Category
Wearable Technology
Document Type
Paper
Abstract
The purpose of this study was to use machine learning (i.e., artificial neural network – ANN), to predict vertical ground reaction force (vGRF) from tibial accelerations in runners with different foot strike patterns and at different running speeds. Thirty-eight healthy runners ran at three different speeds: the pace at which the runner spends most of their training time (LSD), 15% faster than LSD (LSD15), and 30% faster than LSD (LSD30). vGRF and IMU-based accelerations from the tibia were collected during the last 30 seconds at each speed. Tibial accelerations were used to calculate the resultant tibial acceleration (RTA). Time-series stance-phase vGRF and RTA from 34 subjects at all three speeds were used to train the ANN. Trials from two males and two females, who exhibited different foot-strike patterns, were used to test the ANN. The prediction error of the ANN was 102.4 N (1.6 N/kg or 0.16 BW) across the entire stance phase of running. The ability to predict GRF with an ANN and only RTA as input appears to be practical and feasible.
Recommended Citation
Miller, Alec C.; Aljohani, Marwan; Kim, Hoon; and Kipp, Kristof
(2019)
"NEURAL NETWORK METHOD TO PREDICTING STANCE-PHASE GROUND REACTION FORCE IN DISTANCE RUNNERS,"
ISBS Proceedings Archive: Vol. 37:
Iss.
1, Article 98.
Available at:
https://commons.nmu.edu/isbs/vol37/iss1/98
