Name
Abstract | ||
|---|---|---|
Reducing the power consumption of wearable sensors is a very important issue in relation to the device usage time and form factor. However, continuous wireless communication to analyze the measured signal in real-time significantly increases the power consumption of the wearable sensor. In this study, we propose a wearable vibration sensor that operates with extremely low power through an embedded signal classifier, which exhibits high accuracy and low calculation load. We demonstrate cough detection through the proposed sensor system. The result exhibits an accuracy of 93.0%, which is 24.3% higher than the conventional embedded classification algorithm. Also, the proposed approach reduces the average power consumption of the wearable sensor by 8.8 times. Clinical Relevance-People can measure the vibration from the body using an ultra-low-power wearable sensor. It provides a solution to automatically monitor cough symptoms in numerous patients. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EMBC48229.2022.9871084 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Inyeol Yun | 1 | 0 | 0.34 |
| Jinpyeo Jeung | 2 | 0 | 0.34 |
| Yoonsik Kim | 3 | 11 | 3.87 |
| Yonghun Song | 4 | 0 | 0.34 |
| Yoonyoung Chung | 5 | 0 | 0.34 |