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

Abstract

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.

MXene Functionalized, Highly Breathable and Sensitive Pressure Sensors with Multi‐Layered Porous Structure

“…[8] The sensitivity of the pressure sensors was defined as S = (ΔI/I 0 )/P, where I 0 , ΔI, and P denote the initial current without external pressure, relative current change, and external pressure, respectively. [4] The pressure sensing performance was determined by the electrical conductivity of the MXene-based sensing electrodes and the layers of the sensing electrodes (Figure 4e (1), [17] (2), [5] (3), [17] (4), [17] (5), [5] (6), [17] (7), [17] (8), [17] (9), [9] (10), [17] (11), [17] (12), [17] (13), [17] (14), [17] (15), [17] (16), [17] (17), [25] (18). [8] h) Cycling stability of the MAF-3 pressure sensor over 5000 loading/unloading cycles.…”

“…g) Comparison for the sensing performance of the sensor with those reported in previous literatures. (1),[17] (2),[5] (3),[17] (4),[17] (5),[5] (6),[17] (7),[17] (8),[17] (9),[9] (10),[17] (11),[17] (12),[17] (13),[17] (14),[17] (15),[17] (16),[17] (17),[25] (18). [8] h) Cycling stability of the MAF-3 pressure sensor over 5000 loading/unloading cycles.…”

Intelligent wearable devices based on nanomaterials and nanostructures for healthcare

“…With the rapid development of wearable sensors, devices, and intelligent algorithms, a great number of reviews have been devoted to a specific topic, including types of sensors, [32][33][34] advanced materials, 12,35,36 and machine learning algorithms for stretchable sensing. 37,38 However, a systematic summary, from mechanisms, sensors and algorithms to intelligent multifunctional devices, has not yet been presented. In this review, we present a comprehensive overview of wearable devices for smart healthcare (Fig.…”

Strong, Compressible, and Ultrafast Self‐Recovery Organogel with In Situ Electrical Conductivity Improvement

“…In short, the onestep process in this work provides a simple yet effective method to prepare highly compressible and conductive organogel with a complete ultrafast self-recovery, which may function as an alternative to design high-performance soft materials for soft robotics, wearable electronics, and tissue-device interfaces. [42][43][44]…”