TY - GEN
T1 - Efficient Contaminant Identification in sEMG Signals using Machine Learning
AU - Jena, Ashutosh
AU - Sharma, Padmaja
AU - Gehlot, Naveen
AU - Vijayvargiya, Ankit
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Surface electromyography (sEMG) signal classification applications, such as upper limb prosthesis, have increased in recent years. Eliminating unwanted noise signal for the exact and controlled motion of prosthesis is imperative. Noise in signal make a model biased and lead to mis-classifications. During the usage of a prosthetic arm, mis-classifications of hand gestures can cause it to behave erratically. The choice of a proper filter depends on the type of noise. Therefore, it is necessary to correctly identify the noise and filter it without increasing its hardware complexity. In this article, a study on three approaches to identify four commonly occurring noise in sEMG signal is performed, which is useful for filter selection. The three approaches include the tabular feature, sequence feature, and image feature based classification. Six different classifiers are used to compare the three approaches on the basis of accuracy, time consumption, and memory consumption. Models are also tested on varying noise levels for robustness analysis. On all the levels of noise, the sequence feature based classification approach gives a reasonably good accuracy while consuming the least time and memory.
AB - Surface electromyography (sEMG) signal classification applications, such as upper limb prosthesis, have increased in recent years. Eliminating unwanted noise signal for the exact and controlled motion of prosthesis is imperative. Noise in signal make a model biased and lead to mis-classifications. During the usage of a prosthetic arm, mis-classifications of hand gestures can cause it to behave erratically. The choice of a proper filter depends on the type of noise. Therefore, it is necessary to correctly identify the noise and filter it without increasing its hardware complexity. In this article, a study on three approaches to identify four commonly occurring noise in sEMG signal is performed, which is useful for filter selection. The three approaches include the tabular feature, sequence feature, and image feature based classification. Six different classifiers are used to compare the three approaches on the basis of accuracy, time consumption, and memory consumption. Models are also tested on varying noise levels for robustness analysis. On all the levels of noise, the sequence feature based classification approach gives a reasonably good accuracy while consuming the least time and memory.
KW - image feature
KW - machine learning
KW - Noise identification
KW - surface electromyography
UR - http://www.scopus.com/inward/record.url?scp=85190376473&partnerID=8YFLogxK
U2 - 10.1109/ICPC2T60072.2024.10474881
DO - 10.1109/ICPC2T60072.2024.10474881
M3 - Conference contribution
AN - SCOPUS:85190376473
T3 - 2024 3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024
SP - 25
EP - 30
BT - 2024 3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024
Y2 - 18 January 2024 through 20 January 2024
ER -