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

Wearables and MHealth Reviews (310) mHealth for Rehabilitation (352) Wearable Devices and Sensors (1133) mHealth for Data Collection and Research (1196) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1732) Falls and Emergency Detection with Sensors and other Technologies (57) Cognitive and Neurorehabilitation (349) Neurorehabilitation and Technology (32) Dementia and Cognitive Decline (696) Artificial Intelligence (2129)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67265, first published .
Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review

Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review

Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review

Authors of this article:

Mirjam Bonanno1 Author Orcid Image ;   Augusto Ielo1 Author Orcid Image ;   Paolo De Pasquale1 Author Orcid Image ;   Antonio Celesti2 Author Orcid Image ;   Alessandro Marco De Nunzio3 Author Orcid Image ;   Angelo Quartarone1 Author Orcid Image ;   Rocco Salvatore Calabrò1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Stenger R, Hozhabr Pour H, Teich J, Hein A, Fudickar S. Gait Event Detection and Gait Parameter Estimation from a Single Waist-Worn IMU Sensor. Sensors 2025;25(20):6463 View
  2. Oyetunji O, Rain A, Feris W, Eckert A, Zabihollah A, Abu Ghazaleh H, Priest J. Design of a Smart Foot–Ankle Brace for Tele-Rehabilitation and Foot Drop Monitoring. Actuators 2025;14(11):531 View
  3. Gattani A, Dixit S, Patil M, Gupta M, Navghane A, Hule O, Srinivasan K. Artificial intelligence for fall detection in older adults: A comprehensive survey of machine learning, deep learning approaches, and future directions. Ageing Research Reviews 2026;113:102948 View