Here, we developed highly sensitive piezoelectric sensors in which flexible membrane components were harmoniously integrated. An electrospun nanofiber mat of poly(vinylidenefluoride-co-trifluoroethylene) was sandwiched between two elastomer sheets with sputtered electrodes as an active layer for piezoelectricity. The developed sensory system was ultrasensitive in response to various microscale mechanical stimuli and able to perceive the corresponding deformation at a resolution of 1 μm. Owing to the highly flexible and resilient properties of the components, the durability of the device was sufficiently stable so that the measuring performance could still be effective under harsh conditions of repetitive stretching and folding. When employing spin-coated thin elastomer films, the thickness of the entire sandwich architecture could be less than 100 μm, thereby achieving sufficient compliance of mechanical deformation to accommodate artery-skin motion of the heart pulse. These skin-attachable film- or sheet-type mechanical sensors with high flexibility are expected to enable various applications in the field of wearable devices, medical monitoring systems, and electronic skin.

Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.

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Here, we present a simple yet highly efficient method to enhance the output performance of a piezoelectric device containing electrospun nanofiber mats. Multiple nanofiber mats were assembled together to harness larger piezoelectric sources in the as-spun fibers, thereby providing enhanced voltage and current outputs compared to those of a single-mat device. In addition to the multilayer assembly, microbead-based electrodes were integrated with the nanofiber mats to deliver a complexed compression and tension force excitation to the piezoelectric layers. A vacuum-packing process was performed to attain a tight and well-organized assembly of the device components even though the total thickness was several millimeters. The integrated piezoelectric device exhibited a maximum voltage and current of 10.4 V and 2.3 μA, respectively. Furthermore, the robust integrity of the device components could provide high-precision sensitivity to perceive small pressures down to approximately 100 Pa while retaining a linear input-output relationship.

For a few decades, various methods to suppress the vibrations of structures have been proposed and exploited. These include passive methods using constrained layer damping (CLD) and active methods using smart materials. However, applying these methods to large structures may not be practical because of weight, material, and actuator constraints. The objective of the present study is to propose and exploit an effective method to suppress the vibration of a large and heavy beam structure with a minimum increase in mass or volume of material. Traditional tuned mass dampers (TMD) are very effective for attenuating structural vibrations; however, they often add substantial mass. Eddy current damping is relatively simple and has excellent performance but is force limited. The proposed method is to apply relatively light-weight TMD to attenuate the vibration of a large beam structure and increase its performance by applying eddy current damping to a TMD. The results show that the present method is simple but effective in suppressing the vibration of a large beam structure without a substantial weight increase.