TY - GEN
T1 - DWMS-HGR
T2 - 5th International Conference on Data Science and Applications, ICDSA 2024
AU - Gehlot, Naveen
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Surface electromyography (sEMG) based recognition of hand gestures controls robotic arms or prosthetics and assists in rehabilitation. Controlling these devices requires data acquisition and a deep learning model to classify gestures for precise control. However, deep learning models are highly sensitive to the raw sEMG signal data and struggle with variations in the magnitude of sEMG signal features. Therefore, this study proposes an approach to address this issue. This approach combines preprocessing of the raw sEMG signal data from two channels, normalization, and a sliding window technique to provide meaningful data to the deep learning model (1D-CNN). Specifically, the proposed framework preprocesses the data (DWP), normalizes it using Min-Max normalization (MMN), and applies a sliding window (SW) approach known as “DWMS” to classify the gestures. The DWMS achieves the highest accuracy compared to other scenarios, including those without preprocessing and with preprocessing using other normalization techniques such as Z-score, Robust scaling, and Root Mean Square. The results of the DWMS, in terms of accuracy, precision, recall, and F-score, are 81.72%, 81.47%, 81.72%, and 81.38%, respectively.
AB - Surface electromyography (sEMG) based recognition of hand gestures controls robotic arms or prosthetics and assists in rehabilitation. Controlling these devices requires data acquisition and a deep learning model to classify gestures for precise control. However, deep learning models are highly sensitive to the raw sEMG signal data and struggle with variations in the magnitude of sEMG signal features. Therefore, this study proposes an approach to address this issue. This approach combines preprocessing of the raw sEMG signal data from two channels, normalization, and a sliding window technique to provide meaningful data to the deep learning model (1D-CNN). Specifically, the proposed framework preprocesses the data (DWP), normalizes it using Min-Max normalization (MMN), and applies a sliding window (SW) approach known as “DWMS” to classify the gestures. The DWMS achieves the highest accuracy compared to other scenarios, including those without preprocessing and with preprocessing using other normalization techniques such as Z-score, Robust scaling, and Root Mean Square. The results of the DWMS, in terms of accuracy, precision, recall, and F-score, are 81.72%, 81.47%, 81.72%, and 81.38%, respectively.
KW - 1D CNN
KW - Electromyography
KW - Hand gesture recognition
KW - Normalization
KW - Windowing
UR - http://www.scopus.com/inward/record.url?scp=105006831540&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-1185-0_11
DO - 10.1007/978-981-96-1185-0_11
M3 - Conference contribution
AN - SCOPUS:105006831540
SN - 9789819611843
T3 - Lecture Notes in Networks and Systems
SP - 129
EP - 140
BT - Data Science and Applications - Proceedings of ICDSA 2024
A2 - Nanda, Satyasai Jagannath
A2 - Yadav, Rajendra Prasad
A2 - Gandomi, Amir H.
A2 - Saraswat, Mukesh
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 17 July 2024 through 19 July 2024
ER -