Name
Abstract | ||
|---|---|---|
Mobile activity recognition focuses on inferring current user activities by leveraging sensory data available on today׳s sensor rich mobile phones. Supervised learning with static models has been applied pervasively for mobile activity recognition. In this paper, we propose a novel phone-based dynamic recognition framework with evolving data streams for activity recognition. The novel framework incorporates incremental and active learning for real-time recognition and adaptation in streaming settings. While stream evolves, we refine, enhance and personalise the learning model in order to accommodate the natural drift in a given data stream. Extensive experimental results using real activity recognition data have evidenced that the novel dynamic approach shows improved performance of recognising activities especially across different users. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.09.074 | Neurocomputing |
Keywords | Field | DocType |
Ubiquitous computing,Mobile application,Activity recognition,Stream mining,Incremental learning,Active learning | Data stream mining,Activity recognition,Active learning,Data stream,Computer science,Incremental learning,Supervised learning,Phone,Artificial intelligence,Ubiquitous computing,Machine learning | Journal |
Volume | ISSN | Citations |
150 | 0925-2312 | 24 |
PageRank | References | Authors |
0.70 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahraa Said Abdallah | 1 | 88 | 6.20 |
| Mohamed Medhat Gaber | 2 | 1081 | 71.17 |
| Bala Srinivasan | 3 | 1076 | 191.20 |
| Shonali Priyadarsini Krishnaswamy | 4 | 1439 | 104.01 |
| AbdallahZahraa Said | 5 | 24 | 0.70 |
| GaberMohamed Medhat | 6 | 24 | 0.70 |