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It is imperative to find the most accurate way to detect falls in elders to help mitigate the disastrous effects of such unfortunate injuries. In order to mitigate fall related accidents, we propose the Good-Eye System, an Internet of Things (IoT) enabled Edge Level Device which works when there is an orientation change detected by camera, and monitors physiological signal parameters. If the observed change is greater than the set threshold, the user is notified with information regarding a prediction of fall or a detection of fall, using LED lights. The Good-Eye System has a remote wall attached camera to monitor continuously the subject as long as the person is in a room along with a camera attached to a wearable to increase the accuracy of the model. The observed accuracy of the Good-Eye System as a whole is approximately 95%.
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Fall Detection via Inaudible Acoustic Sensing
The fall detection system is of critical importance in protecting elders through promptly discovering fall accidents to provide immediate medical assistance, potentially saving elders' lives. This paper aims to develop a novel and lightweight fall detection system by relying solely on a home audio device via inaudible acoustic sensing, to recognize fall occurrences for wide home deployment. In particular, we program the audio device to let its speaker emit 20kHz continuous wave, while utilizing a microphone to record reflected signals for capturing the Doppler shift caused by the fall. Considering interferences from different factors, we first develop a set of solutions for their removal to get clean spectrograms and then apply the power burst curve to locate the time points at which human motions happen. A set of effective features is then extracted from the spectrograms for representing the fall patterns, distinguishable from normal activities. We further apply the Singular Value Decomposition (SVD) and K-mean algorithms to reduce the data feature dimensions and to cluster the data, respectively, before input them to a Hidden Markov Model for training and classification. In the end, our system is implemented and deployed in various environments for evaluation. The experimental results demonstrate that our system can achieve superior performance for detecting fall accidents and is robust to environment changes, i.e., transferable to other environments after training in one environment.
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- Award ID(s):
- 2019511
- PAR ID:
- 10338351
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3478094
