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

Mobile Health (mhealth) (2939) Wearable Devices and Sensors (888) Portable and Mobile Technologies for Rehabilitation (151) Frailty Detection, Assessment and Prediction (28) Usability and Technology Use Studies with Elder Subjects (176) Telehealth and Telemonitoring (1671) Mobile Devices and Apps for Seniors and Healthy Aging (167)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29884, first published .
Accuracy and Diversity of Wearable Device–Based Gait Speed Measurement Among Older Men: Observational Study

Accuracy and Diversity of Wearable Device–Based Gait Speed Measurement Among Older Men: Observational Study

Accuracy and Diversity of Wearable Device–Based Gait Speed Measurement Among Older Men: Observational Study

Authors of this article:

Min-gu Kang1 Author Orcid Image ;   Seong-Ji Kang2, 3 Author Orcid Image ;   Hye-Kang Roh3 Author Orcid Image ;   Hwa-Young Jung3 Author Orcid Image ;   Sun‑wook Kim4 Author Orcid Image ;   Jung-Yeon Choi4 Author Orcid Image ;   Kwang-il Kim4, 5 Author Orcid Image
Article Authors Cited by (3) Tweetations (5) Metrics

Journals

  1. Dlima S, Shevade S, Menezes S, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR Bioinformatics and Biotechnology 2022;3(1):e39618 View
  2. Biswas N, Chakrabarti S, Jones L, Ashili S. Smart wearables addressing gait disorders: A review. Materials Today Communications 2023;35:106250 View
  3. Turimov Mustapoevich D, Kim W. Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey. Healthcare 2023;11(18):2483 View