Abstract：
Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.
摘要：
近年来，可穿戴传感器和生物电子学的机器学习技术取得了巨大的进步，它在实时传感数据分析中起着至关重要的作用，为个性化医疗提供临床级信息。为此,监督学习和无监督学习算法已经成为强大的工具，允许检测复杂的模式和关系，高维数据集。在这篇评论中,我们的目标是描述可穿戴传感器机器学习的最新进展，专注于算法技术的关键发展，应用程序,以及这种不断发展的景观所固有的挑战。此外,我们强调了机器学习方法提高准确性的潜力，可靠性,和可穿戴传感器数据的可解释性，并讨论这一新兴领域的机会和局限性。最终,我们的工作旨在为这个令人兴奋和快速发展的领域的未来研究工作提供路线图。
