Name
Abstract | ||
|---|---|---|
Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/PerCom45495.2020.9127356 | 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom) |
Keywords | DocType | ISSN |
Kinetic,Energy Harvester,Wearables,IoT,Context Detection,Energy Positive Sensing,Sensing Points,Signals | Conference | 2474-2503 |
ISBN | Citations | PageRank |
978-1-7281-4657-7 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandhu Muhammad Moid | 1 | 1 | 0.36 |
| Geissdoerfer Kai | 2 | 1 | 0.36 |
| Khalifa Sara | 3 | 1 | 0.36 |
| Raja Jurdak | 4 | 1069 | 92.45 |
| Portmann Marius | 5 | 1 | 0.36 |
| Brano Kusy | 6 | 60 | 8.48 |