Self-Powered and Flexible Triboelectric Sensors with Oblique Morphology towards Smart Swallowing Rehabilitation Monitoring System

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A quantitative evaluation of kinetic parameters, the joint's range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device's positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user's vital signs directly from the body in an accurate and non-invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach's subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post-operative rehabilitation and athletes' training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user's health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

Harsh Environmental-Tolerant and High-Performance Triboelectric Nanogenerator Based on Nanofiber/Microsphere Hybrid Membranes

Triboelectric nanogenerator (TENG) can convert tiny mechanical energy into precious electrical energy. Constant improvements to the output performance of TENG is not only the driving force for its sustainable development, but also the key to expand its practical applicability in modern smart devices. However, most previous studies were conducted at room temperature, ignoring the influence of temperature on the output performance of TENG. Additionally, due to thermionic emission effect, the electrons transferred to a dielectric surface can be released into a vacuum after contact electrification. Therefore, TENG cannot maintain an effective electrical output under high-temperature conditions. Here, a series of high-temperature operatable flexible TENGs (HO-TENGs) based on nanofiber/microsphere hybrid membranes (FSHMs) was fabricated by electrospinning and electrospraying. The V_oc of HO-TENG is 212 V, which is 2.33 times higher than that of control TENG. After 10,000 cycle stability tests, the HO-TENG shows excellent durability. Especially, this HO-TENG can maintain 77% electrical output at 70 °C compared to room temperature, showing excellent high-temperature operability. This study can not only provide a reference for the construction of advanced high-performance TENG, but also provide a certain experimental basis for efficient collection of mechanical energy in high-temperature environment and promote the application of TENG devices in harsh environments.

A Mask-Shaped Respiration Sensor Using Triboelectricity and a Machine Learning Approach toward Smart Sleep Monitoring Systems

Daily sleep monitoring is limited by the needs for specialized equipment and experts. This study combines a mask-shaped triboelectric nanogenerator (M-TENG) and machine learning for facile daily sleep monitoring without the specialized equipment or experts. The fabricated M-TENG demonstrates its excellent ability to detect respiration, even distinguishing oral and nasal breath. To increase the pressure sensitivity of the M-TENG, the reactive ion etching is conducted with different tilted angles. By investigating each surface morphology of the polytetrafluoroethylene films according to the reactive ion etching with different tilted angles, the tilted angle is optimized with the angle of 60° and the pressure sensitivity is increased by 5.8 times. The M-TENG can also detect changes in the angle of head and snoring. Various sleep stages can be classified by their distinctive electrical outputs, with the aid of a machine learning approach. As a result, a high averaged-classification accuracy of 87.17% is achieved for each sleep stage. Experimental results demonstrate that the proposed combination can be utilized to monitor the sleep stage in order to provide an aid for self-awareness of sleep disorders. Considering these results, the M-TENG and machine learning approach is expected to be utilized as a smart sleep monitoring system in near future.