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

• 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

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...