Remote Automatic Fall Detection and Activity Monitoring using Smart Wearables

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dc.contributor.authorMoufawad El Achkar, Christopher
dc.contributor.authorJorge, João
dc.contributor.authorMuntané Calvo, Enric
dc.contributor.authorGerber, Mickael
dc.contributor.authorLemkaddem, Alia
dc.contributor.authorLemay, Mathieu
dc.contributor.authorVerjus, Christophe
dc.date.accessioned2025-11-11T15:37:44Z
dc.date.available2025-11-11T15:37:44Z
dc.date.issued2020
dc.description.abstractA timely alarm after a fall can save the faller's life and reduce the risk of debilitating injuries. In a connected world, wearable sensors offer a massive opportunity to accurately detect falls and send immediate alarms to family and healthcare providers. At CSEM, we have developed real-time embedded algorithms for unobtrusive fall detection focused on context and activity classification. These algorithms can detect falls more accurately while rejecting false positives. Our solutions target sensors embedded in non-stigmatizing widely available wearable devices.
dc.identifier.citationCSEM Scientific and Technical Report 2020, p. 96
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1809
dc.titleRemote Automatic Fall Detection and Activity Monitoring using Smart Wearables
dc.typeCSEM Report
dc.type.csemdivisionsBU-D
dc.type.csemresearchareasDigital Health
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