  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
