| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 552036 | Decision Support Systems | 2014 | 10 Pages |
•Propose a practical multi-sensor activity recognition system for home-based care.•Collect a real data set from a group of elderly people using seven on-body sensors.•Conduct investigation on the effect of different sensor in human activity classification.•Evaluate different feature selection and classification techniques for activity recognition.
To cope with the increasing number of aging population, a type of care which can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor. We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy. The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care.
