Abstract—

Human activity recognition (HAR) has become a focus of study over the past few years. It is widely used in many fields like health, home safety, security, and energy saving, among others. Research around the health area has evidenced an important increase and a promissory impact on the life quality of a population like the elderly. If we combine sensors and a health condition then we may have a technological solution with methods and techniques that will help us to improve life quality. Smart sensors have become popular. They allow us to monitor data and acquire data in real-time. In HAR, they are used to detect actions and activities like breathing, falling, standing up, or walking. Many commercial solutions use this technology in real-life applications. However, we focused this paper on the Vayaar sensor and the WideFind sensor, two commercial sensors based on ultra-wideband technology, with promising performance, as part of a study developed at the Human Health and Activity Laboratory (H2AL) in the Luleå Tekniska Universitet in Sweden. The study performed a technological and commercial comparison applying machine learning techniques in WEKA for two datasets created with the data gathered from each sensor during an experiment, in which precision and accuracy were analyzed as evaluation parameters of the applied methods. It was identified that random forest (RF) and LogitBoost were the most suitable classifiers to process both WideFind and Vayyar datasets. Random forest had a performance of 85.99% of precision, 85.48% of recall, and 96% of ROC area for the WideFind sensor while LogitBoost had a 69.39% of the performance for precision, 68.89% for recall, and 88.35% of ROC area for the Vayaar sensor.

中文翻译：

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用于智能环境中位置和姿势活动识别的传感器和机器学习算法

摘要-

人类活动识别（HAR）已成为过去几年的研究热点。它广泛应用于健康、家庭安全、安保、节能等众多领域。围绕健康领域的研究已经证明，健康领域的增长显着，并且对老年人等人群的生活质量产生了预期的影响。如果我们将传感器和健康状况结合起来，那么我们可能会得到一种技术解决方案，其方法和技术将帮助我们提高生活质量。智能传感器已变得流行。它们使我们能够实时监控数据并获取数据。在 HAR 中，它们用于检测呼吸、跌倒、站立或行走等动作和活动。许多商业解决方案在实际应用中使用该技术。然而，我们本文的重点是 Vayaar 传感器和 WideFind 传感器，这两种基于超宽带技术的商用传感器具有良好的性能，是吕勒奥科技大学人类健康与活动实验室 (H2AL) 开发的研究的一部分在瑞典。该研究应用 WEKA 中的机器学习技术，对实验过程中从每个传感器收集的数据创建的两个数据集进行了技术和商业比较，其中分析精度和准确度作为所应用方法的评估参数。结果表明，随机森林 (RF) 和 LogitBoost 是处理 WideFind 和 Vayyar 数据集的最合适的分类器。WideFind 传感器的随机森林的精度为 85.99%，召回率为 85.48%，ROC 面积为 96%，而 LogitBoost 的精度为 69.39%，召回率为 68.89%，ROC 面积为 88.35%。瓦亚尔传感器。