Soft Electronics for Health Monitoring Assisted by Machine Learning

Abstract

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

Surface Control and Electrical Tuning of MXene Electrode for Flexible Self‐Powered Human–Machine Interaction

“…The 2D carbide MXene Ti 3 C 2 T x can be synthesized by chemically stripping the aluminum layer of ternary layered Ti 3 AlC 2 with either a synthetic precursor of hydrofluoric acid (HF) or with the acid itself. [24] In this work, we gently etched nanosheets with preparation environments consisting of different (temperature/time) combinations: 40 °C for 48 h (called GE 40/48 ), 40 °C for 36 h (GE 40/36 ), and 20 °C for 48 h (GE 20/48 ). We also directly synthesized custom-made products (CM-Ti 3 C 2 ) and etched Ti 3 AlC 2 with high concentration HF (HF-Ti 3 C 2 ) for comparison.…”

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

“…The sheer volume and complexity of the data collected by wearable diagnostic and therapeutic devices poses challenges in deriving meaningful information. 675 Machine learning algorithms offer promising solutions to extract valuable insights from these signals, enabling accurate recognition, classification, and decision making. A number of biophysical and physiological signals, including ECG, 676 heart rate, 677 blood pressure, 260 hand gestures, 177 speech, 519 body motion, 516 and more, have been powered with machine learning techniques for automated analysis, recognition, and classification purposes.…”

“…The sheer volume and complexity of the data collected by wearable diagnostic and therapeutic devices poses challenges in deriving meaningful information . Machine learning algorithms offer promising solutions to extract valuable insights from these signals, enabling accurate recognition, classification, and decision making.…”

Ionic hydrogels-based triboelectric nanogenerators for self-powered human–machine interfaces

“…Low-dimensional materials possess numerous characteristics and advantages for the preparation of ionic hydrogels [130]. They exhibit higher specific surface area, enhanced conductivity and ion transport, unique mechanical properties, ease of modification, and controllable porous structures [131].…”