IMU Based Deep Stride Length Estimation With Self-Supervised Learning
This work addresses the problem of accurate gait analysis for health care and sports training by reducing calibration needs and data requirements, though it is incremental as it builds on existing deep learning methods.
The paper tackles stride length estimation from IMU data by proposing a CNN model that uses self-supervised learning on unlabeled data to address limited labeled data, achieving 4.78% average error in stride length regression and 99.83% accuracy in gait classification, outperforming a previous 7.44% error.
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.