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Traditionally, rural areas in many countries are limited by a lack of access to health care due to the inherent challenges associated with recruitment and retention of healthcare professionals. Telemedicine, which uses communication technology to deliver medical services over distance, is an economical and potentially effective way to address this problem. In this research, we develop a new telepresence application using an Augmented Reality (AR) system. We explore the use of the Microsoft HoloLens to facilitate and enhance remote medical training. Intrinsic advantages of AR systems enable remote learners to perform complex medical procedures such as Point of Care Ultrasound (PoCUS) without visual interference. This research uses the HoloLens to capture the first-person view of a simulated rural emergency room (ER) through mixed reality capture (MRC) and serves as a novel telemedicine platform with remote pointing capabilities. The mentor’s hand gestures are captured using a Leap Motion and virtually displayed in the AR space of the HoloLens. To explore the feasibility of the developed platform, twelve novice medical trainees were guided by a mentor through a simulated ultrasound exploration in a trauma scenario, as part of a pilot user study. The study explores the utility of the system from the trainees, mentor, and objective observers’ perspectives and compares the findings to that of a more traditional multi-camera telemedicine solution. The results obtained provide valuable insight and guidance for the development of an AR-supported telemedicine platform.

The rapid development of wearable electronic products brings challenges to corresponding power supplies. In this work, a thermally stable and stretchable ionogel-based triboelectric nanogenerator (SI-TENG) for biomechanical energy collection is proposed. The ionic conductivity of the ionogel increased to 0.53 S·m −1 through optimal regulation of the amount of aminoterminated hyperbranched polyamide (NH 2 -HBP), which also has high strain of 812%, excellent stretch recovery, and wide operating temperature range of −80 to 250 °C. The SI-TENG with this ionogel as electrode and silicone rubber both as the triboelectric layer and encapsulation layer exhibits high temperature stability, stretchability, and washability. By adding appropriate amount of nano SiO 2 to triboelectric layer, the output performance is further improved by 93%. Operating in single-electrode mode at 1.5 Hz, the outputs of a SI-TENG with an area of 3 cm × 3 cm are 247 V, 11.7 µA, 78 nC, and 3.2 W·m −2 , respectively. It was used as a self-charging power supply to charge a 22 µF capacitor to 1.6 V in 167 s with the palm patting and then to power the electronic calculator. Furthermore, the SI-TENG can also be used as a self-powered motion sensor to detect the amplitude and frequency of finger bending, human swallowing, nodding, and shaking of the head motion changes through the analysis of the output voltage.

Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor’s output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count.

Issues of size and power consumption in IoT devices can be addressed through triboelectricity-driven energy harvesting technology, which generates electrical signals without external power sources or batteries. This technology significantly reduces the complexity of devices, enhances installation flexibility, and minimizes power consumption. By utilizing shear thickening fluid (STF), which exhibits variable viscosity upon external impact, the sensitivity of triboelectric nanogenerator (TENG)-based sensors can be adjusted. For this study, the highest electrical outputs of STF and sponge-hybrid TENG (SSH-TENG) devices under various input forces and frequencies were generated with an open-circuit voltage (VOC) of 98 V and a short-circuit current (ISC) of 4.5 µA. The maximum power density was confirmed to be 0.853 mW/m2 at a load resistance of 30 MΩ. Additionally, a lying state detection system for use in medical settings was implemented using SSH-TENG as a hybrid triboelectric motion sensor (HTMS). Each unit of a 3 × 2 HTMS array, connected to a half-wave rectifier and 1 MΩ parallel resistor, was interfaced with an MCU. Real-time detection of the patient’s condition through the HTMS array could enable the early identification of hazardous situations and alerts. The proposed HTMS continuously monitors the patient’s movements, promptly identifying areas prone to pressure ulcers, thus effectively contributing to pressure ulcer prevention.

The welding quality, as the product of manual or semi-automatic welding process, is highly affected by the hand motion of the welders. In the shipbuilding industry, the welding inspection process is commonly performed after the welding process is finished by the welding inspectors. The research aims to develop a realtime monitoring system for welder’s performance by recording their hand motion. The hand motion sensors and QR codes are integrated through the Internet of Things (IoT) to monitor the welder’s performance and weldment quality. The realtime monitoring concept was developed by designing the monitoring concept utilising the QR code and Android mobile smartphone. The welder’s hands are equipped with the Inertial Measurement Unit (IMU) sensor to record their hand motion. The motion data are transferred directly from the IMU sensor to the smartphone storage through a Bluetooth connection. The data then are uploaded to the cloud storage through the internet connection. The stored recorded data was then analysed and compared with the qualified welders’ hand motions. After welding ends, the welder reports the visual appearance of the weldment and its location as identified by the QR code through the Android system. Finally, the functionality of the application prototype is tested, and the results show that the system can be used practically for real-time monitoring of welder performance and welding quality.

Various genres of dance, such as Yosakoi Soran, have contributed to the health of many people and contributed to their sense of belonging to a community. However, due to the effects of COVID-19, various face-to-face activities have been restricted and group dance practice has become difficult. Hence, there is a need to facilitate remote dance practice. In this paper, we propose a system for detecting and visualizing the very important dance motions known as stops. We measure dance movements by motion capture and calculate the features of each movement based on velocity and acceleration. Using a neural network to learn motion features, the system detects stops and visualizes them using a human-like 3D model. In an experiment using dance data, the proposed method obtained highly accurate stop detection results and demonstrated its effectiveness as an information and communication technology support for remote group dance practice.

Cross-axis sensitivity is generally undesirable, and lower values are required for the accurate performance of a thermal accelerometer. In this study, errors in devices are utilized to simultaneously measure two physical quantities of an unmanned aerial vehicle (UAV) in the X-, Y-, and Z-directions, i.e., where three accelerations and three rotations can also be simultaneously measured using a single motion sensor. The 3D structures of thermal accelerometers were designed and simulated in a FEM simulator using commercially available FLUENT 18.2 software Obtained temperature responses were correlated with input physical quantities, and a graphical relationship was created between peak temperature values and input accelerations and rotations. Using this graphical representation, any values of acceleration from 1g to 4g and rotational speed from 200 to 1000°/s can be simultaneously measured in all three directions.

The development of automation industry is inseparable from the progress of sensing technology. As a promising self-powered sensing technology, the durability and stability of triboelectric sensor (TES) have always been inevitable challenges. Herein, a continuous charge supplement (CCS) strategy and an adaptive signal processing (ASP) method are proposed to improve the lifetime and robustness of TES. The CCS uses low friction brushes to increase the surface charge density of the dielectric, ensuring the reliability of sensing. A triboelectric mechanical motion sensor (TMMS) with CCS is designed, and its electrical signal is hardly attenuated after 1.5 million cycles after reasonable parameter optimization, which is unprecedented in linear TESs. After that, the dynamic characteristics of the CCS-TMMS are analyzed with error rates of less than 1% and 2% for displacement and velocity, respectively, and a signal-to-noise ratio of more than 35 dB. Also, the ASP used a signal conditioning circuit for impedance matching and analog-to-digital conversion to achieve a stable output of digital signals, while the integrated design and manufacture of each hardware module is achieved. Finally, an intelligent logistics transmission system (ILTS) capable of wirelessly monitoring multiple motion parameters is developed. This work is expected to contribute to automation industries such as smart factories and unmanned warehousing.

Conductive hydrogels with adhesive properties have gained substantial attention in recent years due to their potential applications in soft electronics technology and robotic systems. However, fabricating human motion sensor hydrogels with fast self-healing, strong adhesion to human and animal skin, and high conductivity remains a challenge. Herein, a novel strain sensor hydrogel based on polyvinyl alcohol (PVA) and polydopamine (PDA) in the presence of borax as the cross-linker and bis(trifluoromethane)sulfonimide lithium (LiTFSI) as the conductive medium was fabricated. LiTFSI has not been previously applied to PVA/PDA/borax hydrogels for motion sensitivity. The hydrogel demonstrates robust adherence to various substrates, including polypropylene (PP), glass, steel, rubber, human skin, chicken skin, and chicken bone. Notably, the hydrogel exhibited an adhesion strength of 12.4 kPa on chicken skin. The synthesized PVA/borax@PDA–LiTFSI hydrogel exhibits excellent self-healing with fully healed in 30 s. Furthermore, the hydrogel has high conductivity with a value of 0.79 mS/cm. Therefore, the hydrogel is a promising candidate for human motion monitoring and soft robotics applications. Graphical abstract

Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.