Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 19; p. 6322
• Main Authors: Pereira, Carlos Vinicius Fernandes, de Oliveira, Edvard Martins, de Souza, Adler Diniz
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29-09-2024; MDPI
• Subjects: Algorithms; Artificial intelligence; Data collection; Delivery of Health Care; Edge computing; Health care industry; healthcare; Humans; Innovations; Internet of Things; Keywords; Machine Learning; Medical research; Medicine, Experimental; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Neural networks; Neural Networks, Computer; R&D; Research & development; Research methodology; Smartphone; systematic mapping; Systematic Review; Technology application; Trends; Wearable computers; wearable devices; Wearable Electronic Devices; wearable devices; systematic mapping; machine learning; edge computing; healthcare
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24196322

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.