Fuzzy-Logic-Based Terrain Identification with Multisensor Fusion for Transtibial Amputees
Authors:
- 1 April 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ASME Transactions on Mechatronics
- Vol. 20 (2) , 618-630
Abstract
Terrain identification is essential for the control of robotic transtibial prostheses to realize smooth locomotion transitions. In this paper, we present a real-time fuzzy-logic-based terrain identification method with multisensor fusion. Five locomotion features, including the foot inclination angle at the first strike, the shank inclination angle at the first strike, foot strike sequence, the foot inclination angle at mid-stance, and the shank inclination angle at toe-off, are used to identify different terrains and terrain transitions. These features are measured by the fusion of two triaxis gyroscopes, two triaxis accelerometers, two force sensitive resistors, and a timer, which can be embedded into the prosthesis. Based on these features, a fuzzy-logic-based identification method is proposed to identify five terrains: level ground, stair ascent, stair descent, ramp ascent, and ramp descent. Moreover, a transition constraint function is developed to improve the identification performance. The execution time of the identification method is 0.79 ms ± 0.02 ms (mean ± standard error of mean) and continuous terrain identification results show that the method can be operated online in real time. The average identification accuracy of 98.74% ± 0.32% is obtained from experiments with six able-bodied and three amputee subjects during steady locomotion periods (no terrain transition). In locomotion transition periods, all the eight transitions we studied are correctly identified and the average identification delay is 9.06% ± 3.46% of one gait cycle.Keywords
Funding Information
- National Natural Science Foundation of China (61005082 and Grant 61020106005)
- Doctoral Fund of the Ministry of Education of China (20100001120005)
- 985 project of Peking University (3J0865600)
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