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| 基于SVM的sEMG和加速度融合跌倒识别方法 |
| Fall recognition based on surface electromyography and acceleration fusion by SVM |
| 投稿时间：2018-11-09 修订日期：2018-11-22 |
| DOI： |
| 中文关键词: 跌倒识别 表面肌电信号 三轴加速度 支持向量机 模式识别 |
| 英文关键词: Fall recognition Surface electromyography signal Triaxial acceleration Support vector machine Pattern recognition |
| 基金项目:国家自然科学基金项目（61473112，61673158）,国家重点基础研究发展计划（No.2017YFB1401200） |
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| 中文摘要: |
| 目的：针对老人易跌倒和跌倒过后可能产生严重后果这一现实问题，进一步提高跌倒识别的准确性。
方法：提出基于表面肌电和加速度信号融合的跌倒识别算法，首先采集股直肌，股内侧肌，胫骨前肌和腓肠肌的表面肌电信号以及位于腰部的三轴加速度信号作为实验数据，然后利用滑动窗口法提取表面肌电和加速度信号的均方根值，最后针对人体日常活动和跌倒的运动特征，构建了支持向量机的分类器。
结果：实验数据共计240组数据，包括3种日常活动和向前跌倒，其中120组数据作为训练集，另外120组数据作为测试集。对4种动作进行识别实验，算法的准确度为93.71%、灵敏度为92.5%、特异度为100%，达到了良好的分类效果。
结论：基于支持向量机的表面肌电信号和加速度融合的跌倒识别算法分类效果良好，对于老人跌倒防护具有现实意义。 |
| 英文摘要: |
| Objective: Aiming at the fact that the elderly are prone to fall and may have serious consequences after falling, the accuracy of fall recognition is further improved.
Methods: A fall recognition algorithm based on surface electromyography and acceleration fusion signals is proposed. Firstly, the surface electromyography signals of the rectus femoris, femoral medial, tibialis anterior and gastrocnemius muscles and triaxial acceleration signals at the waist were collected as experimental data. Then the root mean square values of the surface electromyography and acceleration signals were extracted by sliding window method. Finally, a classifier based on support vector machine is constructed for the daily activities of the human body and the motion characteristics of the fall.
Results: The experimental data totaled 240 sets of data, including 3 daily activities and forward fall, of which 120 sets of data were used as training sets and 120 sets of data were used as test sets. The recognition experiments were performed on four kinds of motions. The accuracy of the algorithm is 93.71%, the sensitivity is 92.5%, and the specificity is 100%, which achieves a good classification effect.
Conclusions: The fall recognition algorithm based on SVM combined sEMG and acceleration has a good classification effect, which is of practical significance for the elderly fall protection. |
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