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- Title
Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare.
- Authors
Liang, Yunji; Zhou, Xingshe; Yu, Zhiwen; Guo, Bin
- Abstract
Activity recognition plays an important role for pervasive healthcare such as health monitoring, assisted living and pro-active services. Despite of the continuous and transparent sensing with various built-in sensors in mobile devices, activity recognition on mobile devices for pervasive healthcare is still a challenge due to the constraint of resources, such as battery limitation, computation workload, etc. Keeping in view the demand of energy-efficient activity recognition, we propose a hierarchical method to recognize user activities based on a single tri-axial accelerometer in smart phones for health monitoring. Specifically, the contribution of this paper is two-fold. First, it is demonstrated that the activity recognition based on the low sampling frequency is feasible for the long-term activity monitoring. Second, this paper presents a hierarchical recognition scheme. The proposed algorithm reduces the opportunity of usage of time-consuming frequency-domain features and adjusts the size of sliding window to improve recognition accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm, with more than 85 % recognition accuracy rate for 11 activities and 3.2 h extended battery life for mobile phones. Our energy efficient recognition algorithm extends the battery time for activity recognition on mobile devices and contributes to the health monitoring for pervasive healthcare.
- Subjects
ENERGY consumption; HUMAN activity recognition; MEDICAL care; MOBILE apps; ACCELEROMETERS
- Publication
Mobile Networks & Applications, 2014, Vol 19, Issue 3, p303
- ISSN
1383-469X
- Publication type
Article
- DOI
10.1007/s11036-013-0448-9