Authors:
Alessandro Masullo
;
Toby Perrett
;
Dima Damen
;
Tilo Burghardt
and
Majid Mirmehdi
Affiliation:
University of Bristol, BS8 1UB, Bristol, U.K.
Keyword(s):
Multi-sensory Fusion, Ambient Assisted Living, Silhouettes, Wearable Devices, Acceleration.
Abstract:
The majority of the Ambient Assisted Living (AAL) systems, designed for home or lab settings, monitor one participant at a time – this is to avoid the complexities of pre-fusion correspondence of different sensors since carers, guests, and visitors may be involved in real world scenarios. Previous work from (Masullo et al., 2020) presented a solution to this problem that involves matching video sequences of silhouettes to accelerations from wearable sensors to identify members of a household while respecting their privacy. In this work, we elevate this approach to the next stage by improving its architecture and combining it with a tracking functionality that makes it possible to be deployed in real-world homes. We present experiments on a new dataset recorded in participants’ own houses, which includes multiple participants visited by guests, and show an auROC score of 90.2%. We also show a novel first example of subject-tailored health monitoring measurement by applying our methodo
logy to a sit-to-stand detector to generate clinically relevant rehabilitation trends.
(More)