Rhenix Lifesciences, Hyderabad 500038, India.
CureScience, San Diego, CA 92121, USA.
Sensors (Basel). 2022 Jan 19;22(3):756. doi: 10.3390/s22030756.
Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.
可穿戴设备使用传感器来评估生理参数，如心率、脉搏率、步数、体脂和饮食。生理参数的连续监测为评估个人健康提供了一种潜在的解决方案。识别心率和其他特征中的异常值或异常情况有助于识别模式，这些模式在理解疾病状态的根本原因方面可以发挥重要作用。由于异常值存在于可穿戴设备传感器生成的大量数据中，因此识别异常值需要准确的自动化技术。鉴于异常值的临床意义及其对诊断和治疗的影响，已经提出了广泛的检测方法来检测异常值。本文中的大部分内容都是基于以前发表的文献。使用可穿戴设备的临床研究也在增加。在本文中，我们回顾了与可穿戴设备相关的数据的性质，以及用于检测异常值的下游处理方法。此外，我们还回顾了监督和无监督技术以及半监督方法，这些方法克服了缺失和未注释的医疗保健数据的挑战。
