Article

Identifying finger and wrist flexion based actions using a single
channel surface electromyogram (sEMG) can lead to a number of applications such
as sEMG based controllers for near elbow amputees, human computer interface (HCI)
devices for elderly and for defence personnel. These are currently infeasible
because classification of sEMG is unreliable when the level of muscle contraction
is low and there are multiple active muscles. The presence of noise and
cross-talk from closely located and simultaneously active muscles is exaggerated
when muscles are weakly active such as during sustained wrist and finger flexion.
This paper reports the use of fractal properties of sEMG to reliably identify
individual wrist and finger flexion, overcoming the earlier shortcomings.
METHODS: SEMG signal was recorded when the participant maintained pre-specified
wrist and finger flexion movements for a period of time. Various established sEMG
signal parameters such as root mean square (RMS), Mean absolute value (MAV),
Variance (VAR) and Waveform length (WL) and the proposed fractal features:
fractal dimension (FD) and maximum fractal length (MFL) were computed.
Multi-variant analysis of variance (MANOVA) was conducted to determine the p
value, indicative of the significance of the relationships between each of these
parameters with the wrist and finger flexions. Classification accuracy was also
computed using the trained artificial neural network (ANN) classifier to decode
the desired subtle movements. RESULTS: The results indicate that the p value for
the proposed feature set consisting of FD and MFL of single channel sEMG was
0.0001 while that of various combinations of the five established features ranged
between 0.009 - 0.0172. From the accuracy of classification by the ANN, the
average accuracy in identifying the wrist and finger flexions using the proposed
feature set of single channel sEMG was 90%, while the average accuracy when using
a combination of other features ranged between 58% and 73%. CONCLUSIONS: The
results show that the MFL and FD of a single channel sEMG recorded from the
forearm can be used to accurately identify a set of finger and wrist flexions
even when the muscle activity is very weak. A comparison with other features
demonstrates that this feature set offers a dramatic improvement in the accuracy
of identification of the wrist and finger movements. It is proposed that such a
system could be used to control a prosthetic hand or for a human computer
interface.

Langue : ANGLAIS