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

Falls and Emergency Detection with Sensors and other Technologies (28) mHealth for Data Collection and Research (912) Falls Prevention in the Elderly (64) Ubiquitous Health (uHealth) (116) Wearable Devices and Sensors (816) mHealth for Data Collection and Research (912) Wearable Devices and Sensors (816) Falls Prevention in the Elderly (64) Ubiquitous Health (uHealth) (116)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49587, first published .
Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis

Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis

Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis

Authors of this article:

Nahime Al Abiad1, 2 Author Orcid Image ;   Kimberley S van Schooten3, 4 Author Orcid Image ;   Valerie Renaudin2 Author Orcid Image ;   Kim Delbaere3, 4 Author Orcid Image ;   Thomas Robert1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Abiad N, Houdry E, El Khoury C, Renaudin V, Robert T. A method for calculating fall risk parameters from discrete stride time series regardless of sensor placement. Gait & Posture 2024;111:182 View
  2. Codina M, Castells-Rufas D, Torrelles M, Carrabina J. Technoeconomic Analysis for Deployment of Gait-Oriented Wearable Medical Internet-of-Things Platform in Catalonia. Information 2024;15(5):288 View