论文标题
基于IMU的深度步长度估计，并有自我监督的学习
IMU Based Deep Stride Length Estimation With Self-Supervised Learning
论文作者
论文摘要
使用惯性测量单元（IMU）传感器的步幅长度估计最近成为医疗保健和运动训练的代表步态参数。传统的估计方法需要一些明确的校准和设计假设。当前的深度学习方法遭受了很少的标记数据问题。为了解决上述问题，本文提出了一个单个卷积神经网络（CNN）模型，以预测跑步和步行的长度，并对每次步幅进行跑步或步行类型进行分类。该模型通过在一个大型未标记的数据集上进行自我监督的学习来训练其借口任务，以进行功能学习，并通过使用小标记的数据集进行监督学习，在步幅长度估计和分类任务上进行下游任务。提出的模型可以在跑步和步行长度回归时达到更好的平均百分比误差，4.78％\％，与以前的方法相比，在跑步和步行分类方面的精度为99.83 \％的精度，在步幅长度估计中为7.44 \％。
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.
