TY - GEN
T1 - The Identification of Significant Mechanomyography Time-Domain Features for the Classification of Knee Motion
AU - Mohamed, Tarek Mohamed Mahmoud Said
AU - Abdullah, Muhammad Amirul
AU - Alqaraghuli, Hasan
AU - Musa, Rabiu Muazu
AU - Nasir, Ahmad Fakhri Ab
AU - Mohd Razman, Mohd Azraai
AU - Bajuri, Mohd Yazid
AU - P. P. Abdul Majeed, Anwar
N1 - Funding Information:
Acknowledgements The authors would like to acknowledge Universiti Malaysia Pahang for funding this study via RDU180321.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Stroke is the third leading cause of long term disability in the world. More often than not, the patients who suffer from such cerebrovascular disease endure restricted activities of daily living (ADL). Rehabilitation is deemed necessary to improve ones ADL, especially in the early stages of stroke. This study presents the classification of knee motion; particularly extension and flexion, based on muscle signals that could be utilised by an exoskeleton for rehabilitation purpose. A total of 20 subjects participated in the present investigation. The mechanomyography (MMG) signals were collected by accelerometers placed on four of the muscles that control the knee joint, namely, Rectus Femoris, Gracilis, Vastus Medialis, and Biceps Femoris, respectively. Eight statistical features were extracted from the raw data, i.e., root mean square (RMS), variance (VAR), mean, standard deviation (STD), kurtosis, skewness, minimum, and maximum along all x, y and z-axes. The Chi-Square (χ2) feature selection technique was used to identify significant features, in which 30 was identified amongst the 96 extracted features. A 10-fold cross-validation technique was employed in training a Support Vector Machine (SVM) model on a dataset that was partitioned with a ration of 80:20 for train and test, respectively. It was demonstrated in the present investigation that through the reduction of features, the test accuracy increased from 83.3 to 90%, suggesting the importance of the selected features. The findings from the study could pave the way for its adoption on a knee-based exoskeleton for rehabilitation.
AB - Stroke is the third leading cause of long term disability in the world. More often than not, the patients who suffer from such cerebrovascular disease endure restricted activities of daily living (ADL). Rehabilitation is deemed necessary to improve ones ADL, especially in the early stages of stroke. This study presents the classification of knee motion; particularly extension and flexion, based on muscle signals that could be utilised by an exoskeleton for rehabilitation purpose. A total of 20 subjects participated in the present investigation. The mechanomyography (MMG) signals were collected by accelerometers placed on four of the muscles that control the knee joint, namely, Rectus Femoris, Gracilis, Vastus Medialis, and Biceps Femoris, respectively. Eight statistical features were extracted from the raw data, i.e., root mean square (RMS), variance (VAR), mean, standard deviation (STD), kurtosis, skewness, minimum, and maximum along all x, y and z-axes. The Chi-Square (χ2) feature selection technique was used to identify significant features, in which 30 was identified amongst the 96 extracted features. A 10-fold cross-validation technique was employed in training a Support Vector Machine (SVM) model on a dataset that was partitioned with a ration of 80:20 for train and test, respectively. It was demonstrated in the present investigation that through the reduction of features, the test accuracy increased from 83.3 to 90%, suggesting the importance of the selected features. The findings from the study could pave the way for its adoption on a knee-based exoskeleton for rehabilitation.
KW - Feature selection
KW - Knee motion
KW - Machine learning
KW - Mechanomyography
KW - Rehabilitation
UR - http://www.scopus.com/inward/record.url?scp=85112584264&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4597-3_29
DO - 10.1007/978-981-33-4597-3_29
M3 - Conference Proceeding
AN - SCOPUS:85112584264
SN - 9789813345966
T3 - Lecture Notes in Electrical Engineering
SP - 313
EP - 319
BT - Recent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Ibrahim, Ahmad Najmuddin
A2 - Ishak, Ismayuzri
A2 - Mat Yahya, Nafrizuan
A2 - Zakaria, Muhammad Aizzat
A2 - P. P. Abdul Majeed, Anwar
PB - Springer Science and Business Media Deutschland GmbH
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
Y2 - 6 August 2020 through 6 August 2020
ER -