Human Activity Recognition (HAR) has been a popular research field due to the widespread of devices with sensors and computational power (e.g., smartphones and smartwatches). Applications for HAR systems have been extensively researched in recent literature, mainly due to the benefits of improving quality of life in areas like health and fitness monitoring. However, since persons have different motion patterns when performing physical activities, a HAR system must adapt to user characteristics to maintain or improve accuracy. Mobile devices, such as smartphones, used to implement HAR systems, have limited resources (e.g., battery life). They also have difficulty adapting to the device's constraints to work efficiently for long periods. In this work, we present a kNN-based HAR system and an extensive study of the influence of hyperparameters (window size, overlap, distance function, and the value of k) and parameters (sampling frequency) on the system accuracy, energy consumption, and inference time. We also study how hyperparameter configurations affect the model's user and activity performance. Experimental results show that adapting the hyperparameters makes it possible to adjust the system's behavior to the user, the device, and the target service. These results motivate the development of a HAR system capable of automatically adapting the hyperparameters for the user, the device, and the service.

翻译：人类活动识别（HAR）因配备传感器和计算能力的设备（如智能手机和智能手表）的普及而成为一个热门研究领域。近年来，HAR系统的应用已在文献中得到广泛研究，主要归因于其在健康和健身监测等领域提升生活质量的益处。然而，由于不同个体在执行身体活动时具有不同的运动模式，HAR系统必须适应使用者特征以维持或提高准确率。用于实现HAR系统的移动设备（如智能手机）资源有限（例如电池续航），且难以适应设备约束以实现长时间高效运行。本文提出一个基于kNN的HAR系统，并系统研究了超参数（窗口大小、重叠率、距离函数和k值）及参数（采样频率）对系统准确率、能耗和推理时间的影响。同时，我们探究了超参数配置如何影响模型在不同用户和活动上的性能。实验结果表明，调整超参数能够使系统行为适应使用者、设备及目标服务。这些结果激励了开发一种能针对用户、设备和服务自动调整超参数的HAR系统。