Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control wearable assistive devices, yet there has not been enough research facilitating its implementation. In this study, we propose several models to predict four activities from inertial sensors located in the ankle area of a lower-leg assistive device user. This choice is because they do not need to be attached to the user's skin and can be directly implemented inside the control unit of the device. The proposed models are based on Artificial Neural Networks and could achieve up to 92.8% average classification accuracy

翻译：人体活动识别（HAR）是矫形器与外骨骼等人机辅助设备的基本构建模块之一。根据应用场景的不同，HAR存在多种实现方法。大量研究通过优化输入数据或分类算法来改进这些方法，但多数研究聚焦于安防监控、智能设备、物联网等应用领域。另一方面，HAR虽可助力可穿戴辅助设备的调节与控制，但相关实施性研究仍显不足。本研究提出多个模型，用于从下肢辅助设备使用者踝关节区域的惯性传感器中预测四种活动类型。选择该部位的原因在于传感器无需贴附使用者皮肤，可直接集成于设备控制单元。所提出的模型基于人工神经网络，平均分类准确率最高可达92.8%。