As society advances, the shift from passive medical care to health management and preventive medical care has become an important issue, with the realization of wearable monitors becoming desirable. In light of the COVID‐19 pandemic, the number of patients who are in urgent need of the monitoring of biological information is increasing. This review focuses on piezoelectric materials and composites that convert kinetic energy into electrical energy to realize self‐powered wearable monitoring sensors, outlining the recent research activity on sensors for use in healthcare monitoring. First, a general description of the principles of piezoelectric monitoring sensors is given. Next, the development status of piezoelectric materials and composites aimed at the application of detecting tiny motions of the human body is introduced, and then the research trends on the detection of larger human body movements are highlighted. Finally, after presenting the performance of current piezoelectric sensors and future research guidelines for developing multifunctional systems in the post COVID‐19 era, the achievements are summarized. Overall, this review will provide guidance to researchers who are seeking to design and develop highly sensitive self‐powered piezoelectric sensors that monitor human motion and physiological signals.

A timely review is presented on artificial intelligence (AI) and, more specifically, deep learning, which is a subfield of machine learning (ML), applied to the design and behavior of modern composite materials systems. The use of composites is increasing due to their high specific strength and stiffness, which make them comparable to metals, and their tunable properties that can be altered to produce lightweight materials with efficient structural configurations. Recent studies are examined and discussed, wherein computational tools have been developed that mimic human brain activity to answer questions and solve challenging problems toward characterizing materials behavior and improving the performance of materials with less effort and cost. The attractiveness of AI comes from its self-learning capability, the faster computer processing time of large datasets, and the potential to yield highly accurate results. However, as a data-driven method, the quantity and quality of data largely affect the accuracy of ML in addition to the need for well-designed AI algorithms and virtual reality models, hence the need to continue the research efforts in this area.

Cellulose nanofiber (CNF) has been accepted as a valid nanofiller that can improve the mechanical properties of composite materials by mechanical and chemical methods. The purpose of this work is to numerically and experimentally evaluate the mechanical behavior of CNF-reinforced polymer composites under tensile loading. Finite element analysis (FEA) was conducted using a model for the representative volume element of CNF/epoxy composites to determine the effective Young’s modulus and the stress state within the composites. The possible random orientation of the CNFs was considered in the finite element model. Tensile tests were also conducted on the CNF/epoxy composites to identify the effect of CNFs on their tensile behavior. The numerical findings were then correlated with the test results. The present randomly oriented CNF/epoxy composite model provides a means for exploring the property interactions across different length scales.

It is well known that the curing residual stress induced during a fabrication process has a great influence on the performance of piezoelectric composite devices. The purpose of this work was to evaluate the residual thermal stress of lead zirconate titanate piezoelectric fiber aluminum (Al) matrix (piezoelectric fiber/Al) composites generated during fabrication numerically and experimentally and to understand the effect of the residual thermal stress on the electromechanical response. The three-dimensional finite element method was employed, and the residual stress generated during the solidification process of the Al matrix was calculated. The output voltage was also calculated in the analysis when putting stresses on the composite materials in the length direction of the piezoelectric fiber. It was shown that the cooling from higher temperatures increases the electromechanical conversion capability. Furthermore, we also performed the simulation, and we recorded the output voltage under concentrated load to investigate its application as a load position detection sensor, and we also discussed the influence of the position by changing the modeling with a different fiber position in the Al. The residual stress of hot press molded piezoelectric fiber/Al composite was then measured, and the comparison was made with the calculated values. The simulation results revealed that our model predictions reproduced and explained the experimental observations of curing residual stress. After this study, similar models of composite materials can be analyzed by this simulation, and the result can be used to design piezoelectric composite materials.

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) has spread rapidly around the world. In order to prevent the spread of infection, city blockades and immigration restrictions have been introduced in each country, but these measures have a severe serious impact on the economy. This paper examines the possibility of both harvesting vibration energy and detecting mass by using a magnetostrictive alloy. Few efforts have been made to develop new magnetostrictive biosensor materials. Therefore, we propose magnetostrictive Fe-Co/Ni clad steel vibration energy harvesters with mass detection, and we numerically and experimentally discuss the effect of the proof mass weight on the frequency shift and output voltage induced by bending vibration. The results reveal that the frequency and output voltage decrease significantly as the mass increases, indicating that the energy harvesting device is capable of mass detection. In the future, device miniaturization and the possibility of virus detection will be considered.