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

Mobile Health (mhealth) (2884) mHealth for Data Collection and Research (943) Fitness Trackers and Smart Pedometers/Accelerometers (556) Augmented Reality (incl. Google Glass) Applications (90) Wearable Devices and Sensors (873) Prevention and Health Promotion (953) Falls and Emergency Detection with Sensors and other Technologies (33)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41220, first published .
Applying the UTAUT2 Model to Smart Eyeglasses to Detect and Prevent Falls Among Older Adults and Examination of Associations With Fall-Related Functional Physical Capacities: Survey Study

Applying the UTAUT2 Model to Smart Eyeglasses to Detect and Prevent Falls Among Older Adults and Examination of Associations With Fall-Related Functional Physical Capacities: Survey Study

Applying the UTAUT2 Model to Smart Eyeglasses to Detect and Prevent Falls Among Older Adults and Examination of Associations With Fall-Related Functional Physical Capacities: Survey Study

Authors of this article:

Justine Hellec1, 2 Author Orcid Image ;   Meggy Hayotte1 Author Orcid Image ;   Frédéric Chorin1, 3 Author Orcid Image ;   Serge S Colson1 Author Orcid Image ;   Fabienne d'Arripe-Longueville1 Author Orcid Image
Article Authors Cited by (2) Tweetations (6) Metrics

Journals

  1. Hellec J, Colson S, Jaafar A, Guérin O, Chorin F. A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups. Sensors 2024;24(5):1427 View
  2. Zhou Z, Liu Z, Liu Y, Zhao Y, Wang J, Zhang B, Xia Y, Zhang X, Li S. TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack method. DIGITAL HEALTH 2024;10 View