A Low-Cost, Flexible Pressure Capacitor Sensor Using Polyurethane for Wireless Vehicle Detection

Microsphere-Structured Protein Hydrogel Dielectrics for Capacitive Wearable Sensors

The desire for healthy living has created a crucial need for portable flexible health-monitoring devices based on biomaterials. Toward this end, we report a microsphere-structured hydrogel that uses bovine serum albumin (BSA) as a dielectric layer for capacitive pressure sensors. We developed a theoretical model that describes how stacking dielectric layers of spheres affects the performance of capacitive sensors. We also prepared a prototype sensor featuring the unique microsphere structure to create capacitive sensors with high sensitivity (360.91 strain sensitivity), excellent cyclical stability, and a long service life (over 5000 stretching-compression cycles). Furthermore, the design of the hydrogel sensor allows for easy integration into fabrics to create devices such as smart wristbands, which can collect a diverse range of health data. Thus, BSA-hydrogel-based sensors not only provide safe wearable devices but also advance the performance of high-sensitivity capacitive sensors.

Multi-Layer Polyurethane-Fiber-Prepared Entangled Strain Sensor with Tunable Sensitivity and Working Range for Human Motion Detection

The entanglement of fibers can form physical and topological structures, with the resulting bending and stretching strains causing localized changes in pressure. In this study, a multi-layer polyurethane-fiber-prepared (MPF) sensor was developed by coating the CNT/PU sensing layer on the outside of an elastic electrode through a wet-film method. The entangled topology of two MPFs was utilized to convert the stretching strain into localized pressure at the contact area, enabling the perception of stretching strain. The influence of coating mechanical properties and surface structure on strain sensing performance was investigated. A force regulator was introduced to regulate the mechanical properties of the entangled topology of MPF. By modifying the thickness and length proportion of the force regulator, the sensitivity factor and sensitivity range of the sensor could be controlled, achieving a high sensitivity factor of up to 127.74 and a sensitivity range of up to 58%. Eight sensors were integrated into a sensor array and integrated into a dance costume, successfully monitoring the multi-axis motion of the dancer's lumbar spine. This provides a new approach for wearable biomechanical sensors.

A High-Reliability Piezoelectric Tile Transducer for Converting Bridge Vibration to Electrical Energy for Smart Transportation

Piezoelectric energy transducers offer great potential for converting the vibrations of pedestrian footsteps or cars moving on a bridge or road into electricity. However, existing piezoelectric energy-harvesting transducers are limited by their poor durability. In this paper, to enhance this durability, a piezoelectric energy transducer with a flexible piezoelectric sensor is fabricated in a tile protype with indirect touch points and a protective spring. The electrical output of the proposed transducer is examined as a function of pressure, frequency, displacement, and load resistance. The maximum output voltage and maximum output power obtained were 6.8 V and 4.5 mW, respectively, at a pressure of 70 kPa, a displacement of 2.5 mm, and a load resistance of 15 kΩ. The designed structure limits the risk of destroying the piezoelectric sensor during operation. The harvesting tile transducer can work properly even after 1000 cycles. Furthermore, to demonstrate its practical applications, the tile was placed on the floor of an overpass and a walking tunnel. Consequently, it was observed that the electrical energy harvested from the pedestrian footsteps could power an LED light fixture. The findings suggest that the proposed tile offers promise with respect to harvesting energy produced during transportation.

Estimation of Knee Joint Angle Using Textile Capacitive Sensor and Artificial Neural Network Implementing with Three Shoe Types at Two Gait Speeds: A Preliminary Investigation

The lower limb joints might be affected by different shoe types and gait speeds. Monitoring joint angles might require skill and proper technique to obtain accurate data for analysis. We aimed to estimate the knee joint angle using a textile capacitive sensor and artificial neural network (ANN) implementing with three shoe types at two gait speeds. We developed a textile capacitive sensor with a simple structure design and less costly placing in insole shoes to measure the foot plantar pressure for building the deep learning models. The smartphone was used to video during walking at each condition, and Kinovea was applied to calibrate the knee joint angle. Six ANN models were created; three shoe-based ANN models, two speed-based ANN models, and one ANN model that used datasets from all experiment conditions to build a model. All ANN models at comfortable and fast gait provided a high correlation efficiency (0.75 to 0.97) with a mean relative error lower than 15% implement for three testing shoes. And compare the ANN with A convolution neural network contributes a similar result in predict the knee joint angle. A textile capacitive sensor is reliable for measuring foot plantar pressure, which could be used with the ANN algorithm to predict the knee joint angle even using high heel shoes.

Polyethylene-Carbon Composite (Velostat®) Based Tactile Sensor

The progress observed in ‘soft robotics’ brought some promising research in flexible tactile, pressure and force sensors, which can be based on polymeric composite materials. Therefore, in this paper, we intend to evaluate the characteristics of a force-sensitive material—polyethylene-carbon composite (Velostat^®) by implementing this material into the design of the flexible tactile sensor. We have explored several possibilities to measure the electrical signal and assessed the mechanical and time-dependent properties of this tactile sensor. The response of the sensor was evaluated by performing tests in static, long-term load and cyclic modes. Experimental results of loading cycle measurements revealed the hysteresis and nonlinear properties of the sensor. The transverse resolution of the sensor was defined by measuring the response of the sensor at different distances from the loaded point. Obtained dependencies of the sensor’s sensitivity, hysteresis, response time, transversal resolution and deformation on applied compressive force promise a practical possibility to use the polyethylene-carbon composite as a sensitive material for sensors with a single electrode pair or its matrix. The results received from experimental research have defined the area of the possible implementation of the sensor based on a composite material—Velostat^®.

Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck

Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making.