  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 pretext task with self-supervised learning on a large unlabeled
dataset for feature learning, and its downstream task on the stride length
estimation and classification tasks with supervised learning with a small
labeled dataset. The proposed model can achieve better average percent error,
4.78\%, on running and walking stride length regression and 99.83\% accuracy on
running and walking classification, when compared to the previous approach,
7.44\% on the stride length estimation.
