Augmenting Sensor Performance with Machine Learning Towards Smart Wearable Sensing Electronic Systems

Wearable sensing electronic systems (WSES) are becoming a fundamental platform to construct smart and intelligent networks for broad applications. Various physiological data are readily collected by the WSES, including biochemical, biopotential, and biophysical signals from human bodies. However, understanding these sensing data, such as feature extractions, recognitions, and classifications, is largely restrained because of the insufficient capacity when using conventional data processing techniques. Recent advances in sensing performance and system‐level operation quality of the WSES are expedited with the assistance of machine learning (ML) algorithms. Here, the state‐of‐the‐art of the ML‐assisted WSES is summarized with emphasis on how the accurate perceptions on physiological signals under different algorithms paradigm augment the performance of the WSES for diverse applications. Concretely, ML algorithms that are frequently implemented in the WSES studies are first synopsized. Then broad applications of ML‐assisted WSES with strengthened functions are discussed in the following sections, including intelligent physiological signals monitoring, disease diagnosis, on‐demand treatments, assistive devices, human–machine interface, and multiple sensations‐based virtual and augmented reality. Finally, challenges confronted for the ML‐assisted WSES are addressed.

Reversible Hydration Composite Films for Evaporative Perspiration Control and Heat Stress Management.

Donning of personal protective equipment (PPE) in the healthcare sector has been intensified by the on-going COVID-19 pandemic around the globe. While extensive PPE provides protection, it typically limits moisture permeability and severely hinders the sweat evaporation process, resulting in greater heat stress on the personnel. Herein, a zinc-poly(vinyl alcohol) (Zn-PVA) composite film is fabricated by embedding a super-hygroscopic zinc-ethanolamine complex (Zn-complex) in the PVA matrix. By attaching the Zn-PVA composite film, the relative humidity (RH) inside the protective suit decreases from 91.0% to 48.2%. The reduced RH level, in turn, enhances evaporative cooling, hence bringing down the heat index from 64.6 to 40.0 °C at an air temperature of 35 °C, remarkably lowering the likelihood of heat stroke. The American Society for Testing and Materials tests conducted on a sweating manikin have also proven that the Zn-PVA composite films can significantly reduce the evaporative resistance of the protective suit by 90%. The low material cost, facile fabrication process, and reusability allow the Zn-PVA composition films to be readily available for healthcare workers worldwide. This application can be further extended to other occupations that are facing severe thermal discomfort and heat stress.