Human Activity Recognition (HAR) stands as a pivotal technique within pattern recognition, dedicated to deciphering human movements and actions utilizing one or multiple sensory inputs. Its significance extends across diverse applications, encompassing monitoring, security protocols, and the development of human-in-the-loop technologies. However, prevailing studies in HAR often overlook the integration of human-centered devices, wherein distinct parameters and criteria hold varying degrees of importance compared to other applications. Notably, within this realm, curtailing the sensor observation period assumes paramount importance to safeguard the efficiency of exoskeletons and prostheses. This study embarks on the optimization of this observation period specifically tailored for HAR using Inertial Measurement Unit (IMU) sensors. Employing a Deep Convolutional Neural Network (DCNN), the aim is to identify activities based on segments of IMU signals spanning durations from 0.1 to 4 seconds. Intriguingly, the outcomes spotlight an optimal observation duration of 0.5 seconds, showcasing an impressive classification accuracy of 99.95%. This revelation holds immense significance, elucidating the criticality of precise temporal analysis within HAR, particularly concerning human-centric devices. Such findings not only enhance our understanding of the optimal observation period but also lay the groundwork for refining the performance and efficacy of devices crucially relied upon for aiding human mobility and functionality.

翻译：人体活动识别（HAR）作为模式识别领域的一项关键技术，致力于利用单个或多个传感器输入解析人体运动与行为。其重要性遍及监测、安防协议及人在回路技术开发等多样化应用场景。然而，现有HAR研究常忽视人本设备的集成需求——在此类应用中，不同参数与标准的相对重要性与其他应用场景存在显著差异。特别值得注意的是，在该领域中，缩短传感器观测周期对于保障外骨骼与假肢的运行效率具有至关重要的意义。本研究针对基于惯性测量单元（IMU）传感器的HAR系统，专门致力于优化其观测周期。通过采用深度卷积神经网络（DCNN），旨在根据持续时间为0.1至4秒的IMU信号片段进行活动识别。值得关注的是，研究结果明确指出0.5秒为最优观测时长，并展现出99.95%的卓越分类准确率。这一发现具有重大意义，不仅揭示了HAR领域中精确时序分析的关键性，更对人本设备的设计具有特殊指导价值。此类研究成果不仅深化了我们对最优观测周期的理解，更为提升辅助人类运动功能的核心设备性能与效能奠定了理论基础。