| Autor: |
De Rossi, S. M. M., Crea, S., Donati, M., Rebersek, P., Novak, D., Vitiello, N., Lenzi, T., Podobnik, J., Munih, M., Carrozza, M. C. |
| Zdroj: |
2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics & Biomechatronics (BioRob); 1/ 1/2012, p361-366, 6p |
| Abstrakt: |
We present an automated gait segmentation method based on the analysis of foot plantar pressure patterns elaborated from two wireless pressure-sensitive insoles. The 64 pressure signals recorded by each device are elaborated to extract 10 feature variables which are used to segment the gait cycle into 6 sub-phases following a simplified version of Perry's gait model. The method is based on a Hidden Markov Model with a minimum phase length constraint and a univariate Gaussian emission model, which is decoded using a classic Viterbi algorithm. The method is tested on a pool of 5 healthy young subjects walking at two different speeds, through a leave-one-out cross-subject validation. The results show that the method is highly effective, yielding to an average performance of about 95% of correct phase classification, and 85 to 90% of phase transitions detected inside an acceptance window of 50ms. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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