In the automotive industry and smart car sectors, the demand for high-accuracy pressure measurement using piezoresistive pressure sensors is increasingly urgent. In response to such needs, this paper systematically investigates the sources of nonlinear error and optimization methods for piezoresistive pressure sensors using SOI substrates. Four circular flat film sensors with a measuring range of 0 similar to 10 MPa are designed and manufactured. These folded piezoresistive structures employ different sizes and doping concentrations in the inflection section. Test results show that sensors with lightly doped inflection sections exhibit lower nonlinear error. At 25 degrees C, the sensor's nonlinear error is 0.018% with an accuracy of 0.044%. At 150 degrees C, the sensor's nonlinear error is 0.027% with an accuracy of 0.040%FS. Over the temperature range of-10 degrees C to 150 degrees C, the sensor's temperature coefficient of zero (TCO) is 0.015 %FS/degrees C, and its temperature coefficient of sensitivity (TCS) is 0.118 %FS/degrees C. The optimized sensors are applied to pressure parameter measurement in applications such as automotive engines and braking systems, providing high-accuracy pressure data and possessing significant practical value.

Accurate and continuous recognition of individual animal behaviors is fundamental to intelligent livestock farming and precision management. Traditional manual inspection lacks scalability and real-time capability, while vision-based approaches are often constrained by occlusion, illumination variation, and complex barn environments. Wearable inertial sensors provide a robust alternative for individual-level behavior perception with weak dependence on environmental conditions. However, pig behaviors exhibit pronounced heterogeneity in temporal scales and motion patterns, making accurate temporal modeling under limited computational resources particularly challenging for edge deployment. To address these challenges, this paper proposes a Multi-scale Semantic-aware Temporal Network (MSATNet) for sow behavior recognition using wearable six-axis inertial sensors. MSATNet combines a Large-scale Temporal Modeling (LTM) module and a Small-scale Temporal Refinement (STR) module to jointly model long-duration stable behaviors and short-duration transient motions within fixed time windows. In addition, a Semantic-aware Supervision (SAS) mechanism is introduced during training to incorporate hierarchical behavioral semantics and reduce confusion among semantically similar behaviors without increasing inference cost. The proposed method was evaluated in a real-world large-scale pig farm. MSATNet achieves accuracies of 93.69% on the validation set and 92.19% on an independent test set across seven daily sow behaviors, outperforming Random Forest (88.59%), MobileNet V2 (89.59%) and ViT (91.99%) under identical experimental settings. Furthermore, it achieves a single-sample inference latency of 1.34 ms on the Raspberry Pi 5 edge device, demonstrating its suitability for real-time deployment. These results indicate that MSATNet provides an accurate, lightweight, and scalable solution for continuous pig behavior monitoring in edge-intelligent livestock farming systems.

This paper presents a novel micro-electromechanical system piezoresistive pressure sensor designed for a low-pressure range of 0-2 kPa. The sensor features an innovative square silicon diaphragm incorporating beam-island structures on the front surface and beams on the back surface. This structural configuration effectively concentrates stress in the piezoresistive regions while enhancing local stiffness, thereby achieving high sensitivity and excellent linearity. Finite element method (FEM) simulations were performed to analyze the stress distribution and deflection characteristics of the diaphragm. To efficiently optimize the diaphragm structure, a deep learning-assisted optimization framework combining a multilayer perceptron (MLP) surrogate model with particle swarm optimization (PSO) was developed. The trained MLP captures the nonlinear relationship between structural parameters and sensor performance, while PSO enables continuous and global exploration of the design space to identify optimal configurations. This MLP-PSO framework significantly improves design efficiency and flexibility compared with conventional FEM-based parametric scanning, enabling sensitivity maximization while maintaining low nonlinearity. FEM simulations of the optimized designs were conducted for square diaphragm sizes ranging from 3500 to 3900 mu m, demonstrating consistently high sensitivity and low nonlinearity across the design space. For a diaphragm size of 3795 mu m with a thickness of 16.2 mu m, the proposed sensor achieves a voltage sensitivity of 25.25 mV V-1 kPa-1 and a nonlinearity of 0.12% FS.

Low-pressure MEMS piezoresistive pressure sensors commonly employ structured diaphragms as their primary pressure-sensing element, and the diaphragm geometry plays a decisive role in determining overall sensor performance. Designing such structures typically relies on simulation-based optimization, which can be computationally expensive when a large design space is explored. To improve design efficiency, this study proposes a data-driven surrogate-assisted optimization framework that integrates the CatBoost regression model with the Differential Evolution (DE) algorithm. Finite element simulation data are first generated for different parameter combinations within the predefined diaphragm configuration. A CatBoost surrogate model is then trained to predict the structural response of the diaphragm, with geometric parameters as inputs and the maximum stress and deflection as outputs. The trained surrogate model is subsequently embedded within a DE-based optimization process to efficiently search the design space while satisfying the deflection constraint. The results demonstrate that the proposed framework can effectively identify parameter combinations that achieve high stress levels under the specified constraints, while significantly reducing the need for repeated finite element simulations during the optimization process. In addition, SHAP (SHapley Additive exPlanations) analysis is employed to provide interpretable insights into the influence and interaction of structural parameters on diaphragm performance. The proposed CatBoost-DE framework therefore offers an efficient and interpretable data-driven workflow for the structural optimization of low-pressure MEMS piezoresistive pressure sensors.

In low-frequency vibrating MEMS resonators, device structures typically vibrate in flexural mode and torsional mode. The mechanical stiffness in the motion direction of the bending mode is often strongly correlated with the device’s equivalent stiffness, while torsional mode generally use piezoelectric actuation and usually have a low Q factor. These factors limit the application of low-frequency resonators in some high-reliability scenarios. This paper proposes a low-frequency wheel-shaped resonator vibrating in the in-plane torsional mode. By using the torsional vibration of the wheel-shaped mass instead of translational vibration, and adopting an in-plane capacitive structure for driving and sensing, the device maintains high in-plane stiffness while vibrating at low frequencies. At the same frequency, its in-plane stiffness is 10² orders of magnitude higher than that of the flexural mode. Simulations on the anti-overload performance of the device structure show that under a 100g static load, the frequency drifts in the X-axis and Z-axis directions are 21.67 ppb and 60.39 ppb, respectively. Under 10000g impact loads in the X-axis and Z-axis directions, the maximum in-plane stresses are 24.7 MPa and 45.7 MPa, with maximum displacements of 0.111 µm and 0.137 µm, respectively—values far lower than the maximum allowable stress of silicon material and the minimum line width of the device. This demonstrates that the structure has high frequency stability and anti-overload performance.

Low-cost, high-precision indoor positioning systems are becoming crucial in some applications such as smart manufacturing, logistics and warehousing, pedestrian tracking and embodied artificial intelligence. Ultrawideband (UWB)-based ranging techniques offer advantages like short pulse intervals and high temporal resolution, but non-line-of-sight (NLOS) occlusion can limit their performance and application scope. Low-cost inertial measurement units (IMUs) provide accurate navigation information over short periods but suffer from error accumulation. To address these issues, this paper propose a IMU/UWB tightly coupled navigation algorithm combining carrier motion characteristics. In line-of-sight (LOS) environments, the precise positioning information from UWB assists in correcting the accumulated error of IMU. In NLOS environments, the corrected inertial navigation system (INS) information improves the system's robustness and accuracy. Additionally, we incorporate the carrier motion information into the INS using the extended Kalman filter algorithm and use the results for NLOS determination. The experimental results show that the tight coupling algorithm proposed in this paper can effectively eliminate the accumulated error of IMU. Under LOS conditions, the root mean square error is reduced by 29.9% compared with single UWB positioning. At the same time, the large-scale NLOS problem is solved while ensuring positioning accuracy. This method can reduce the deployment density of base stations in practical applications, expand the effective positioning range of the system, and make positioning in some NLOS areas possible.

This work aims to introduce for the first time the concept of "in situ release" for MEMS sensors and validate the feasibility of this technology using an accelerometer structure with a modified design. MEMS sensors enhanced with in situ release technology will be able to fix their movable structures in place using thermally decomposable materials during the fabrication process and can be released as needed during their operational phase on boards thereby enhancing the ability of the fragile MEMS structures to withstand acceleration overload events. This research covers the entire process of the in situ release technology including the selection and formulation of thermally decomposable materials, the development of a precise droplet addition system for the materials, the design and fabrication of a verification structure with microheaters, and the final testing phase. Driven by voltage signals, the microheaters functioned effectively, enabling the efficient decomposition of the thermally decomposable material and the restoration of the structure's mobility. The decomposition process was documented and analyzed, thereby validating the feasibility of the in situ release technology.

Human activity recognition (HAR) is an application of great importance in the Internet of Things (IoT). Inertial measurement units (IMU) on wearable devices provide the primary source of time-series data for HAR. This paper focuses on addressing the energy consumption and performance issues in real-world scenarios for continuous HAR using time-series signals. We present a continuous adaptive spiking neural network (CASNN) suitable for low-power wearable devices. CASNN is implemented by introducing early exit into SNN, and the early exit branches do not require additional classifiers. The results show that CASNN can reduce over 56% FLOPs and improve accuracy by 4% on average across two datasets, through additional cross-person generalization capabilities on the continuous HAR dataset.

Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.

With the continuous development of the IoT, compact wireless communication modules have become indispensable components, and their antennas are gradually being developed from external devices into onboard integrated devices. The serpentine antenna, a variant of the monopole antenna known for its small size and easy integration, is often applied to engineering practices. However, its performance has always been closely affected by the size of the surrounding grounding plane. By conducting a characteristic mode analysis (CMA), this study explored the variation patterns in the ground plane size and the resonant frequency. Based on the simulation results, it was clear that when the ground plane size is less than a quarter of the working wavelength, the ground plane will have a significant effect on the antenna's resonant frequency. Thus, this study further analyzed a serpentine antenna with a grounding branch, and through analysis of the basic law of the influence of grounding structure on the antenna's performance, we found that by adjusting the branch length, the matching performance of the antenna can be effectively improved. Furthermore, by changing the size of the ground plate, the antenna's resonant frequency can be adjusted. Such a conclusion will hopefully provide a reference for future designs of integrated antennas in engineering applications.

Silicon piezoresistive pressure sensors are widely used, and the pursuit of high linearity and high accuracy of sensors is always there. In this article, through simulations and calculations, including higher order piezoresistance effects, an S-shaped piezoresistor is proposed to optimize the linearity of pressure sensors; that is, the piezoresistive connecting arms are also lightly doped silicon. Two kinds of silicon piezoresistive absolute pressure sensors with a circular diaphragm in the range of 0–60 MPa were designed and fabricated, respectively, with tri-meander-shaped piezoresistors and S-shaped piezoresistors. After measurement, the linearity of the pressure sensor with S-shaped piezoresistors is about 30% better than that of the sensor with tri-meander-shaped piezoresistors, and the linearity and accuracy can reach 0.045% FS and 0.054% FS under the voltage source.

MEMS device degradation due to aging and other factors is becoming a major concern because it will cause parametric deviations and catastrophic failures in the mechanical and structural subsystems. However, MEMS testing in general which needs specific sophisticated testing equipment is complicated and time-consuming. To solve these problems, this paper specifically introduces a built-in self-test method which based on the periodic observation of the temperature-dependent output signal. A packaging scheme is designed and the test circuit is built to conduct test experiments on MEMS pressure sensors with different ranges and materials. The experimental results show that this method can effectively test the performance and will not affect the continued normal operation of the sensor. Furthermore, some compensation is made to correct the output to greatly improve the accuracy and reliability. Low cost, ease of implementation, and possibility to monitor in real time are the main advantages.

Ocean depth measurement is very important for ocean observation, prediction and development. In this work, an absolute piezoresistive pressure sensor with high sensitivity and high accuracy for full ocean depth measurement is designed and fabricated through theoretical calculations and simulations. The shape and dimension of the diaphragm are designed through finite element simulation. A thick circular C-type diaphragm is used and the stress distribution on the diaphragm is simulated. The influence of the shape, dimension and position of the piezoresistors on the sensitivity and nonlinearity is calculated and analyzed, and the performance of sensors with single-bar-shaped piezoresistors and sensors with meander-shaped piezoresistors with different spacing is further compared. A tri-meanders-shaped piezoresistor with a large ratio of piezoresistance to connecting layer resistance is designed, and the piezoresistor position with high linearity is determined through simulation. Non-glue oil-filled isolation package is used to improve the repeatability and hysteresis of the sensor. The pressure sensor has a range of 0–120 MPa, a sensitivity of 0.425 mV/V/MPa, and an accuracy of 0.0182 %FS.

Piezoresistive pressure sensors have been widely used in the industry and many other fields. However, conventional pressure sensor inspection is performed with the device off, which causes several inconveniences. In this article, an electrothermal actuator is designed and fabricated on the basis of bimetallic alloy material to realise the self-test of the piezoresistive silicon pressure sensor, and a kind of pressure sensor package structure with self-test capability is developed using the electrothermal actuator. Experimental results show that the structure can realise the dual excitation of the pressure and temperature of the diffused silicon pressure sensor core and enable it to return to the normal working state in less than 3.3 s. The self-test module can be driven at a low voltage of 0.8 V at less than 618 mW and can reflect the performance of the pressure sensor quickly and accurately. The structure is suitable for most differential pressure, absolute pressure sensor chips.

Indoor pedestrian positioning has been widely used in many scenarios, such as fire rescue and indoor path planning. Compared with other technologies, inertial measurement unit (IMU)-based indoor positioning requires no additional equipment and has a lower cost. However, IMU-based indoor positioning has the problem of error accumulation, resulting in inaccurate positioning. Therefore, this paper proposes a cascade filtering algorithm to correct the accumulated error using only a small amount of map information. In the lower filter, the zero-velocity correction and the attitude-extended complementary filtering (ECF) algorithm are utilized to initially solve the pedestrian’s trajectory. In the upper filter, a particle filter (PF) combined with the map information is adopted to correct the accumulated error of the heading and stride length. In the 2D positioning process, the root mean square error (RMSE) of the proposed algorithm is only 1.35 m. In the altitude correction, this paper proposes a method of clustering floor discrimination to deal with the instability of the barometer resulting from an uneven pressure and temperature. In the final 3D positioning experiment, with a total length of 536.5 m and including the process of going up and down the stairs, the end-point error is only 2.45 m by the proposed algorithm.

The heavily doped connecting layer is an indispensable part in the fabrication but usually an overlooked part in the design of piezoresistive pressure sensors. In this paper, we combined theories, experiments and simulations to study the influence of heavily doped connecting layers on the sensitivity of pressure sensors. Connecting layers and lightly doped piezoresistors contribute the output in their respective resistance ratio. Usually, the presence of connecting layers will reduce sensitivity due to the average piezoresistive effect. The low resistance ratio and appropriate induced stress of connecting layers are both beneficial for higher sensitivity. In addition, an extension phenomenon of connecting layers to piezoresistors is also shown. Some constructive suggestions for the design of the whole piezoresistor with connecting layers are proposed.

Piezoresistive coefficients of materials are of great significance to the design of piezoresistance based devices. This paper proposed an in situ extraction method to determine the piezoresistive coefficients of n-type 4H-SiC, which requires no separate experimental study on the material properties and can directly obtain device-level parameters. Two types of all-SiC piezoresistive pressure sensors based on the SiC sealed cavity structure with different piezoresistor arrangements were fabricated and characterized. Through parameter fitting of the experimental data of pressure sensors and the numerical calculation results obtained by parameterization finite element analysis method, the longitudinal and transverse piezoresistive coefficients $\pi _{11}$ and $\pi _{12}$ that resulted in the best fit were found to be $- 3.4\times10$ −11 Pa −1 and $6.15\times10$ −11 Pa −1 , respectively. The extracted parameters were also verified to prove the reliability of the extraction method. The above research has significant value for the understanding of piezoresistive effect in materials and provides guidance for the design and performance optimization of SiC-based sensors.

Robust and accurate human stride length estimation (SLE) using smartphone integrated inertial measurement units (IMU) is essential in pedestrian dead reckoning (PDR) and mobile health applications. However, the change of smartphone carrying mode (i.e. sensor location) in daily usage often leads to significant estimation errors. To address this problem, we propose a novel SLE framework called Mode-Independent Neural Network (MINN) using multi-source unsupervised domain adaptation (UDA) methods. First, we present a hierarchical neural network to extract spatio-temporal features based on multi-level ResNet and GRU. Then, we use adversarial training and a subclass classifier to build a UDA network that can extract mode-invariant features shared by the data from different modes. Finally, we integrate these architectures into an end-to-end learning framework. Through a systematic evaluation under the leave-one-out setting on two public SLE datasets, the MINN outperforms the state-of-the-art algorithms by achieving stride length error rates of 2.5% and 5.1% in supervised settings. We also evaluated the mode-independent adaptability of this model by performing single and multiple UDA tasks. The results demonstrate that the proposed MINN significantly improves the generalization of SLE model under new subjects or modes.

Sensitivity and nonlinearity are two of the most basic and important properties of microelectromechanical systems piezoresistive pressure sensors. In this paper, we proposed a simple method to enhance sensor performance by changing the diaphragm size, which can improve both sensitivity and linearity. To obtain close sensitivity for different diaphragms, we first fixed the ratio of the edge length to the thickness of the diaphragm. Then, the relationship between sensitivity, linearity, and diaphragm size was established by calculation, and the simulation was carried out using ANSYS. We found that both sensitivity and linearity increased with an increase in the diaphragm side length. In addition, we designed and fabricated two pressure sensors with square diaphragms in the range of 30 MPa. They have the same length-to-thickness ratio of 3.33, and the edge lengths of the diaphragms are 346 μ m and 599 μ m, respectively. The test results showed that the device with a larger edge length had higher sensitivity and lower nonlinearity, which is consistent with the conclusion above. Therefore, increasing the diaphragm size may be an easy and practical way to improve the performance of the pressure sensors.

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.