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.

翻译：最近,使用惯性测量单位(IMU)传感器的轮廓长度估计作为保健和体育培训的一个具有代表性的行进参数受到欢迎。传统估计方法需要一些明确的校准和设计假设。目前深层次的学习方法存在很少的标签数据问题。为了解决上述问题,本文件建议采用单一的进化神经网络模型,预测运行和行走的行进长度,并对运行或行走的每条行进类型进行分类。模型将自己的借口任务训练为自我监督学习大型无标签数据集进行特征学习,并进行下游任务,即用小标签数据集进行监督学习,进行轮廓长度估计和分类。拟议的模型在运行和行进长度回归和行走的精确度方面可以实现更好的平均误差,4.78 ⁇,在运行和行走的轨迹分类方面可以达到99.83 ⁇ 的精确度,与先前的方法相比,在行进长度估计的7.44 ⁇ 。