Active Learning Enabled Activity Recognition
| dc.contributor.author | Hossain, H M Sajjad | |
| dc.contributor.author | Roy, Nirmalya | |
| dc.contributor.author | Khan, Md Abdullah Al Hafiz | |
| dc.date.accessioned | 2018-09-04T18:39:55Z | |
| dc.date.available | 2018-09-04T18:39:55Z | |
| dc.date.issued | 2016-04-21 | |
| dc.description | © 2016 IEEE, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) | en_US |
| dc.description.abstract | Activity recognition in smart environment has been investigated rigorously in recent years. Researchers are enhancing the underlying activity discovery and recognition process by adding various dimensions and functionalities. But one significant barrier still persists which is collecting the ground truth information. Ground truth is very important to initialize a supervised learning of activities. Due to a large variety in number of Activities of Daily Living (ADLs), acknowledging them in a supervised way is a non-trivial research problem. Most of the previous researches have referenced a subset of ADLs and to initialize their model, they acquire a vast amount of informative labeled training data. On the other hand to collect ground truth and differentiate ADLs, human intervention is indispensable. As a result it takes an immense effort and raises privacy concerns to collect a reasonable amount of labeled data. In this paper, we propose to use active learning to alleviate the labeling effort and ground truth data collection in activity recognition pipeline. We investigate and analyze different active learning strategies to scale activity recognition and propose a dynamic k-means clustering based active learning approach. Experimental results on real data traces from a retirement community-(IRB #HP-00064387) help validate the early promise of our approach. | en_US |
| dc.description.sponsorship | This work is supported by UMB-UMBC Research and Innovation Partnership grant. | en_US |
| dc.description.uri | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7456524&isnumber=7456493 | en_US |
| dc.format.extent | 9 PAGES | en_US |
| dc.genre | conference papers and proceedings preprints | en_US |
| dc.identifier | doi:10.13016/M2CJ87P8B | |
| dc.identifier.citation | H. M. S. Hossain, N. Roy and M. A. A. H. Khan, "Active learning enabled activity recognition," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, NSW, 2016, pp. 1-9. | en_US |
| dc.identifier.uri | 10.1109/PERCOM.2016.7456524 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11215 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author. | |
| dc.subject | Uncertainty | en_US |
| dc.subject | Labeling | en_US |
| dc.subject | Smart homes | en_US |
| dc.subject | Crowdsourcing | en_US |
| dc.subject | Data models | en_US |
| dc.subject | Wearable sensors | en_US |
| dc.subject | Adaptation models | en_US |
| dc.subject | Mobile Pervasive & Sensor Computing Lab | en_US |
| dc.title | Active Learning Enabled Activity Recognition | en_US |
| dc.type | Text | en_US |