Abstract:
Daily life of thousands of individuals around the globe suffers due to physical or mental
disability related to limb movement. The quality of life for such individuals can be made better
by use of assistive applications and systems. In such scenario, mapping of physical actions
from movement to a computer aided application can lead the way for solution. Surface
Electromyography (sEMG) presents a non-invasive mechanism through which we can translate
the physical movement to signalsfor classification and use in applications. Keeping this in view,
this study propose a machine learning based framework for classification of 20 physical
actions. The framework looks into the various features from different modalities which
contribution from time domain, frequency domain and inter channel statistics. Next, we
conducted a comparative analysis of k-NN and SVM classifier using the feature set for multiple
normal and aggressive activities. Effect of different combinations of feature set has also been
recorded. Finally, the SVM classifier gives an accuracy of 100% for 10 normal actions and 1-
NN for a subset of features gives an accuracy of 98.91% for 10 aggressive actions respectively.
Additionally, we use both SVM and 1-NN to propose a hybrid approach to classify 20 physical
actions. The hybrid classifier gives an accuracy of 98.97% respectively. These finding are
useful for algorithm designer to choose the best approach keeping in mind the resources
available for execution of an algorithm.
