Federal University of Itajubá, Professor José Rodrigues Seabra Campus, Itajubá 37500-903, Brazil.
Sensors (Basel). 2024 Sep 29;24(19):6322. 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.
机器学习 (ML) 与边缘计算和可穿戴设备的融合正在迅速推进医疗保健应用。本研究系统地绘制了该新兴领域的文献图谱，分析了 171 项研究，并经过严格筛选后聚焦于 28 篇关键文章。该研究探讨了涉及 ML、边缘计算和可穿戴设备的医疗保健应用中使用的关键概念、技术和架构。分析表明，过去六年的研究显著增加，尤其是在过去三年中，涵盖了跌倒检测、心血管监测和疾病预测等应用。研究结果突出了对神经网络模型的强烈关注，特别是卷积神经网络 (CNN) 和长短时记忆网络 (LSTM)，以及各种边缘计算平台，如树莓派和智能手机。尽管方法多样，但该领域仍处于起步阶段，表明未来有很大的研究机会。该研究强调需要标准化架构，并进一步探索硬件和软件，以提高基于机器学习的医疗保健解决方案的效果。作者最后确定了可能有助于医疗技术持续创新的潜在研究方向。
