IMU Based Deep Stride Length Estimation With Self-Supervised Learning. (arXiv:2205.02977v1 [cs.LG])

Stride length estimation using inertial measurement unit (IMU) sensors is
getting popular recently as one representative gait parameter for health care
and sports training. The traditional estimation method requires some explicit
calibrations and design assumptions. Current deep learning methods suffer from
few labeled data problem. To solve above problems, this paper proposes a single
convolutional neural network (CNN) model to predict stride length of running
and walking and classify the running or walking type per stride. The model
trains its …

arxiv learning self-supervised learning supervised learning