Recognition of Chronic Low Back Pain during Lumbar Spine Movements Based on Surface Electromyography Signals
文献类型：期刊论文
| 作者 | Shipeng Han; Wenjing Du; Olatunji Mumini Omisore; Huihui Li; Kamen Ivanov; Lei Wang |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | 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 chronic low back pain 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 study, surface electromyography (sEMG) signal analysis was explored for reocgnition of low back pain in subjects with non-specific symptoms. Eighty-eight CLBP subjects and a control group of eighty-three 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 thirty-one features from both the time and frequency domains were extracted 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 the SVM classifiers and optimal feature selection allowed for improved classification performance. The main aim of this study was to recognize CLBP in subjects with non-specific pathology during four types of movement. The major steps carried out to achieve that 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, the present study could inspire the design of appropriate programs that can ensure effective rehabilitation of CLBP patients. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14260]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Shipeng Han,Wenjing Du,Olatunji Mumini Omisore,et al. Recognition of Chronic Low Back Pain during Lumbar Spine Movements Based on Surface Electromyography Signals[J]. IEEE ACCESS,2018. |
| APA | Shipeng Han,Wenjing Du,Olatunji Mumini Omisore,Huihui Li,Kamen Ivanov,&Lei Wang.(2018).Recognition of Chronic Low Back Pain during Lumbar Spine Movements Based on Surface Electromyography Signals.IEEE ACCESS. |
| MLA | Shipeng Han,et al."Recognition of Chronic Low Back Pain during Lumbar Spine Movements Based on Surface Electromyography Signals".IEEE ACCESS (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
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