This paper is in the following e-collection/theme issue:

New Methods (1134) Design, Usability, and Evaluation of Research Instruments, Scales, and Measures (85) Falls and Emergency Detection with Sensors and other Technologies (57) Falls Prevention in the Elderly (101)

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

Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study

Authors of this article:

Kim Sarah Sczuka1 Author Orcid Image ;   Lars Schwickert1 Author Orcid Image ;   Clemens Becker1 Author Orcid Image ;   Jochen Klenk1, 2, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. 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
  2. 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
  3. 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

Conference Proceedings

  1. Yadav H, Gupta D, Soni V, Roy B. 2025 6th International Conference on Recent Advances in Information Technology (RAIT). A Novel Additive Attention-Based MICNN-BiLSTM Model for Fall Detection Using Wearable Inertial Sensors View