Probabilistic Sensor Fusion for Ambient Assisted Living
This work addresses the problem of improving health-care provision through sensor systems in homes, specifically for ambient assisted living, but it is incremental as it builds on existing Bayesian methods for sensor fusion.
The paper tackles the challenge of fusing heterogeneous sensor modalities for ambient assisted living by introducing Bayesian models for sensor fusion, enabling identification of the most useful modalities and features for specific activities and integrating location prediction and activity recognition into a single model, with results showing favorable performance compared to benchmark models on data from the SPHERE house.
There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home. Given a multiple-modality sensor platform with heterogeneous network connectivity, as is under development in the Sensor Platform for HEalthcare in Residential Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face specific challenges relating to the fusion of the heterogeneous sensor modalities. We introduce Bayesian models for sensor fusion, which aims to address the challenges of fusion of heterogeneous sensor modalities. Using this approach we are able to identify the modalities that have most utility for each particular activity, and simultaneously identify which features within that activity are most relevant for a given activity. We further show how the two separate tasks of location prediction and activity recognition can be fused into a single model, which allows for simultaneous learning an prediction for both tasks. We analyse the performance of this model on data collected in the SPHERE house, and show its utility. We also compare against some benchmark models which do not have the full structure,and show how the proposed model compares favourably to these methods