Published on in Vol 22, No 4 (2020): April
![Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study](https://asset.jmir.pub/assets/cd5dd18c7bc2b80e649e4a850e136a5f.png 480w,https://asset.jmir.pub/assets/cd5dd18c7bc2b80e649e4a850e136a5f.png 960w,https://asset.jmir.pub/assets/cd5dd18c7bc2b80e649e4a850e136a5f.png 1920w,https://asset.jmir.pub/assets/cd5dd18c7bc2b80e649e4a850e136a5f.png 2500w)
Journals
- García E, Villar M, Fáñez M, Villar J, de la Cal E, Cho S. Towards effective detection of elderly falls with CNN-LSTM neural networks. Neurocomputing 2022;500:231 View
- Sczuka K, Schneider M, Schellenbach M, Kerse N, Becker C, Klenk J. Evaluating the Effect of Activity and Environment on Fall Risk in a Paradigm-Depending Laboratory Setting: Protocol for an Experimental Pilot Study. JMIR Research Protocols 2023;12:e46930 View
- Soni V, Yadav H, Bijrothiya S, Semwal V. CABMNet: An adaptive two-stage deep learning network for optimized spatial and temporal analysis in fall detection. Biomedical Signal Processing and Control 2024;96:106506 View