This paper is in the following e-collection/theme issue:
Development and Evaluation of Research Methods, Instruments and Tools (452) Registered Report (577) mHealth for Data Collection and Research (941) Tools, Programs and Algorithms (254) Fitness Trackers and Smart Pedometers/Accelerometers (555) Wearable Devices and Sensors (863) Formative Evaluation of Digital Health Interventions (2324) Mobile Health (mhealth) (2858) Implantable Wearable Devices and Body Extensions; Smart Prostheses (5) Wearable Devices and Sensors (20)Published on in Vol 8 (2024)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/50035, first published
.
Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study
Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study
Authors of this article:
Felix Kluge1 ; Yonatan E Brand2 ; M Encarna Micó-Amigo3 ; Stefano Bertuletti4 ; Ilaria D'Ascanio5 ; Eran Gazit6 ; Tecla Bonci7 ; Cameron Kirk3 ; Arne Küderle8 ; Luca Palmerini5, 9 ; Anisoara Paraschiv-Ionescu10 ; Francesca Salis4 ; Abolfazl Soltani10 ; Martin Ullrich8 ; Lisa Alcock3, 11 ; Kamiar Aminian10 ; Clemens Becker12, 13 ; Philip Brown14 ; Joren Buekers15, 16, 17 ; Anne-Elie Carsin15, 16, 17 ; Marco Caruso4 ; Brian Caulfield18, 19 ; Andrea Cereatti4 ; Lorenzo Chiari5, 9 ; Carlos Echevarria3, 20 ; Bjoern Eskofier8 ; Jordi Evers21 ; Judith Garcia-Aymerich15, 16, 17 ; Tilo Hache1 ; Clint Hansen22 ; Jeffrey M Hausdorff6, 23, 24, 25, 26 ; Hugo Hiden14 ; Emily Hume27 ; Alison Keogh18, 19 ; Sarah Koch15, 16, 17 ; Walter Maetzler22 ; Dimitrios Megaritis27 ; Martijn Niessen21 ; Or Perlman2, 23 ; Lars Schwickert12 ; Kirsty Scott7 ; Basil Sharrack28, 29 ; David Singleton18, 19 ; Beatrix Vereijken30 ; Ioannis Vogiatzis27 ; Alison Yarnall3, 11, 14 ; Lynn Rochester3, 11, 14 ; Claudia Mazzà7 ; Silvia Del Din3, 11 ; Arne Mueller1Journals
- Brand Y, Kluge F, Palmerini L, Paraschiv-Ionescu A, Becker C, Cereatti A, Maetzler W, Sharrack B, Vereijken B, Yarnall A, Rochester L, Del Din S, Muller A, Buchman A, Hausdorff J, Perlman O. Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults. Scientific Reports 2024;14(1) View