No Need for a Lab: Towards Multi-Sensory Fusion for Ambient Assisted Living in Real-World Living Homes

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 [Masullo2020] 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 methodology to a sit-to-stand detector to generate clinically relevant rehabilitation trends.