Healthcare with Wearable Devices and Machine Learning

Abstract

As health management continues to grow in importance globally, driven by the quest for improved health outcomes, longevity, and personalized care, machine learning (ML) has become a popular technique for detecting and analyzing health indicators, playing an essential role in multiple areas of health detection, such as continuous heart and blood pressure monitoring, blood sugar tracking for diabetics, sleep analysis to improve sleep quality, and even disease prediction. With wearable devices, a particular scale of health detection data has been accumulated at this stage. With this data, machine learning can be predictive analytics to assess real-time health conditions, enabling early intervention and personalized treatment plans. However, data quality remains the most critical issue for machine learning-based health detection systems, as inaccuracies in sensor readings can lead to misdiagnoses or incorrect treatment decisions. In addition, privacy concerns are significant, as the sensitive health information collected by these devices requires strong security measures to protect users’ data. Moreover, the interpretability of machine learning algorithms, especially those involving deep learning, can be limited, hindering clinicians’ understanding and trust. This paper addresses these challenges by providing a comprehensive overview of relevant data sets used in health monitoring and examining the latest machine learning algorithms tailored for wearables-based health detection. It further delves into the current limitations and challenges facing this emerging field, providing insights into potential solutions and future directions for research and development.