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Wearables and MHealth Reviews (704) mHealth for Data Collection and Research (1772) mHealth in a Clinical Setting (1197) Evaluation and Research Methodology for mHealth (407) Fitness Trackers and Smart Pedometers/Accelerometers (805) Machine Learning (3307) Digital Biomarkers and Digital Phenotyping (467) Artificial Intelligence (4944)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60521, first published .
Smartwatch displaying ECG with medical icons: heart, brain, skeleton, ambulance.

The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review

The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review

Authors of this article:

Ricardo Smits Serena1, 2 Author Orcid Image ;   Florian Hinterwimmer1, 2 Author Orcid Image ;   Rainer Burgkart1 Author Orcid Image ;   Rudiger von Eisenhart-Rothe1 Author Orcid Image ;   Daniel Rueckert2 Author Orcid Image
Article Authors Cited by (24) Tweetations Metrics

Journals

  1. Kita K, Kaito T. Artificial intelligence in spine research: A multimodal perspective beyond imaging. Spine Research 2025;1(1):7 View
  2. Pedroso A, Schwamm L, Khera R. Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data. Stroke 2026;57(2):526 View
  3. Valle C, Praetorius A. Diagnostik und konservative Therapie der Gonarthrose. Arthroskopie 2025;38(6):498 View
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  7. Yokoyama R, Hayasaka T, Harada T, Huang S, Yarimizu K, Yokoyama M, Kawamae K. Proof-of-Concept of IMU-Based Detection of ICU-Relevant Agitation Motion Patterns in Healthy Volunteers. Bioengineering 2026;13(2):164 View
  8. Smits Serena R, Hirschmann M, Matziolis G, von Eisenhart‐Rothe R, Rueckert D, Hinterwimmer F, Valle C. Deep learning of wrist accelerometry from UK Biobank data identifies early movement signatures of knee osteoarthritis up to 5 years before diagnosis. Knee Surgery, Sports Traumatology, Arthroscopy 2026;34(4):1524 View
  9. Wądrzyk Ł, Staszkiewicz R, Sokołowski K, Strzała M, Badzińska J, Żegleń M, Kryst Ł. Exploring associations between energetic and anthropometric characteristics with front crawl technique in young male swimmers using IMU sensors: a preliminary study. Scientific Reports 2026;16(1) View
  10. Mejía-González E, Castro-Perez M, Villarreal-Reyes S, Olguín-Tiznado J, Galaviz-Mosqueda A, Camargo-Wilson C, Cano-Gutiérrez J, García-Alcaraz J, Rodríguez-Serrato C. Application of TRIZ Methodological Tools for Wearable Device Design Using Low-Cost Off-the-Shelf Sensors. Applied Sciences 2026;16(11):5270 View
  11. Pegorini C, Cattaneo D, Meotti M, Baglio F, Mannini A, Cordani C, Gervasoni E. Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies. Journal of Neurology 2026;273(6) View
  12. Wei Z, Wider W, Wu C, Chan C, Xu Y, Wu H. Mapping artificial intelligence in older adult care: A bibliometric analysis. DIGITAL HEALTH 2026;12 View
  13. Xiong W, Zeng X, Sun J, Zhang Y, Pei C, Chan Y, Chen P, Ma Y. IMU-based rotator cuff injury recognition with varying configurations and combinations. Frontiers in Physiology 2026;17 View
  14. Caprioli L, Romagnoli C, Della Loggia L, Najlaoui A, Campoli F, Cariati I, Edriss S, Bonanni R, Paté A, Chadefaux D, Tancredi V, Padua E, Annino G, Bonaiuto V. The use of inertial sensors in sports: A bibliometric network and cluster analysis. Smart Health 2026;41:100690 View
  15. Amici C, Bussola R, Pollet J, Gobbo M, Buraschi R. Automatic identification of postural transitions using a single inertial measurement unit and dynamic time warping: a pilot study in healthy individuals and people with Parkinson’s disease. Frontiers in Bioengineering and Biotechnology 2026;14 View
  16. Dr Uttam Mande , Sammy Mande , Dr. Dimpi Phukan(PT) , S.Gnanasaravanan , Pallab Paul . Transforming Orthopedic Care Through Emerging Technologies: The Role of Artificial Intelligence, Smart Implants, and Digital Biomarkers in Precision Musculoskeletal Medicine. PAIN, JOINTS, SPINE 2026;16(7S):33 View
  17. Oladipo A. Failure mechanisms in electrochemical point-of-care sensors: biofouling, interfacial degradation, and signal drift. Analytical Methods 2026;18(31):6559 View
  18. Gatica-Rojas V, Vidal-Silva C. Bridging Health Sciences and Engineering Through the Interdisciplinary Development of a Proof-of-Concept Wireless Telerehabilitation Prototype: A Preliminary Laboratory and Usability Study. Applied Sciences 2026;16(16):8277 View
  19. Ely M, Ahmadi A, Shariq S, Duchonova A, Filo R, Kastovska B, Zach O, Jimison H, Elavsky S. Use of mHealth interventions to enhance self-regulation and self-efficacy in individuals living with obesity: Scoping review. DIGITAL HEALTH 2026;12 View
  20. Kalathoti R, Mohanty S. Blockchain-enabled hybrid CNN–Transformer framework with Integrated Gradients for Parkinson’s disease detection. Egyptian Informatics Journal 2026;35:101034 View
  21. Mikołajewska E, Rogalla-Ładniak U, Masiak J, Panas E, Mikołajewski D. Agentic AI-Enabled Digital Twins for Intelligent Non-Destructive Testing of 3D-Printed Rehabilitation Equipment—A Narrative Review. Applied Sciences 2026;16(18):9001 View
  22. Veran N, Jamri A, Mohd Noor A. Smart Insole with Deep Learning for Real-Time Classification of Gait Abnormalities. Journal of Engineering Technology and Applied Physics 2026;8(2):9 View

Books/Policy Documents

  1. Barabanova E, Vytovtov K, Dvoretsky A. Distributed Computer and Communication Networks. View

Conference Proceedings

  1. Vasisht V, V H, Yadav L, Giriyapur L, K A, Modi S. 2026 11th International Conference on Machine Learning Technologies (ICMLT). Energy Optimized Feature Engineering for Parkinson's Disease Detection Using Wearable Sensor Data View