Abstract
A gait analysis technique to model user presences in an office scenario is presented in this chapter. In contrast with other approaches, we use unobtrusive sensors, i.e., an array of Kinect devices, to detect some features of interest. In particular, the position and the spatio-temporal evolution of some skeletal joints are used to define a set of gait features, which can be either static (e.g., person height) or dynamic (e.g., gait cycle duration). Data captured by multiple Kinects is merged to detect dynamic features in a longer walk sequence. The approach proposed here was been evaluated by using three classifiers (SVM, KNN, Naive Bayes) on different feature subsets.
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This work has been partially supported by the PO FESR 2007/2013 grant G73F11000130004 funding the SmartBuildings project.
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Maida, G., Morana, M. (2014). Gait Analysis Using Multiple Kinect Sensors. In: Gaglio, S., Lo Re, G. (eds) Advances onto the Internet of Things. Advances in Intelligent Systems and Computing, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-319-03992-3_12
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DOI: https://doi.org/10.1007/978-3-319-03992-3_12
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