Abstract
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.
Highlights
The assistance of wearable technologies in monitoring healthcare is revolutionizing the medical field
We address the process of detecting clinically relevant features extracted from wearable sensors and the associated data
Another study performed by Zhu et al, on COVID-19 prediction by utilizing heart rate and sleep data collected from wearables devices, employed a neural network predictionbased method
Summary
The assistance of wearable technologies in monitoring healthcare is revolutionizing the medical field. These devices have shown good performance in terms of accuracy of user physiological parameters, which has led to them being employed for various scientific studies Apart from these devices, the Apple® Watch 2, Samsung Gear S3® , Xiaomi Mi® and Huawei Talk Band B2® are other wearable devices that provide health care monitoring [4,5]. The heart rate is a vital physiological parameter, and abnormal heart rates that span a period of time can be translated into indicators of various diseases by utilizing mathematical models, which are elaborated from Section 3 onwards These abnormal data points are called anomalies, and this review is primarily on understanding and reviewing algorithms that are capable of detecting anomalies in addition to decision-based systems that can handle the constantly evolving personalized data [13]. We provide an overview of anomaly detection, data types, imputation strategies and the prospects of the field
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