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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Vydané v:PeerJ (San Francisco, CA) Ročník 10; s. e12752
Hlavní autori: Alcantara, Ryan S., Edwards, W. Brent, Millet, Guillaume Y., Grabowski, Alena M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States PeerJ. Ltd 04.01.2022
PeerJ, Inc
PeerJ Inc
Predmet:
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
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Shrnutí: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 stance phase of level-ground running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment. We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data. Nineteen subjects ran on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average ± SD RMSE of 0.16 ± 0.04 BW and relative RMSE of 6.4 ± 1.5% across all conditions and subjects. The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accuracy of quantifying and monitoring external forces experienced by the body when running.
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ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.12752