TY - JOUR
T1 - Recognition of Chronic Low Back Pain during Lumbar Spine Movements Based on Surface Electromyography Signals
AU - Du, Wenjing
AU - Omisore, Olatunji Mumini
AU - Li, Huihui
AU - Ivanov, Kamen
AU - Han, Shipeng
AU - Wang, Lei
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61501445 and Grant U1505251, in part by the Applied Project for Scientific and Technological Research and Development under Grant 2015B020233004, and in part by the Foundation of Public Technology Service Platform of Biomedical Electronics.
Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - Chronic low back pain (CLBP) is a common musculoskeletal disorder and a major source of disability in adults. The assessment of lumbar muscle functioning has proven as an appropriate approach for early identification of CLBP when significant pathological signs and symptoms are absent. Thus, earlier therapy or rehabilitation can be administered to prevent further deterioration, such as spinal stenosis or disk herniation. In this paper, surface electromyography (sEMG) signal analysis was explored for the recognition of low back pain in subjects with non-specific symptoms; 88 CLBP subjects and a control group of 83 subjects were recruited for sEMG data acquisition. Subjects were asked to perform four specific movements, namely forward bending, backward bending, right lateral flexion, and left lateral flexion. While performing each movement, sEMG signals from three pairs of lumbar muscles were captured, and 31 features from both the time and frequency domains were extracted from the signal. Finally, the main feature group and four subsets, derived from it, were explored. The suggested method allowed to achieve CLBP recognition accuracy of 98.04% based on subset C for forward bending, followed by 96.15% based on subset E for right lateral flexion, 93.33% based on subset E for left lateral flexion, and 91.30% based on subset B for backward bending. A combination of support vector machine classifiers and optimal feature selection allowed for improved classification performance. The main aim of this paper is to recognize CLBP in subjects with non-specific pathology during the four types of movement. The major steps carried out to achieve this are pre-processing, feature selection, and classification of the sEMG signals acquired from 171 subjects. Results suggest CLBP recognition based on sEMG as a promising alternative to the conventional methods. Therefore, this paper could inspire the design of appropriate programs that can ensure effective rehabilitation of CLBP patients.
AB - Chronic low back pain (CLBP) is a common musculoskeletal disorder and a major source of disability in adults. The assessment of lumbar muscle functioning has proven as an appropriate approach for early identification of CLBP when significant pathological signs and symptoms are absent. Thus, earlier therapy or rehabilitation can be administered to prevent further deterioration, such as spinal stenosis or disk herniation. In this paper, surface electromyography (sEMG) signal analysis was explored for the recognition of low back pain in subjects with non-specific symptoms; 88 CLBP subjects and a control group of 83 subjects were recruited for sEMG data acquisition. Subjects were asked to perform four specific movements, namely forward bending, backward bending, right lateral flexion, and left lateral flexion. While performing each movement, sEMG signals from three pairs of lumbar muscles were captured, and 31 features from both the time and frequency domains were extracted from the signal. Finally, the main feature group and four subsets, derived from it, were explored. The suggested method allowed to achieve CLBP recognition accuracy of 98.04% based on subset C for forward bending, followed by 96.15% based on subset E for right lateral flexion, 93.33% based on subset E for left lateral flexion, and 91.30% based on subset B for backward bending. A combination of support vector machine classifiers and optimal feature selection allowed for improved classification performance. The main aim of this paper is to recognize CLBP in subjects with non-specific pathology during the four types of movement. The major steps carried out to achieve this are pre-processing, feature selection, and classification of the sEMG signals acquired from 171 subjects. Results suggest CLBP recognition based on sEMG as a promising alternative to the conventional methods. Therefore, this paper could inspire the design of appropriate programs that can ensure effective rehabilitation of CLBP patients.
KW - Chronic low back pain
KW - lumbar muscle function
KW - recognition accuracy
KW - surface electromyography
UR - http://www.scopus.com/inward/record.url?scp=85055184365&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2877254
DO - 10.1109/ACCESS.2018.2877254
M3 - Article
AN - SCOPUS:85055184365
SN - 2169-3536
VL - 6
SP - 65027
EP - 65042
JO - IEEE Access
JF - IEEE Access
M1 - 8502033
ER -