Sharif Digital Repository

Control of powered lower limb prostheses has a locomotion mode-dependent structure which demands a pattern recognizer that can classify the current locomotion mode and also detect transitions between them in an appropriate time. In order to achieve this goal, this paper presents a Fuzzy sequential locomotion mode recognition system to classify daily locomotion modes including level- walking, stair climbing, slope walking, standing and sitting using low-cost mechanical sensors. Since these signals have a quasi-periodic nature, using sequential pattern recognition tools, such as Hidden Markov Model(HMM) improves the recognition performance considering they use sequences of information to make...