Toward Device-free and User-independent Fall Detection Using Floor Vibration
Open Access
- 21 February 2023
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Sensor Networks
- Vol. 19 (1), 1-20
- https://doi.org/10.1145/3519302
Abstract
The inevitable aging trend of the world’s population brings a lot of challenges to the health care for the elderly. For example, it is difficult to guarantee timely rescue for single-resided elders who fall at home. Under this circumstance, a reliable automatic fall detection machine is in great need for emergent rescue. However, the state-of-the-art fall detection systems are suffering from serious privacy concerns, having a high false alarm, or being cumbersome for users. In this paper, we propose a device-free fall detection system, namely G-Fall, based on floor vibration collected by geophone sensors. We first decompose the falling mode and characterize it with time-dependent floor vibration features. By leveraging Hidden Markov Model (HMM), our system is able to detect the fall event precisely and achieve user-independent detection. It requires no training from the elderly but only an HMM template learned in advance through a small number of training samples. To reduce the false alarm rate, we propose a novel reconfirmation mechanism using Energy-of-Arrival (EoA) positioning to assist in detecting the human fall. Extensive experiments have been conducted on 24 human subjects. On average, G-Fall achieves a 95.74% detection precision on the anti-static floor and 97.36% on the concrete floor. Furthermore, with the assistance of EoA, the false alarm rate is reduced to nearly 0%.Keywords
Funding Information
- China NSFC (U2001207, 61872248, 61872246)
- Guangdong NSF (2017A030312008)
- Shenzhen Science and Technology Foundation (ZDSYS20190902092853047)
- DEGP (2019KCXTD005, R2020A045)
- Guangdong “Pearl River Talent Recruitment Program” (2019ZT08X603)
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