Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Abstract

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

“…For this reason, researchers in the field of machine learning have been addressing the issue of cardiovascular diseases and trying to find more feasible solutions that can help in predicting these diseases to reduce their deadly impact. Several implementations have been performed in the literature to predict CVDs or heart-related information, whether using smart wearables equipped with smart machine learning models [ 34 ] or using only machine learning models as shown in [ 102 ].…”

“…Smart wearables are known to provide people with continuous, long-term, and real-time monitoring. For example, fitness trackers and smartwatches have the potential to play an important role in the early detection and management of various diseases such as cardiovascular disease [ 34 ], diabetes [ 35 ], or even fatigue detection in the workplace, as shown in [ 36 ]. These tools can continuously monitor health data, such as the heart rate, and provide data that can help identify potential health problems.…”

“…The importance of smart wearables stems from their specifications, which have resulted from improvements in information and communication technologies (ICTs), the Internet of Things (IoT), and artificial intelligence. Smart wearables, as seen in [ 34 , 35 , 36 ], can be known as: Non-invasive: do not penetrate the skin to collect data; Compact: should not be bulky or large so as not to interfere with life activities; Affordable: to increase its acceptance; Rugged: to withstand harsh operating conditions such as light scratches or shocks; Easy to use: should have an intuitive interface; Durable power source: able to operate for a long period of time. …”

“…This can be particularly useful for patients with chronic conditions who need close monitoring to avoid complications. For example, using AI in smart wearables, a person’s heart rate and blood pressure can be continuously monitored and the data analyzed to detect the early signs of cardiovascular diseases (CVDs), as shown in [ 34 ]. In addition, smart wearables equipped with sensors and machine learning algorithms can play a critical role in detecting and monitoring diabetes by continuously tracking and analyzing biometric data such as blood glucose levels, heart rate, and activity levels, enabling early detection and intervention [ 35 ].…”

Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System

“…The advent of wearable systems for the measuring of vital signs has started a revolution in non-laborious healthcare and sports science practices [ 1 ]. Unarguably, such systems have provided the possibility of customized continuous monitoring of physiological parameters, such as heart rate (HR), blood pressure, and respiratory rate (RR), which are of great value for both clinical and research purposes [ 2 , 3 ].…”