4.6 Article

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

期刊

PEERJ
卷 10, 期 -, 页码 -

出版社

PEERJ INC
DOI: 10.7717/peerj.12752

关键词

Biomechanics; Machine learning; IMU; LSTM; GRF; RNN; Biofeedback

资金

  1. National Science Foundation [ACI-1532235, ACI-1532236]
  2. University of Colorado Boulder
  3. Colorado State University

向作者/读者索取更多资源

This study developed a recurrent neural network that can predict continuous normal ground reaction force waveforms at different running speeds and slopes.
Background. 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. Purpose. 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. Methods. Nineteen subjects ran on a force-measuring treadmill at five slopes (0 degrees, +/- 5 degrees, +/- 10 degrees) 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. Results. 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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