Abstract
Analyzing the human gait and obtaining the walking patterns can be an important biometric signature through which one could confirm an individual’s identity. In this paper, a nonvision-based approach using rotation sensor has been applied to acquire the oscillations from eight major joints of human body. These joints are, both the shoulders, elbows which constitute the upper body, and both hips and knees, which constitute the lower body. The gait patterns (from these eight oscillations) for male and female were obtained for different gait speeds varying from 3 to 5 km/h. The 3-km/h data was used as reference gait speed for training to classify the data at other gait speeds (4 and 5 km/h). This speed invariant human gait classification was done using a naïve Bayesian classifier along with applying Euclidean distance method and K-nearest neighbor technique. We have achieved encouraging classification results with those techniques.
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Nandy, A., Bhowmick, S., Chakraborty, P., Nandi, G.C. (2014). A Sensor-Based Technique for Speed Invariant Human Gait Classification. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_53
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DOI: https://doi.org/10.1007/978-81-322-1665-0_53
Publisher Name: Springer, New Delhi
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