We present an unsupervised approach for discovery of Activities of Daily Living (ADL) in a smart home. Activity discovery is an important enabling technology, for example to tackle the healthcare requirements of elderly people in their homes. The technique applied most often is supervised learning, which relies on expensive labelled data and lacks the flexibility to discover unseen activities. Building on ideas from text mining, we present a powerful topic model and a segmentation algorithm that can learn from unlabelled sensor data. The model has been evaluated extensively on datasets collected from real smart homes. The results demonstrate that this approach can successfully discover the activities of residents, and can be effectively used in a range of applications such as detection of abnormal activities and monitoring of sleep quality, among many others.
|Title of host publication||Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 15 Jul 2016|
- Jean Golding
Chen, Y., Diethe, T., & Flach, P. (2016). ADL™: A Topic Model for Recognition of Activities of Daily Living in a SmartHome. In S. Kambhampati (Ed.), Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (pp. 1404-1410). AAAI Press. http://www.ijcai.org/Abstract/16/202