A Locomotion Intent Prediction System Based on Multi-Sensor Fusion

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 7; pp. 12349 - 12369
• Main Authors: Chen, Baojun, Zheng, Enhao, Wang, Qining
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.07.2014; MDPI
• Subjects: Accuracy; Algorithms; Amputation; Artificial Limbs; Classifiers; Deterioration; Discriminant analysis; Electromyography; Electromyography - methods; Human mechanics; Humans; Leg - physiology; linear discriminant analysis; Locomotion; Locomotion - physiology; locomotion mode recognition; locomotion transition detection; lower-limb prosthesis; Prostheses; Prosthesis Design - instrumentation; Prosthetics; Recognition; sensor fusion; Sensors; Signal processing; Signal Processing, Computer-Assisted - instrumentation; Strategy; Thigh; Time use; Trains; Walking - physiology
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s140712349

Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.