Title :
A Noncontact Capacitive Sensing System for Recognizing Locomotion Modes of Transtibial Amputees
Author :
Enhao Zheng ; Long Wang ; Kunlin Wei ; Qining Wang
Author_Institution :
Intell. Control Lab., Peking Univ., Beijing, China
Abstract :
This paper presents a noncontact capacitive sensing system (C-Sens) for locomotion mode recognition of transtibial amputees. C-Sens detects changes in physical distance between the residual limb and the prosthesis. The sensing front ends are built into the prosthetic socket without contacting the skin. This novel signal source improves the usability of locomotion mode recognition systems based on electromyography (EMG) signals and systems based on capacitance signals obtained from skin contact. To evaluate the performance of C-Sens, we carried out experiments among six transtibial amputees with varying levels of amputation when they engaged in six common locomotive activities. The capacitance signals were consistent and stereotypical for different locomotion modes. Importantly, we were able to obtain sufficiently informative signals even for amputees with severe muscle atrophy (i.e., amputees lacking of quality EMG from shank muscles for mode classification). With phase-dependent quadratic classifier and selected feature set, the proposed system was capable of making continuous judgments about locomotion modes with an average accuracy of 96.3% and 94.8% for swing phase and stance phase, respectively (Experiment 1). Furthermore, the system was able to achieve satisfactory recognition performance after the subjects redonned the socket (Experiment 2). We also validated that C-Sens was robust to load bearing changes when amputees carried 5-kg weights during activities (Experiment 3). These results suggest that noncontact capacitive sensing is capable of circumventing practical problems of EMG systems without sacrificing performance and it is, thus, promising for automatic recognition of human motion intent for controlling powered prostheses.
Keywords :
biomechanics; biomedical telemetry; body sensor networks; capacitive sensors; electromyography; feature extraction; feature selection; medical control systems; medical signal detection; medical signal processing; prosthetics; quadratic programming; signal classification; skin; telemedicine; C-Sens performance evaluation; EMG quality; EMG signals; amputation level variation; automatic human motion intent recognition; capacitance signal consistency; electromyography; feature set selection; load bearing change; locomotion mode classification accuracy; locomotion mode recognition system usability; locomotive activity; mass 5 kg; muscle atrophy; noncontact capacitive sensing system; phase-dependent quadratic classifier; powered prosthesis control; prosthetic socket; residual limb-prosthesis physical distance change detection; sensing front end; shank muscle; signal source; skin contact; stance phase; stereotypical capacitance signal; swing phase; transtibial amputee locomotion mode recognition; Capacitive sensors; Electromyography; Motion detection; Prosthetics; Wearable sensors; Capacitive sensing; locomotion mode recognition; lower-limb prosthesis; noncontact wearable sensing; quadratic discriminant classifier;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2014.2334316