Identification and State Evaluation Based on Frontal Views of Walkers
Akio Nozawa*, Tota Mizuno*, Masafumi Uchida**,
Hisaya Tanaka***, and Hideto Ide*
*Department of Electronics and Electric Engineering, College of Science and Engineering, Aoyama Gakuin University, 5-10-1 Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan
**Department of Electronic Engineering, The University of Electro-communications, 1-5-1 Chofu-Ga-Oka, Chofu, Tokyo 182-8585, Japan
***Kogakuin University, 1-24-2 Nishi-shinjuku, Shinjuku-ku, Tokyo 163-8677, Japan
Walking, a basic human movement, is said to reflect an individual’s mental and physical state as well as individual characteristics. We discuss identifying walking gaits and evaluating mental and physical states using lower-limb trajectories extracted from frontal videos of walkers. We studied 3 mental and physical states – “normal,” “hurried,” and “tired” – using neural networks (NNs) were used for individual identification and state evaluation. From trajectories of the toes sampled from videos, we extracted static features (quantization) and dynamic features (displacement per unit time) and used these for teaching NNs. Experiments showed that trajectories showed features typical of individual walkers and their mental and physical states, with individual identification averaging 73% and evaluation 86.3%.
Hisaya Tanaka, and Hideto Ide, “Identification and State Evaluation Based on Frontal Views of Walkers,” J. Robot. Mechatron., Vol.18, No.1, pp. 59-66, 2006.
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