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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.

Recognition of Chronic Low Back Pain During Lumbar Spine Movements Based on Surface Electromyography Signals