@inproceedings{9e050f5755384a149a3be0c839d29bf9,
title = "Co-recognition of human activity and sensor location via compressed sensing in wearable body sensor networks",
abstract = "Human activity recognition using wearable body sensors is playing a significant role in ubiquitous and mobile computing. One of the issues related to this wearable technology is that the captured activity signals are highly dependent on the location where the sensors are worn on the human body. Existing research work either extracts location information from certain activity signals or takes advantage of the sensor location information as a priori to achieve better activity recognition performance. In this paper, we present a compressed sensing-based approach to co-recognize human activity and sensor location in a single framework. To validate the effectiveness of our approach, we did a pilot study for the task of recognizing 14 human activities and 7 on body-locations. On average, our approach achieves an 87.72\% classification accuracy (the mean of precision and recall).",
keywords = "Compressed Sensing, Human Activity Analysis, Sensor Localization, Wearable Device",
author = "Wenyao Xu and Mi Zhang and Sawchuk, \{Alexander A.\} and Majid Sarrafzadeh",
year = "2012",
doi = "10.1109/BSN.2012.14",
language = "English",
isbn = "9780769546988",
series = "Proceedings - BSN 2012: 9th International Workshop on Wearable and Implantable Body Sensor Networks",
pages = "124--129",
booktitle = "Proceedings - BSN 2012",
note = "9th International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2012 ; Conference date: 09-05-2012 Through 12-05-2012",
}