Details

Autor(en) / Beteiligte

• De Rossi, S. M. M.
• Crea, S.
• Donati, M.
• Rebersek, P.
• Novak, D.
• Vitiello, N.
• Lenzi, T.
• Podobnik, J.
• Munih, M.
• Carrozza, M. C.

Titel

Gait segmentation using bipedal foot pressure patterns

Ist Teil von

• 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2012, p.361-366

Ort / Verlag

IEEE

Erscheinungsjahr

2012

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.