Classification of Fine-ADL Using sEMG Signals Under Different Measurement Conditions

Surya Naidu, Anish Turlapaty, Vidya Sagar

2024

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 frameworks 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.

Paper Citation

in Harvard Style

Naidu S., Turlapaty A. and Sagar V. (2024). Classification of Fine-ADL Using sEMG Signals Under Different Measurement Conditions. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 591-598. DOI: 10.5220/0012346000003657

in Bibtex Style

@conference{biosignals24,
author={Surya Naidu and Anish Turlapaty and Vidya Sagar},
title={Classification of Fine-ADL Using sEMG Signals Under Different Measurement Conditions},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={591-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012346000003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Classification of Fine-ADL Using sEMG Signals Under Different Measurement Conditions
SN - 978-989-758-688-0
AU - Naidu S.
AU - Turlapaty A.
AU - Sagar V.
PY - 2024
SP - 591
EP - 598
DO - 10.5220/0012346000003657
PB - SciTePress