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.