Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution

Ground reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory environment. Previous studies have used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the...

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Bibliographic Details
Published in:	PeerJ (San Francisco, CA) Vol. 10; p. e12752
Main Authors:	Alcantara, Ryan S., Edwards, W. Brent, Millet, Guillaume Y., Grabowski, Alena M.
Format:	Journal Article
Language:	English
Published:	United States PeerJ. Ltd 04.01.2022 PeerJ, Inc PeerJ Inc
Subjects:	Accelerometers Accelerometry Accuracy Biomechanics Computational linguistics Data Mining and Machine Learning Exercise Test Fitness equipment GRF Humans IMU Kinematics Laboratories Language processing LSTM Machine learning Movement Natural language interfaces Neural networks Neural Networks, Computer RNN Running Sacrum Somatotropin releasing hormone Variables Wearable computers United States > US Biomechanics IMU LSTM RNN Machine learning Biofeedback GRF
ISSN:	2167-8359, 2167-8359
Online Access:	Get full text
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