Smart insole: a wearable system for gait analysis
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
See UV on your skin: an ultraviolet sensing and visualization system
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Proper running posture guide: a wearable biomechanics capture system
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Cost-effective activity recognition on mobile devices
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Sleep posture analysis using a dense pressure sensitive bedsheet
Pervasive and Mobile Computing
| Hi-index | 0.00 |
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).