Zhiming Yao

A FOOTPRINT TRACKING METHOD FOR GAIT ANALYSIS

Gait analysis is popular in many clinical and biomechanical applications, such as diagnosis of diabetic neuropathy, rehabilitation evaluation of stroke patients and performance measurement of sports training. With the rapid and in-depth development of flexible sensing technology, a large-area pressure-sensitive floor can be easily installed in many locations. Complex movement besides linear walking can be designed on large-area floors for gait analysis in clinical or sports research. To conduct those researches, as a basic step, a computational approach is necessary to track each footprint correctly during the movement process. A multi-stage methodology is proposed to solve two main subtasks in the tracking process: (1) the labeling of different footprints and (2) the detection of basic foot gestures in the movement process. The methodology consists of an initial clusters creating stage, a cluster labeling stage and an overlapped footprints separating process. Tai Chi Chuan, one of complex foot movements, was used as an example to evaluate the proposed approach. An overall accuracy of 99.07% for footprint labeling and 90.39% for basic foot gesture detecting were achieved by the method.

A novel biometrie recognition system based on ground reaction force measurements of continuous gait

A novel biométrie recognition system is designed in this paper, using ground reaction force (GRF) measurements of continuous gai t. Original GRF signals are combined in three directions Respectively. Waveform interpolation and align ment are performed to meet th e demand of feature extraction ba sed on wavelet packet (WP) decomposition, re-sampling approach is utilized to expand valid training sets. Features are s elected u sing a fuzzy set-based features election criterion. Classification is accomplished using a kernel-based support vector machine (SVM). The parameter tuning of the S VM classifier is performed using a grid searching method. The approach is tested on a database comprising GRF records obtained from 103 subjects. Comparative results demonstrate that re-sampling approach and waveform interpolation and alignment can improve the recognition accuracy.