Locomotion Mode Classification Using a Wearable Capacitive Sensing System
Locomotion Mode Classification Using a Wearable Capacitive Sensing System
复制标题
使用可穿戴电容传感系统进行运动模式分类
DOI：
10.1109/tnsre.2013.2262952
复制
发表时间：
2013-09-01
影响因子：
4.9
通讯作者：
Wang, Long
中科院分区：
文献类型：
--
作者：
Chen, Baojun;Zheng, Enhao;Wang, Long
运动模式分类是动力假肢控制的一个重要方面。我们提出了一种可穿戴电容传感系统识别运动模式作为一种替代解决方案，流行的肌电图（EMG）为基础的系统，旨在克服后者的缺点。八个健全的主体和五个transtibial截肢者被招募的六种常见的运动模式的自动分类。该系统测量了来自小腿、大腿或两者的十个通道的电容信号。该系统采用相位相关线性判别分析分类器和选定的时域特征，对正常人和截肢者的分类准确率分别为93.6% ±0.9%和93.4% ±0.8%。分类精度与基于EMG的系统相当。更重要的是，我们验证了电容传感固有的神经机械延迟不会影响分类决策的及时性，因为系统类似于基于EMG的系统，可以在步态周期中做出多个判断。实验结果还表明，从大腿单独的电容信号是足够的模式分类健全和transtibial科目。我们的研究表明，电容传感是一个很有前途的替代肌电传感的实时控制的动力下肢假肢。
Locomotion mode classification is one of the most important aspects for the control of powered lower-limb prostheses. We propose a wearable capacitive sensing system for recognizing locomotion modes as an alternative solution to popular electromyography (EMG)-based systems, aiming to overcome drawbacks of the latter. Eight able-bodied subjects and five transtibial amputees were recruited for automatic classification of six common locomotion modes. The system measured ten channels of capacitance signals from the shank, the thigh, or both. With a phase-dependent linear discriminant analysis classifier and selected time-domain features, the system can achieve a satisfactory classification accuracy of 93.6% ±0.9% and 93.4% ±0.8% for able-bodied subjects and amputee subjects, respectively. The classification accuracy is comparable with that of EMG-based systems. More importantly, we verify that neuro-mechanical delay inherent in capacitive sensing does not affect the timeliness of classification decisions as the system, similar to EMG-based systems, can make multiple judgments during a gait cycle. Experimental results also indicate that capacitance signals from the thigh alone are sufficient for mode classification for both able-bodied and transtibial subjects. Our investigations demonstrate that capacitive sensing is a promising alternative to myoelectric sensing for real-time control of powered lower-limb prostheses.