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

Research Letter (37) Telehealth and Telemonitoring (1672)

Published on in Vol 24, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39190, first published .
Recognition of Gait Patterns in Older Adults Using Wearable Smartwatch Devices: Observational Study

Recognition of Gait Patterns in Older Adults Using Wearable Smartwatch Devices: Observational Study

Recognition of Gait Patterns in Older Adults Using Wearable Smartwatch Devices: Observational Study

Authors of this article:

Hyeon-Joo Kim1 Author Orcid Image ;   Hyejoo Kim1 Author Orcid Image ;   Jinyoon Park1 Author Orcid Image ;   Bumjo Oh2 Author Orcid Image ;   Seung-Chan Kim1 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

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  2. Yun S, Kim H, Ryu J, Kim S. Fine-Grained Motion Recognition in At-Home Fitness Monitoring with Smartwatch: A Comparative Analysis of Explainable Deep Neural Networks. Healthcare 2023;11(7):940 View
  3. Biswas N, Chakrabarti S, Jones L, Ashili S. Smart wearables addressing gait disorders: A review. Materials Today Communications 2023;35:106250 View
  4. Kim H, Lee H, Park J, Paillat L, Kim S. Vehicle Control on an Uninstrumented Surface With an Off-the-Shelf Smartwatch. IEEE Transactions on Intelligent Vehicles 2023;8(5):3366 View
  5. Park J, Kim C, Kim S. Enhancing Robustness of Viewpoint Changes in 3D Skeleton-Based Human Action Recognition. Mathematics 2023;11(15):3280 View
  6. Wang L, Zhou Z, Niu J, Peng J, Wang T, Hou X. Emerging innovations in portable chemical sensing devices: Advancements from microneedles to hydrogel, microfluidic, and paper-based platforms. Talanta 2024;278:126412 View