Abstract
Microwave-based radar sensors are increasingly been used for healthcare and security applications. The software defined implementation of the radars allows fall detection and classification of different types of motions enabling elderly care and monitoring without privacy invading cameras. In addition, such radar sensor allow seeing through visually opaque materials suitable for security applications. This paper investigates the use of micro-Doppler signatures of slowly moving objects to localize and detect and classify human micro-motions. Using the NI SDRs, we measure micro-Doppler signatures of various human motion scenarios. Thereafter, the micro Doppler signatures' data is augmented before being used to train a convolutional neural network that detects and identifies the fall events.
| Original language | English |
|---|---|
| Title of host publication | 17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781665401203 |
| DOIs | |
| State | Published - Aug 2021 |
| Externally published | Yes |
| Event | 17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021 - Virtual, Osaka, Japan Duration: 30 Aug 2021 → 31 Aug 2021 |
Publication series
| Name | 17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021 - Proceedings |
|---|
Conference
| Conference | 17th IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2021 |
|---|---|
| Country/Territory | Japan |
| City | Virtual, Osaka |
| Period | 30/08/21 → 31/08/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Fall Detection
- Micro Doppler signatures
- Software defined radio
- convolutional neural network (CNN)
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Signal Processing