Abstract This paper analyzes the main motion characteristics of several types of movement using wearable sensor data. Based on this analysis, different deep learning-based strategies were evaluated to select the best alternative for separating movements within the same type. These strategies included Convolutional and Recurrent Neural Networks for feature learning and classification. Recordings from Pamap2 and Opportunity datasets were used to evaluate the alternatives considering a subject-wise cross-validation. Significant differences were obtained between the alternatives for the different types of movement, demonstrating the need of adapting feature extraction and classification modules to each type of movement. The best results on Pamap2 dataset showed accuracies of 96.7% and 88.7% for repetitive movements and postures, respectively. On Opportunity dataset, the best results reported accuracies of 66.9% and 97.1% for non-repetitive movements and postures, respectively.

中文翻译：

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适应运动类型的人体活动识别

摘要 本文利用可穿戴传感器数据分析了几种运动类型的主要运动特性。在此分析的基础上，评估了不同的基于深度学习的策略，以选择用于分离同一类型内的运动的最佳替代方案。这些策略包括用于特征学习和分类的卷积和循环神经网络。来自 Pamap2 和 Opportunity 数据集的记录用于评估考虑主题交叉验证的替代方案。不同类型运动的替代方案之间获得了显着差异，表明需要使特征提取和分类模块适应每种类型的运动。Pamap2 数据集的最佳结果显示，重复运动和姿势的准确率分别为 96.7% 和 88.7%。