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Extracting walking trajectories at home from a capacitive proximity sensing floor

Abstract : Walking at home can provide valuable information about locomotor efficiency, anticipation of daily hazards and general well-being. Here, we present a multidisciplinary method to reconstruct locomotor trajectories while walking at home with a capacitive proximity sensing device — the SensFloor — which was installed in a real occupied apartment in the city center of Montpellier in France. Our recognition method is based on the spatio-temporal statistical probability of body location corresponding to sensors’ activation. The results led to the localization of the inhabitant in the two-dimensional floor space, and their tracking over a 24-hour period. More precisely, our technique enabled us to distinguish human-related behavior from the location of static objects. It also allowed us to successfully identify locomotor trajectories in a highly confined space, including those from two simultaneously walking individuals in different rooms. It allowed us to obtain valuable information on spatial behavior (trajectory, stationarity) but also on temporal behavior (occupancy time, walking duration). As this technique compensates for the already established low accuracy of capacitive sensors, our method offers innovative possibilities to study locomotor metrics at home using relatively inexpensive sensing technology.
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https://hal.mines-ales.fr/hal-03512496
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Soumis le : mardi 31 mai 2022 - 10:37:25
Dernière modification le : jeudi 13 octobre 2022 - 08:18:56

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Distributed under a Creative Commons Paternité 4.0 International License

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M. Sannier, Stefan Janaqi, V. Raducanu, V. Barysheva, H. Ait Haddou, et al.. Extracting walking trajectories at home from a capacitive proximity sensing floor. IEEE Sensors Journal, 2022, 22 (4), pp.1-1. ⟨10.1109/JSEN.2021.3139442⟩. ⟨hal-03512496⟩

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