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

Machine Learning (1465) Wearable Devices and Sensors (889) Decision Support for Health Professionals (1186) Clinical Information and Decision Making (1407) Osteoporosis and Osteoporosis Prevention (12) Falls Prevention in the Elderly (69) Falls and Emergency Detection with Sensors and other Technologies (36) Fitness Trackers and Smart Pedometers/Accelerometers (561)

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 (15) Tweetations (4) Metrics

Journals

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  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;80(12):5064 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;45(35):3204 View
  13. Han S, Xiao Q, Liang Y, Chen Y, Yan F, Chen H, Yue J, Tian X, Xiong Y. Using Flexible-Printed Piezoelectric Sensor Arrays to Measure Plantar Pressure during Walking for Sarcopenia Screening. Sensors 2024;24(16):5189 View
  14. Lee P, Yu W, Zhou J, Tsai T, Manor B, Lo O. A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study. JMIR mHealth and uHealth 2024;12:e52166 View
  15. Toda H, Chin T. Physical Frailty Prediction Using Cane Usage Characteristics during Walking. Sensors 2024;24(21):6910 View