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

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.

A Systematic Literature Review on Trustworthiness for Applications Used in eHealth Environments.

Context: The technological advancement of the Internet of Things (IoT) creates opportunities in various social sectors. Patients in clinics or home care have their comfort and safety enhanced with remote monitoring, sensors and applications that control and transfer patient data. These applications must be trustworthy, since they deal with sensitive data. Purpose: The purpose of this work is to identify gaps in trustworthiness, availability, effectiveness, security and other attributes. Also, to highlight challenges and opportunities for research and give guidance on choosing the right technology or application based on the resources available to support patients and doctors, protocol of communication and maturity level of these technologies. Methodology: This work presents a systematic review of the literature following four steps: Definition of the Research Questions, Conduct Search, Screening of Papers, and Data Extraction and Mapping Process. Results: Based on the articles studied, it was possible to answer important questions about eHealth applications. The results highlight how eHealth applications can enhance patient care by monitoring health data and supporting doctors' decision-making with a reasonable level of trustworthiness. Additionally, the results demonstrate how applications can notify external caregivers in emergencies and assist in diagnosis and treatment of illnesses. However, these applications still face problems related to sensor lifetime, medical data sharing, interoperability and lack of standardization. Finally, we suggest a literature mapping to support the choice of technologies based on resources available, communication protocol and technological maturity. Conclusion: This work carries out a systematic literature review to discuss state-of-the-art eHealth applications and gather new information of current research. In this process it was possible to show how these applications work, map out their main technological characteristics to assist the decision-making process for future works and uncover eHealth applications' strengths, future perspectives and challenges, specifically related to the high level of trustworthiness necessary.