Advancements in Machine Learning for Healthcare: Enhancing Human Vital Sign Monitoring and Activity Recognition

Abstract

The wide adoption of ML technologies for monitoring human vital signs and human activity recognition is revolutionizing different aspects of healthcare. This study focuses on recent progress in ML algorithms and their application as real-time health monitoring, time for improved patient care, early diagnosis, and health outcomes.

Methods: To satisfy the above objectives, a systematic literature review was conducted across PubMed, IEEE Xplore, and Scopus from January 2019 to August 2024 for application of ML for human vital sign measurement and activity recognition. The studies included in the discussion were studies exploring advances in sensor technology, wearable devices, as well as health analytics.

Results: The human vital sign monitoring, for example, heart rate, respiratory rate and blood pressure, has experienced significant improvement in the accuracy and efficiency of ML algorithms. Moreover, the ML in the advancement of activity recognition has been used in rehabilitation, for fall detection and elderly care. Clinical decision making and health management system has also been improved with the integration of real time monitoring with predictive analytics.

Conclusion:
However, benefits for healthcare of machine learning, especially in human vital sign measurement and activity recognition, are huge. Yet work remains, namely with regard to data privacy concerns, algorithmic bias, and the development of appropriate regulatory frameworks. However, future research should aim to overcome these obstacles and to incorporate these technologies into clinical practice while at the same time enhancing transparency of algorithm and integrating these technologies into clinical practice.