In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have witnessed the emergence of health care and exoskeleton robotics which make use of wearable suits, human–machine interaction based on action recognition performs an important role in multimedia applications. Considering the limitations of the application scenario, the surface electromyography (sEMG) signal stands out in many wearable data collection devices for HAR. That is because: (1) timely feedback; (2) no damage to the human body; and (3) the wide range of recognizable actions. However, existing public datasets of sEMG contained relatively few activities, and several large-scale datasets only collected the action of the hand. In addition, the processing of sEMG signals is a new field with no effective evaluation system for it. To tackle these problems, we establish a novel dataset for HAR on lower-limb sEMG named “HAR-sEMG,” using 6 sEMG signal sensors attached to the left leg. A benchmark summarizing experiments with many combinations of existing high-dimensional signal processing algorithms-based manifold learning on our dataset is also provided for a performance analysis.

在过去十年中，人类活动识别 (HAR) 由于其在安全和娱乐方面的应用而越来越受欢迎。近年来，随着医疗保健和外骨骼机器人的出现，利用可穿戴式西装，基于动作识别的人机交互在多媒体应用中发挥着重要作用。考虑到应用场景的局限性，表面肌电（sEMG）信号在许多用于HAR的可穿戴数据采集设备中脱颖而出。那是因为：（1）及时反馈；(2)对人体无损害；(3) 广泛的可识别动作。然而，现有的sEMG公共数据集包含的活动相对较少，几个大型数据集只收集了手部的动作。此外，sEMG信号的处理是一个新领域，没有有效的评估系统。为了解决这些问题，我们使用连接到左腿的 6 个 sEMG 信号传感器为下肢 sEMG 建立了一个名为“HAR-sEMG”的新型 HAR 数据集。还提供了一个基准总结实验，对我们的数据集上现有的基于高维信号处理算法的流形学习的许多组合进行了性能分析。