz

interference, and the current noise at time t can be

estimated from the previous two noise estimates

(Hamilton, 1996), as shown in equations (1) and (2),

)2()()( nTtenTteNte −

−

(1)

where T is the sample period and N=2cos(2

π.

60.T).

In the filter, the output is generated by subtracting

the estimated noise, e(t), from the input signal, x(t).

The expression presented in equation (2) is used to

implement the filter.

)]()([)]()([)( nTtenTtxtetxtf −−

−

(2)

If f(t)>0, then the estimate was too low, so we

adjust the estimate upward by incrementing d:

dTnTeTnTe ++

)()(

(3)

If f(t) < 0, the estimate was too high, so the

estimate is decremented:

dTnTeTnTe −+

)()(

(4)

DEVELOPMENT OF A MYOELECTRIC CONTROLLER BASED ON KNEE ANGLE ESTIMATION

As d increases, the filter adapts more rapidly, and

exhibits a broad bandwidth. Similarly, as d

decreases, the filter adapts more slowly, and has a

narrower bandwidth. The selection of the d factor is

empiric, based on test realizations, and its value is

small compared to the dynamic range of the A/D

converter (Hamilton, 1996). Figure 7 shows the

adaptive filtering of a SEMG signal measured on the

rectus femoris muscle.

Figure 7: Adaptive filtering performed on a SEMG signal

contaminated with power-line interference.

2.2 Myoelectric Controller

Presenting the myoelectric signal directly to a

classifier is impractical, because of the

dimensionality and the random characteristics of the

signal. It’s necessary that the signal is represented as

a vector with reduced dimensionality, i.e., a feature

vector. The myoelectric controller algorithm

proposes the use of three stages for feature

extraction and pattern classification. The first stage

consists in the mixture of feature vectors from time

domain and spectral analysis. A second stage will

perform the reduction of the feature space, allowing

the increase in the number of SEMG input sensors

without affecting the performance of the control

process. The last stage has the goal of estimating the

knee angle.

2.2.1 Feature Vector Extraction

Given the stochastic nature of the myoelectric

signal, it can be considered as a time series, and

modeled as a linear combination of their past and

present values. The autoregressive model is a

convenient structure for model identification,

especially when the computations of velocity and

response time are important, as in the recognition

and classification of myoelectric patterns. The

autoregressive coefficients provide information

about the muscular contraction. The estimate of the

coefficients is performed using a recursive least

squares (RLS) technique, with a forgetting factor.

This method gives more weight to the most recent

samples at the moment of the iteration cycle. The

parameters are calculated recursively (Ljung, 1987),

as presented in equations (5), (6) and (7):

][

−−

−+=

kkkkk

ηϕηη

(5)

kkk

λϕϕλ

ϕϕ

⎥

⎦

⎤

⎢

⎣

⎡

−=

−

−−

(6)

ϕϕλ

1−

(7)

where

are the vector coefficients that are

estimated at discrete time k;

are the regressive

vectors,

P is the inverse correlation matrix and

L is the gain vector of the filter. The forgetting

factor

controls the system response time. The

coefficient estimated at instant k can be interpreted

as a characteristic of the SEMG signal within the

time interval specified by the forgetting factor, and it

is a way of determining the angular displacement

that the patient wants to impose to the prosthesis

(Ferreira et al, 2005). The coefficients form a feature

vector for the pattern classification processes.

Recent research (Hargrove et al, 2008) has

demonstrated that a functional and efficient

configuration consists of a mixture of feature vectors

on the time domain with autoregressive coefficients.

This configuration provides good classification

precision, and is computationally efficient, which

facilitates its implementation in embedded systems.

It is also more robust to the displacement of the

surface electrodes (Hargrove et al, 2008).

This work uses a mixture of the autoregressive

vector with the EMG Histogram method. The EMG

Histogram is an extension of the Zero Crossing and

the Willison amplitude (Zardoshti-Kermai et al,

1995). Myoelectric signals reach relatively higher

levels during the contraction process, compared to

the base line amplitudes. Thus, vectors obtained

from the histogram provide a measure of the

frequency in which the signal reaches each level of

amplitude, associated with different histogram bins.

For the implementation of the histogram, a

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

100

symmetrical interval with respect to base line over

the SEMG register is established and the same is

subdivided into 9 bins. These bins represent

intervals of amplitude in which the SEMG signal is

grouped.

The resultant feature vectors (autoregressive and

histogram) are concatenated, and then used as the

input vector of the feature projection stage.

2.2.2 Feature Projection

A feature projection stage is used to reduce the

dimension of the feature space of the input vectors,

before pattern classification process, using

supervised neural networks. This reduction is

performed using an unsupervised Kohonen

self-organizing map (SOM) neural network. The

groups of vector coefficients obtained from each

SEMG channel using the RLS and histogram

methods are transformed into two-dimensional

vectors. With the reduction in input dimension, the

SOM is able to reduce noise and absorb the large

variations that appear in the original features. In

addition, the SOM can shorten the training time of

the supervised neural network. The unsupervised

SOM can find the winning neuron on a 2-D map to

represent the original pattern. To find the output

neuron (winning node), the following steps are used,

according to the learning rule of the Kohonen

Feature map (Haykin, 1999).

Step 1: Choose random values for the initial

weight vectors W

(0).

Step 2: Find the winning neuron y

at time step t

(Similarity Matching), by using the minimum-

distance Euclidean criterion:

tjtWtxy

,....,2,1,)()(minarg =−=

(8)

Step 3: Update the synaptic weight vectors of all

neurons by using the following update rule:

)]()([)()()()1(

tWtxthttWtW

jyjjj

−+=+

(9)

where

)(t

is the learning rate, and )(

is the

neighbors function centred around the winner.

)(t

and )(

are varied dynamically during the

learning stage, in order to obtain optimal results.

Step 4: Go back to step 2 until no changes in the

feature map are observed.

The inputs of the Kohonen’s SOM are features

from each channel, and the output is the 2-D

coordinate (on the x and y axes) on the 2-D

topological net. A 2-D coordinate is a condensed

feature for each channel (Figure 8).

2.2.3 Myoelectric Classification

Multi-layer neural networks have been successfully

applied to many difficult and nonlinear problems in

diverse domains and there is considerable research

on methods to accelerate the convergence time of

the multi-layer feedforward neural network

algorithm (Battiti, 1992 and Charalambous, 1992).

The method used in this paper is the Levenberg-

Marquadt (LM) algorithm (Hagan and Menhaj,

1994), which consists in the use of the nonlinear

least squares algorithm to the batch training of

multi-layer perceptrons. The LM algorithm can be

considered a modification of the Gauss-Newton

method. The key step in the LM algorithm is the

computation of the Jacobian matrix. The LM

algorithm is very efficient when training networks

which have up to a few hundred weights. Although

the computational requirements of the LM algorithm

become much higher after each iteration, this is fully

compensated by its higher efficiency. This is

especially true when high precision is required

(Hagan and Menhaj, 1994). Figure 8 presents the

complete block diagram of the myoelectric

controller.

Figure 8: Block diagram of the proposed myoelectric

controller algorithm.

DEVELOPMENT OF A MYOELECTRIC CONTROLLER BASED ON KNEE ANGLE ESTIMATION

101

)(ix

3 RESULTS

As a prototype implementation, the training and

validation processes were performed in off-line

mode, and the algorithms described above were

implemented in Matlab. At a later stage, the full

validation of the controller will be the executed from

an embedded system running on a Linux platform.

For this demonstration, SEMG measurements

were captured from a healthy subject using 10 mm

Ag/AgCL surface electrodes placed on a pair of

antagonistic muscles, associated with the flexing and

extension movements of the knee (Figure 3). The

electrodes were arranged in bipolar configuration,

and gel was used to reduce the resistance between

electrodes and skin. The distances between the

centers of the electrodes was 3-5 mm, and the

reference electrode was placed over the lateral

condyle bone. A total of 10 measurements were

performed, divided in two groups of signals -

training and validation - acquired during walks with

different speeds, with duration of 10 seconds.

For training purposes, it is essential to know

information about the input and output, comparing

the dimensional vectors obtained from the SOM

network to the displacement angle measured by the

electrogoniometer sensor. Figure 9 shows the

estimated angle compared to the measured angle

from the electrogoniometer. Although the estimated

angle follows the measurement satisfactorily, the

output of the LM network presents the impulsive

noise (9a), which is canceled using a moving

average recursive filter with a 50-sample window

(9b). This filter keeps the changes levels or slopes

that are present in the angle estimated and present a

Figure 9: Comparison of the estimated knee angle (blue)

to the measured angle from the electrogoniometer (red):

(a) before filtering; (b) after filtering.

delay of (M - 1) / 2 samples, where M is the number

of samples in the average (Smith, 1999). The results

obtained with 50 samples of average were

satisfactory, decreasing the variance and conserving

the changes levels (Figure 9b).

A preliminary comparison was performed

between the proposed algorithm and the methods

proposed by Ferreira et al. (2005). The proposed

algorithm it is an alternative to the latter approach,

which consists in using the AR model for feature

extraction, and a LM multi-layer perceptron neural

network for pattern classification. The evaluation

was based on the classification error, which was

calculated using the following equation:

(%)100

)(

)()(

−

∑

ixix

error

(10)

where

)(ix and represent the angular values

from the electrogoniometer sensor and the angle

estimated vectors respectively, N is the dimension of

the vectors. Table 1 presents the averages error rate

of classification measured in the group of validation

signals.

The results in Table 1 show that the proposed

algorithm achieved better classification then the

method proposed by Ferreira et al. (2005). This

means that the proposed method is more accurate in

estimating the knee joint angle from the myoelectric

signals.

Table 1: Comparison based the average rate classification.

Ferreira et al. (2005) 8.02% ± 4.2

Proposal Algorithm 5.86% ± 1.6

4 CONCLUSIONS

This paper presents the current state of development

of a bioinstrumentation system for active control of

leg prostheses. Features of the system and of the

signal processing algorithm used in the myoelectric

controller were presented. The system allows the

acquisition of SEMG signals with a maximum

amount of signal information and a minimum

amount of contamination from electrical noise. The

results show that the system has great potential for

future developments in leg prostheses. Another

feature of the system is that it works not only as a

part of the prosthesis control, but also in stand alone

mode. Preliminary analysis showed that the

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

102

computational complexity of the proposed algorithm

increases for each iteration during execution of the

LM network. Future work will aim optimize the

code for its execution in real time.

ACKNOWLEDGEMENTS

This work was partially supported by CAPES and

CNPq.

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