View in EDS

Prediction of knee loads during activities of daily living using custom instrumented insoles and machine learning.

Saved in:
Bibliographic Details
Title: Prediction of knee loads during activities of daily living using custom instrumented insoles and machine learning.
Authors: Snyder SJ; Orthopedics and Sports Medicine, MedStar Health Research Institute, Baltimore, United States; Department of Kinesiology, University of Maryland, College Park, United States. Electronic address: samantha.snyder@medstar.net., Fakhar M; Department of Kinesiology, University of Maryland, College Park, United States. Electronic address: mfakhar@umd.edu., Miller RH; Department of Kinesiology, University of Maryland, College Park, United States; Neuroscience and Cognitive Science Program, University of Maryland, United States. Electronic address: rosshm@umd.edu., Bera A; Department of Computer Science, Purdue University, West Lafayette, United States; Department of Computer Science, University of Maryland, College Park, United States. Electronic address: ab@cs.purdue.edu., Shim JK; Department of Kinesiology, University of Maryland, College Park, United States; Neuroscience and Cognitive Science Program, University of Maryland, United States. Electronic address: jkshim@umd.edu.
Source: Journal of biomechanics [J Biomech] 2025 Oct; Vol. 191, pp. 112921. Date of Electronic Publication: 2025 Aug 19.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Science Country of Publication: United States NLM ID: 0157375 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2380 (Electronic) Linking ISSN: 00219290 NLM ISO Abbreviation: J Biomech Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-2380
DOI:10.1016/j.jbiomech.2025.112921