TY - GEN
T1 - Classification of Finger Movements Using Multi-channel EMG and Machine Learning
AU - Mujeeb Rahman, K. K.
AU - Mohamed Nasor, K.
AU - Yelampalli, Praveen Kumar Reddy
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This paper describes an experimental study on decoding of finger movements using surface electromyography (EMG) signals obtained from Myo-armband and machine learning techniques. The study is set out to determine whether machine learning algorithms and EMG signals could be used to precisely decode finger movements. The paper includes descriptions of the EMG dataset used in the study, pre-processing steps, feature extraction techniques, and machine learning algorithm development. The proposed model recognized seven pre-defined finger movements, with an overall cross-validated AUC of 95.29%. The study’s results, which show that Myo-bands and a support vector machine algorithm can predict finger movements with impressive accuracy, could have a big impact on how prosthetics and other tools help people with disabilities are made.
AB - This paper describes an experimental study on decoding of finger movements using surface electromyography (EMG) signals obtained from Myo-armband and machine learning techniques. The study is set out to determine whether machine learning algorithms and EMG signals could be used to precisely decode finger movements. The paper includes descriptions of the EMG dataset used in the study, pre-processing steps, feature extraction techniques, and machine learning algorithm development. The proposed model recognized seven pre-defined finger movements, with an overall cross-validated AUC of 95.29%. The study’s results, which show that Myo-bands and a support vector machine algorithm can predict finger movements with impressive accuracy, could have a big impact on how prosthetics and other tools help people with disabilities are made.
KW - Decoding of finger movements
KW - Electromyogram
KW - Feature extraction
KW - Machine learning
KW - Myo-band
KW - SVM
UR - https://www.scopus.com/pages/publications/85200353206
U2 - 10.1007/978-981-97-0562-7_33
DO - 10.1007/978-981-97-0562-7_33
M3 - Conference contribution
AN - SCOPUS:85200353206
SN - 9789819705610
T3 - Lecture Notes in Electrical Engineering
SP - 439
EP - 451
BT - Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023
A2 - Kumar Jain, Pradip
A2 - Nath Singh, Yatindra
A2 - Gollapalli, Ravi Paul
A2 - Singh, S. P.
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Advances in Signal Processing and Communication Engineering, ICASPACE 2023
Y2 - 28 April 2023 through 29 April 2023
ER -