Energy harvesting modelling for self-powered fitness gadgets: a feasibility study. Issue 4 (4th July 2019)

• Record Type: Journal Article
• Authors: Zareei, Sophie; Deng, Jeremiah D.
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 34:Issue 4(2019)
• Page Start: 412
• Page End: 429
• Publication Date: 2019-07-04
• Subjects: Human motion energy -- energy harvesting -- fitness gadget; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2017.1410817
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract: The idea of employing scavenged energy from human motion to run electronic devices is attracting increased attention of many researchers worldwide. However, there is still limited knowledge of energy characteristics generated by human motions. Moreover, level of human activities varies during a day from sitting for several hours to running on a treadmill. This highlights a vital need for energy harvesting modelling. Hence, in this paper, we aim to investigate the feasibility of running a simple fitness gadget with scavenged energy from human motions. We analyse kinetic energy generated by human activities and develop energy harvesting modelling techniques which estimate the required time for a device to store needed energy between each active periods. Two large, real-world data sets are employed to obtain empirical results for different motion scenarios. We believe these results are informative to the design of energy harvesting fitness gadgets with sensing applications. Graphical Abstract: We consider two large data sets consist of acceleration signals of walking and human daily routines. Acceleration signals are converted to power in order to estimate potential energy that can be generated from human motions. We also propose energy harvesting modelling techniques for self-powered fitness gadgets. By considering potential application for fitness gears, these techniques are evaluated using statistical analysis and different motion scenarios.