HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices
Abstract
Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80 to 95 percent, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR.
Recommended Citation
S. Khalifa et al., "HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices," IEEE Transactions on Mobile Computing, vol. 17, no. 6, pp. 1353 - 1368, Institute of Electrical and Electronics Engineers (IEEE), Jun 2018.
The definitive version is available at https://doi.org/10.1109/TMC.2017.2761744
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Accelerometers; Electric power utilization; Energy harvesting; Internet of things; Kinetic energy; Kinetics; Pattern recognition; Solar cells; Wearable technology; Activity recognition; Biomedical monitoring; Human activity recognition; Power demands; Wearable computing; Wearable sensors
International Standard Serial Number (ISSN)
1536-1233; 1558-0660
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jun 2018
Comments
The authors are grateful to the anonymous referees for their insightful comments and constructive suggestions which helped improve the quality of the manuscript significantly. The work of M. Hassan is supported by a Data61-UNSW collaborative research grant. The work of S. K. Das is partially supported by NSF grants under award numbers IIS-1404673 and IIP-1648907.