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Journal Article
Fall Recognition System to Determine the Point of No Return in Real?Time
- Authors
- Bae Sun Kim, Yong Ki Son, Joonyoung Jung, Dong-Woo Lee, Hyung Cheol Shin
- Issue Date
- 2021-09
- Citation
- Applied Sciences, v.11, no.18, pp.1-12
- ISSN
- 2076-3417
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Project Code
- 20HS4100, Development of A soft wearable suit using intelligent information and meta-material/structure technology for fall prediction and prevention, Son Yong Ki
- Abstract
- In this study, we collected data on human falls, occurring in four directions while walking or standing, and developed a fall recognition system based on the center of mass (COM). Fall data were collected from a lower?륿ody motion data acquisition device comprising five inertial measurement unit sensors driven at 100 Hz and labeled based on the COM?릒orm. The data were learned to classify which stage of the fall a particular instance belongs to. It was confirmed that both the rep-resentative convolutional neural network learning model and the long short?릘erm memory learning model were performed within a time of 10 ms on the embedded platform (Jetson TX2) and the recognition rate exceeded 94%. Accordingly, it is possible to verify the progress of the fall during the unbalanced and falling steps, which are classified by subdividing the critical step in which the real?릘ime fall proceeds with the output of the fall recognition model every 10 ms. In addition, it was confirmed that a real?릘ime fall can be judged by specifying the point of no return (PONR) near the point of entry of the falling down stage.
- KSP Keywords
- Convolution neural network(CNN), Data Acquisition(DAQ), Inertial Measurement Unit(IMU), Learning model, Motion Data, Neural network learning, Recognition System, Recognition model, Recognition rate, center of mass, data acquisition device
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
