Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the rich sensory data that is available on today's smart phones and other wearable sensors. The state of the art in mobile activity recognition research has focused on traditional classification learning techniques. In this paper, we propose the Mobile Activity Recognition System (MARS) where for the first time the classifier is built on-board the mobile device itself through ubiquitous data stream mining in an incremental manner. The advantages of on-board data stream mining for mobile activity recognition are: i) personalisation of models built to individual users; ii) increased privacy as the data is not sent to an external site; iii) adaptation of the model as the user's activity profile changes. In our extensive experimental results using a recent benchmarking activity recognition dataset, we show that MARS can achieve similar accuracy when compared with traditional classifiers for activity recognition, while at the same time being scalable and efficient in terms of the mobile device resources consumption. MARS has been implemented on the Android platform for empirical evaluation.
|Publication status||Published - 3 Sep 2012|
|Event||Proceedings of the 14th International Conference on Data Warehousing and Knowledge Discovery - Vienna, Austria|
Duration: 3 Sep 2012 → 6 Sep 2012
|Conference||Proceedings of the 14th International Conference on Data Warehousing and Knowledge Discovery|
|Abbreviated title||DaWaK 2012|
|Period||3/09/12 → 6/09/12|