Acquiring in situ training data for context-aware ubiquitous computing applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
| Hi-index | 0.00 |
This paper deals with Activities of Daily Living (ADL) Monitoring System. The proposed system takes into account deploying in real home. The important issue in deployment is the noninvasiveness. That is, the user should not feel inconvenience. Therefore, our system has been developed by making use of FSR sensors and an optional small body-activity sensor. In particular, FSR sensor is a typical noninvasive sensor since it has a shape of film. In order to make a light-weight monitoring system, we use as small number of sensors as possible. And we adopt rule-based ADL inferring algorithms to avoid inconvenience in collecting training data for supervised learning. For the purpose of improving the accuracy of occupation/usage detection, we make FSR sensors into FSR array sensors. We evaluate the proposed system in laboratory and real home environment.