Traumatic brain injury (TBI) assessment is crucial for protecting the health of casualties involved in sudden head-impact incidents. However, traditional methods for assessing TBI are primarily based on bulky imaging equipment, which suffers from time lag, leading to misjudgment of the injury and missing the golden opportunity for treatment. Moreover, most existing Internet of Things (IoT)-aided sensor-based head impact detection studies monitor only a single biomechanical parameter, either the external head impact force or the head center-of-mass acceleration, thus offering an incomplete injury profile. This study proposed a method for rapidly acquiring on-site time-series data to support TBI assessment, which employed flexible piezoelectric sensors to detect head impact, whose electromechanical coupling was characterized by an equivalent-impedance model identified with the genetic algorithm (GA). The voltage signal from the sensor was transmitted to mobile devices via Bluetooth to estimate head-impact biomechanics. Specifically, the impact force on the wearer’s head was converted from the voltage using the sequential quadratic programming algorithm (SQP). Based on the impact force data, the acceleration of the center of mass of the head was predicted with a neural network containing the long short-term memory (LSTM) layer. The experiments were carried out on a head-neck dummy subjected to different levels of impact. The resulting models achieved coefficients of determination (R2) of 0.935 for impact force (amplitude of 0–3 kN) and 0.848 for acceleration of the center of mass (amplitude of 0–120 g). The fabricated wireless flexible wearable sensor was installed on the dummy for performance validation, successfully demonstrating collision detection, impact force and acceleration estimation for TBI assessment, which can be conveniently deployed in helmets used in construction, sports, and emergency rescue, leveraging IoT technology for real-time dual-parameter head-impact estimation and medical-data synchronization to safeguard the golden treatment window and hopefully promote the efficient allocation of medical resources.

Recently, the detection of volatile organic compounds (VOCs) has received extensive attention in the field of industrial pollutants detection. As a microgravity sensor based on microelectromechanical system (MEMS), film bulk acoustic resonator (FBAR) plays an important role in the detection of micro-mass and composition. Combined with metal-organic framework (MOF), a type of porous crystal material, FBAR has the ability to adsorb micro-VOC vapor with high sensitivity. Herein, we proposed a MIL-101(Cr) MOF-based FBAR sensors with high resonant frequency and Q factor for five VOCs detection (acetone, ethanol, isopropanol, acetonitrile, and methanol), realizing high sensitivity and stable sensing performance. The experimental data shows the highest sensitivity is 35.15 Hz/ppm for acetone with the limit of detection (LOD) of 322 ppm, which can provide support in ensuring industrial production safety and VOC real-time in-situ detection.

The well-functioning of disc-pad brake subsystems concerns the safe operation of both automobiles and trains. However, there is a lack of effective constant condition monitoring methods for the vital disc-pad brake pairs in such brake subsystems of driving vehicles. Therefore, we propose a brake pair misalignment detection scheme based on a battery-free electromagnetic-based gap sensor, to deal with several common brake pair misalignments including distance changes, translational deviations, and rotary deflections. The alternating-polarity magnet array in the proposed sensor on the brake disc generates a unique distribution of the effective magnetic flux density within a relatively moving microfabricated planar coil sheet along with the brake pad, which is sensitive to the varied gap if misalignments occur between the disc-pad brake pair. Thus, discriminative voltage performance is induced by the inductive planar coils in response to different misalignment types and degrees. Comprehensive finite element simulation analysis and experiments are conducted to elaborate the misalignment detection capability of the proposed sensor. The proposed scheme has the potential to enhance the self-perception of intelligent vehicle systems, without adding the power consumption burden due to the battery-free nature of the electromagnetic-based sensor.

Pressure sensors are essential in precise robotic operations because they can provide force feedback information. In this paper, we propose a soft piezoresistive pressure sensor consisting of a top porous conductive polymer composite (PCPC) layer and a bottom interdigital electrode (IDE) layer, demonstrating a high sensitivity up to 58.60%$\text{kPa}^{-1}$and a hysteresis error as low as 3.18%. Devices with different pore sizes and IDE geometric parameters are fabricated and experimentally characterized. Results show the dependence of the sensor performance on porosity and IDE configuration, which is consistent with theoretical analysis. The proposed technique enables a customized design of soft piezoresistive pressure sensors for different sensing ranges. As a practical demonstration, the proposed pressure sensor is integrated with a soft robotic gripper to perform delicate manipulation tasks.

Water quality event detection is critically important to national security and people's health. And Empirical Mode Decomposition (EMD) is a common used method to analyze nonlinear and non-stationary signals. However the standard EMD can just deal with one variation and certain information of the original signals would be missed. In this paper, we proposed a multivariate water quality event detection approach for detecting accidental or intentional water contamination events, thus to improve the detection rate. The proposed approach is based on multivariate empirical mode decomposition (MEMD), which is a novel algorithm to analyze nonlinear and non-stationary signals. A sequence of n-dimension intrinsic mode functions (IMFs)is obtained by the process of MEMD, then the Mahalanobis Distance is used to perform information fusion and the normalized instantaneous energy (NIE) is used to perform anomaly detection.

For on-line measuring the periodic fouling resistance in recipe alternation, a novel soft-sensing approach based on FNN (Fuzzy Neural Network), FCM (Fuzzy c-means clustering) and OED (Orthogonal Experimental Design) was investigated. The periodic fouling resistance is modeled by two parts: the increase of short-term fouling in one batch and the long-term irreversible fouling at the beginning of the batch. FCM algorism was used to cluster all the recipes into classes, and give each recipe a fuzzy membership belongs to one class. Based on the membership matrix, a FCM neuron was designed and accordingly a hybrid FNN that is robust to recipe alternation was proposed. OED were used to select out the key parameters influencing the long-term fouling and short-term fouling respectively. Two Hybrid FNN networks are trained to learn the formation trends of the short-term fouling and long-term fouling trend. The new measurement scheme combines the advantages of FCM, OED and FNN, have a good performance to predict the fouling in recipe alternation. The experimental results and the comparison to other methods show the new scheme is effective.