Recognizing Activities and Spatial Context Using Wearable Sensors
  We introduce a new dynamic model with the capability of recognizing both
activities that an individual is performing as well as where that ndividual is
located. Our model is novel in that it utilizes a dynamic graphical model to
jointly estimate both activity and spatial context over time based on the
simultaneous use of asynchronous observations consisting of GPS measurements,
and measurements from a small mountable sensor board. Joint inference is quite
desirable as it has the ability to improve accuracy of the model. A key goal,
however, in designing our overall system is to be able to perform accurate
inference decisions while minimizing the amount of hardware an individual must
wear. This minimization leads to greater comfort and flexibility, decreased
power requirements and therefore increased battery life, and reduced cost. We
show results indicating that our joint measurement model outperforms
measurements from either the sensor board or GPS alone, using two types of
probabilistic inference procedures, namely particle filtering and pruned exact
inference.
