Abstract
This paper presents a walking pattern detection method for a smart rollator. The method detects the rollator user’s lower extremities from the depth data of an RGB-D camera. It then segments the 3D point data of the lower extremities into the leg and foot data points, from which a skeletal system with 6 skeletal points and 4 rods is extracted and used to represent a walking gait. A gait feature, comprising the parameters of the gait shape and gait motion, is then constructed to describe a walking state. K-means clustering is employed to cluster all gait features obtained from a number of walking videos into 6 key gait features. Using these key gait features, a walking video sequence is modeled as a Markov chain. The stationary distribution of the Markov chain represents the walking pattern. Three Support Vector Machines (SVMs) are trained for walking pattern detection. Each SVM detects one of the three walking patterns. Experimental results demonstrate that the proposed method has a better performance in detecting walking patterns than seven existing methods.
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Acknowledgements
This work was supported by the National Institute of Child Health and Human Development, the National Institute of Nursing Research, and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award R01NR016151. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
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Zhang, H., Ye, C. RGB-D camera based walking pattern recognition by support vector machines for a smart rollator. Int J Intell Robot Appl 1, 32–42 (2017). https://doi.org/10.1007/s41315-016-0002-6
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DOI: https://doi.org/10.1007/s41315-016-0002-6