Accessibility settings

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

Wearables and MHealth Reviews (332) mHealth for Data Collection and Research (1288) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1850) Dementia and Cognitive Decline (767) Wearable Devices and Sensors (1245) Cognitive and Neurorehabilitation (393) mHealth for Rehabilitation (406) Falls and Emergency Detection with Sensors and other Technologies (64) Artificial Intelligence (2458) Neurorehabilitation and Technology (34)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67265, first published .
Elderly woman with wearable health monitor and digital circuit background

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 (11) 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
  4. Şirzai H, Gökhan Y, Yavuzer G, Argunsah H. Sensor-Derived Trunk Stability and Gait Recovery: Evidence of Neuromechanical Associations Following Intensive Robotic Rehabilitation. Sensors 2026;26(2):573 View
  5. Castelli L, Iacovelli C, Malizia A, Loreti C, Biscotti L, Caliandro P, Bentivoglio A, Calabresi P, Giovannini S. Assessment of Fall Risk in Neurological Disorders and Technology: Relationship Between Silver Index and Gait Analysis. Sensors 2026;26(3):840 View
  6. Devito F, Gattulli V, Impedovo D. Care-MOVE: A Smartphone-Based Application for Continuous Monitoring of Mobility, Environmental Exposure and Cognitive Status in Older Patients. Applied Sciences 2026;16(3):1549 View
  7. Bai T, Jiang K, Yu Y, Qie S, Wang C, Wang B, Zhang W. A Review of Research on the Applications of Large Models in Each Functional Module of the Entire Rehabilitation Process. Future Internet 2026;18(2):95 View
  8. Mayo N, Abou-Sharkh A, Dawes H, Donkers S, Gillis C, Goulding K, Hill E, Mate K, Tomita Y. Discriminating Between Fallers and Non-Fallers Using Kinematic Data from the Heel2Toe™ Wearable Sensor. Sensors 2026;26(5):1716 View
  9. Alqurashi A, Alharthi A, Alammar M, Aldosari N, Al Ayidh A. Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning. Scientific Reports 2026;16(1) View

Books/Policy Documents

  1. JAGTAP S, PATIL A. AI‐driven Healthcare Innovations. View

Conference Proceedings

  1. Sun Y, Wan H, Li X, Zhang F, Wu W, Zhang H. Proceedings of the 2026 10th International Conference on Artificial Intelligence, Automation and Control Technologies. Cross-Locomotion Task Generalization in sEMG-Based Gait Phase Prediction Using Artificial Neural Networks View