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

Machine Learning (1400) Wearable Devices and Sensors (825) Decision Support for Health Professionals (1145) Clinical Information and Decision Making (1339) Osteoporosis and Osteoporosis Prevention (11) Falls Prevention in the Elderly (64) Falls and Emergency Detection with Sensors and other Technologies (28) Fitness Trackers and Smart Pedometers/Accelerometers (543)

Published on in Vol 10, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32724, first published .
Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

Authors of this article:

Moritz Kraus1 Author Orcid Image ;   Maximilian Michael Saller1 Author Orcid Image ;   Sebastian Felix Baumbach1 Author Orcid Image ;   Carl Neuerburg1 Author Orcid Image ;   Ulla Cordula Stumpf1 Author Orcid Image ;   Wolfgang Böcker1 Author Orcid Image ;   Alexander Martin Keppler1 Author Orcid Image
Article Authors Cited by (12) Tweetations (4) Metrics

Journals

  1. Subramaniam S, Faisal A, Deen M. Wearable Sensor Systems for Fall Risk Assessment: A Review. Frontiers in Digital Health 2022;4 View
  2. Fan S, Ye J, Xu Q, Peng R, Hu B, Pei Z, Yang Z, Xu F. Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty. Frontiers in Public Health 2023;11 View
  3. Velazquez-Diaz D, Arco J, Ortiz A, Pérez-Cabezas V, Lucena-Anton D, Moral-Munoz J, Galán-Mercant A. Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review. Journal of Medical Internet Research 2023;25:e47346 View
  4. Turimov Mustapoevich D, Kim W. Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey. Healthcare 2023;11(18):2483 View
  5. Leghissa M, Carrera Á, Iglesias C. Machine learning approaches for frailty detection, prediction and classification in elderly people: A systematic review. International Journal of Medical Informatics 2023;178:105172 View
  6. Xu S, Fang J, Hu X, Ngai E, Wang W, Guo Y, Leung V. Emotion Recognition From Gait Analyses: Current Research and Future Directions. IEEE Transactions on Computational Social Systems 2024;11(1):363 View
  7. Siva P, Wong A, Hewston P, Ioannidis G, Adachi J, Rabinovich A, Lee A, Papaioannou A. Automatic Radar-Based Step Length Measurement in the Home for Older Adults Living with Frailty. Sensors 2024;24(4):1056 View
  8. Ferreira A, Silva B, Gomes C, Fittipaldi E, Andrade A, Barbosa J. Relação entre medidas fornecidas por smartwatches e a identificação de síndrome da fragilidade em idosos: revisão de escopo. Revista Brasileira de Geriatria e Gerontologia 2024;27 View
  9. Ferreira A, Silva B, Gomes C, Fittipaldi E, Andrade A, Barbosa J. Relationship between measures provided by smartwatches and identification of frailty syndrome in older adults: a scoping review. Revista Brasileira de Geriatria e Gerontologia 2024;27 View
  10. Zhang W, Wang J, Xie F, Wang X, Dong S, Luo N, Li F, Li Y. Development and validation of machine learning models to predict frailty risk for elderly. Journal of Advanced Nursing 2024 View
  11. Wolff C, Steinheimer P, Warmerdam E, Dahmen T, Slusallek P, Schlinkmann C, Chen F, Orth M, Pohlemann T, Ganse B. Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study. Journal of Medical Internet Research 2024;26:e44948 View
  12. Oikonomou E, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. European Heart Journal 2024 View