Authors:
Surya Naidu
;
Anish Turlapaty
and
Vidya Sagar
Affiliation:
Biosignal Analysis Group, IIIT Sri City, Sri City, India
Keyword(s):
sEMG, Fine-ADL, Feature Extraction, Neural Networks, CNN Bi-LSTM, Class-Wise Analysis.
Abstract:
Most studies on surface electromyography (sEMG) related to finger activities have concentrated on grips, grasps and general arm movements without any emphasis on the correlation of body postures and hand positions on the finger-centric activities. The main objective of the new dataset is to investigate activities of daily living (ADL) needing focus on finer motor control in diverse measurement conditions. In this paper, we present a novel dataset of finger-centric activities of daily living comprising 5-channel sEMG signals collected under different body postures and hand positions. The dataset encompasses sEMG signals acquired from 10 subjects, performing 10 distinct fine-ADLs in various body postures and hand positions. Further, a machine learning framework for classification of the fine-ADL is developed as follows. Each signal is segmented into 250ms windows and Time Domain (TD), Frequency Domain (FD), Wavelet Domain (WD) and Eigenvalues features are extracted. Four classifier fra
meworks using the features are implemented for the analyses. The results reveal that a hybrid CNN Bi-LSTM achieves an average test accuracy of 76.85%, followed by a 5-layered fully connected neural network (FCNN) with 72.42%, in aggregate scenario. An average subject-wise test accuracy of 88% is achieved by the FCNN across all body postures and hand positions combined. Most importantly, the CNN Bi-LSTM, enhances subject-wise classification by an average test accuracy of 27 .47% than the FCNN under varying body postures. Dependencies of the test accuracy on measurement conditions are analyzed. Stand body posture is found to be the easiest to classify in Aggregate scenario, whereas Folded Knees was the most difficult to classify. An increase in test accuracy with an increase in training data is observed body postures combinations analysis.
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