Abstract
In the presented system to person fall detection a body-worn accelerometer is used to indicate a potential fall and a ceiling-mounted depth sensor is utilized to authenticate fall alert. In order to expand the observation area the depth sensor has been mounted on a pan-tilt motorized head. If the person acceleration is above a preset threshold the system uses a lying pose detector as well as examines a dynamic feature to authenticate the fall. Thus, more costly fall authentication is not executed frame-by-frame, but instead we fetch from a circular buffer a sequence of depth maps acquired prior to the fall and then process them to confirm fall alert. We show that promising results in terms of sensitivity and specificity can be obtained on publicly available UR Fall Detection dataset.
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Acknowledgment
This work was supported by Polish National Science Center (NCN) under a research grant 2014/15/B/ST6/02808 and a grant 11.11.230.124.
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Kepski, M., Kwolek, B. (2016). Fall Detection Using Body-Worn Accelerometer and Depth Maps Acquired by Active Camera. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_35
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DOI: https://doi.org/10.1007/978-3-319-32034-2_35
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