IEEE Trans Neural Syst Rehabil Eng. 2013 Sep;21(5):744-55. doi: 10.1109/TNSRE.2013.2262952. Epub 2013 May 17.
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.
运动模式分类是动力下肢假肢控制的最重要方面之一。我们提出了一种可穿戴电容感应系统，用于识别运动模式，作为一种替代流行的肌电（EMG）系统的解决方案，旨在克服后者的缺点。招募了 8 名健康受试者和 5 名胫骨截肢者，用于自动分类 6 种常见的运动模式。该系统从小腿、大腿或两者测量 10 个通道的电容信号。使用与相位相关的线性判别分析分类器和选定的时域特征，系统可以实现健康受试者和截肢受试者的分类准确率分别为 93.6%±0.9%和 93.4%±0.8%。分类准确率与基于 EMG 的系统相当。更重要的是，我们验证了电容感应固有的神经机械延迟不会影响分类决策的及时性，因为该系统类似于基于 EMG 的系统，可以在步态周期内进行多次判断。实验结果还表明，对于健康受试者和胫骨截肢者，仅从大腿采集的电容信号就足以进行模式分类。我们的研究表明，电容感应是一种有前途的替代肌电感应的方法，可用于动力下肢假肢的实时控制。
