A MEMS Multi-Sensors System for Pedestrian Navigation

Navigation applications and location-based services are currently becoming standard features in smart phones with built-in GPS receivers. However, a ubiquitous navigation solution which locates a mobile user anytime anywhere is still not available, especially in Global Navigation Satellite System (GNSS) degraded and denied environments. Different motion sensors and angular sensors have been adopted for augmenting the positioning solutions for such environments. An electromyography (EMG) sensor, which measures electrical potentials generated by muscle contractions from human body, is employed in this paper to detect the muscle activities during human locomotion and captures the human walking dynamics for motion recognition and step detection in a Pedestrian Dead Reckoning (PDR) solution. The work presented in this paper is a consecutive step of our pilot studies in developing a novel and robust PDR solution using wearable EMG sensors. The PDR solution includes standing and walking identification, step detection, stride length estimation, and a position calculation with a heading angular sensor. A situation of standing still is identified from the EMG signals collected from a walking process, which has standing and walking dynamics, via a hidden Markov model classifier fed by sample entropy features. Such pre-classified processing reduces the misdetection rate of step detection. After step detection, two stride length estimation methods are investigated for the PDR solution. Firstly, a linear stride length estimation method based on statistic models is investigated to improve the accuracy of the PDR solution. Secondly, five different walking motions are recognized by a motion recognition algorithm based on some particular EMG features, and a fixed stride length is then set for each walking motion to propagate the position. To validate the effectiveness and practicability of the methods mentioned above, some field tests were conducted by a few testers. The test results indicate that the performance of the proposed PDR solution is comparable to that of a commercial GPS receiver in outdoor test under an open-sky environment.

A pedestrian dead-reckoning (PDR) system based on micro-inertial technology usually uses a sensor integrated with a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and is attached to the person's body (usually on the foot) to acquire the trajectory of the pedestrian. Although a variety of PDR system solutions have been proposed, the PDR solution for marking time and walking hybrid motions has not been formally proposed and solved. Therefore, in this paper, we propose the PDR system using a step-and-heading system scheme for marking time and walking hybrid motion. The main contributions include: 1) an adaptive gait partitioning method for walking and marking time hybrid motions is proposed; 2) a motion classification algorithm that uses a multi-layer perceptron to classify whether each step is walking or marking time is proposed; and 3) a new step length estimator based on several currently proposed models is proposed. Finally, an extended Kalman filtering algorithm combining heuristic heading reduction, flat-ground hypothesis, and cardinal heading aided inertial navigation techniques is used for heading estimation. We evaluate the effectiveness of the proposed algorithm through experiments, and the experimental results show that the average position error of the two groups of test experiments based on marking time and walking hybrid motion is 0.42% and 0.6%, respectively.

Estimating wind speed and direction in outdoor areas with high precision and resolution requires a large number of anemometers distributed in different locations. Compared with others conventional sensors, the micro electro mechanical system (MEMS) sensors are lighter and smaller, therefore, they can be integrated in large numbers. Moreover, due to their batch production, MEMS sensors are relatively low cost. Their main drawbacks are the dependence on the air temperature and the inability of detecting the flow direction. This paper deals with the design of a cylindrical-shaped probe equipped with two dual MEMS hot-film sensor chips calibrated in a wind tunnel. With the obtained results, a model based on artificial neural network is developed and implemented on Arduino board. This model is assigned to calculate the corresponding values of wind speed and direction with correcting the effect of temperature drift. Finally, outdoor tests are carried out at wind monitoring points measured by an opto-electronic anemometer and an opto-electronic wind vane. The obtained results are accurate and agree well.

Introduction Single atom catalysts have draw increasing attention of enormous researchers due to its atomically disperation on supports, high efficiency and high catalytic performance. Despite those obvious advantages, there is still rare study about the single atom catalyst’s application in sensors. It was firstly reported the palladium single atoms on TiO2 used as a photocatalytic sensing platform to analyzed organophosphorus pesticide chlorpyrifos[1]. Catalyst-based sensing platform opens up a new pathway for exploring novel strategy of design sensors. Micro-electro-mechanical system (MEMS) process have advantages such as, miniaturization, small size, mechanical parts high resonance frequency, and low power consumption. However, the MEMS prepared by traditional method possess poor stability. Here, we employed the atomic layer deposition to prepare ZnO layer on MEMS. Atomic layer deposition (ALD) is a powerful technique for depositing nanoparticles or thin films and has outstanding advantages, including precise control of size and thickness and excellent uniformity[2], except that the substrate is exposed separately to each gaseous precursor and that the deposition is broken into cycles[3]. ALD functionalized method could gain high performance in thermal stability, mechanical and catalytic performance[4]. It was interesting to obtain single atom MEMS sensors via ALD. Method Prior to ALD, MEMS (Shanghai lingpan Electronic Technology Co., Ltd) were put into ethanol and cleaned by ultrasonic agitation for ten minutes. MEMS were put on quartz wafers and dried in air. ZnO films were deposited onto the MEMS by a hot-wall, closed-chamber ALD reactor. N2 (99.999%) was used as carrier gas. The precursors of ZnO were diethyl zinc and water (H2O). Water acts as an oxidant and enables growth over a wide temperature range. The deposition temperature was 150 °C, and the diethyl zinc was kept at room temperature. After the deposition of ZnO layers, Pt single atoms ALD was subsequently deposited with (methylcyclopentadienyl)trimethylplatinum (MeCpPtMe3) and O3 as precursors at the same temperature as our precious work[2]. Ultimately, the MEMS sensors were tested. Results and Conclusions Fig.1 Response performance for ALD SnO2 layer on MEMS It can be seen from the Fig.1 that sensor response increased with the concentration increase gradually. With the concentration increase for about 1 PPM, response value of sensors gradually reduced. It could also be seen that ALD ZnO sensor have a response time of 90 s and recovery time of 120 s. Baseline with drift less than 4 also demonstrated its good stability. It has little increase in response performance as the concentration increased to 80 PPM, it could be caused by the surface electron transfer arrived balance within high concentrations References [1] X. Ge, P. Zhou, Q. Zhang, Z. Xia, S. Chen, P. Gao, Z. Zhang, L. Gu, S. Guo, Palladium single atoms on TiO2 as a photocatalytic sensing platform for analyzing organophosphorus pesticide chlorpyrifos, Angew Chem Int Ed Engl, (2019).[2] Q. Hu, S. Wang, Z. Gao, Y. Li, Q. Zhang, Q. Xiang, Y. Qin, The precise decoration of Pt nanoparticles with Fe oxide by atomic layer deposition for the selective hydrogenation of cinnamaldehyde, Applied Catalysis B: Environmental, 218 (2017) 591-599.[3] Z. Gao, Y. Qin, Design and Properties of Confined Nanocatalysts by Atomic Layer Deposition, Acc Chem Res, 50 (2017) 2309-2316.[4] M.M. Biener, J. Biener, A. Wichmann, A. Wittstock, T.F. Baumann, M. Baumer, A.V. Hamza, ALD functionalized nanoporous gold: thermal stability, mechanical properties, and catalytic activity, Nano Lett, 11 (2011) 3085-3090. Figure 1

Microelectromechanical systems (MEMS) are core components in unmanned aerial vehicles (UAV). The precision of MEMS sensors is very important when the GPS signal is invalid. However, the precision and performance of MEMS sensors will be degraded by the changing of environment. Therefore, estimation and identification of the various noise terms existing in MEMS sensors are deemed to be necessary. The Allan variance is a common and standard method to analyze MEMS sensors, but it cannot be used to analyze the dynamic characteristics. The dynamic Allan variance (DAVAR) is a sliding version of the Allan variance. It is a practical tool that could represent the nonstationary behavior of the MEMS signal. As the DAVAR needs to estimate the Allan variance at each time epoch, the computation time grows significantly with the length of the signal series. In this paper, in the case of MEMS gyroscope on UAV, an improved fast DAVAR algorithm based on the choice of relevant time is proposed to shorten the computation time. As an experimental validation, numerical experiments are conducted under the proposed method. The results demonstrate that the improved method could greatly increase the computation speed and does not affect the accuracy of estimation.

Smart reinforcement steel bars with low-cost MEMS sensors for the structural health monitoring of RC structures

Commonly encountered problems in the manipulation of objects with robotic hands are the contact force control and the setting of approaching motion. Microelectromechanical systems (MEMS) sensors on robots offer several solutions to these problems along with new capabilities. In this review, we analyze tactile, force and/or pressure sensors produced by MEMS technologies including off-the-shelf products such as MEMS barometric sensors. Alone or in conjunction with other sensors, MEMS platforms are considered very promising for robots to detect the contact forces, slippage and the distance to the objects for effective dexterous manipulation. We briefly reviewed several sensing mechanisms and principles, such as capacitive, resistive, piezoresistive and triboelectric, combined with new flexible materials technologies including polymers processing and MEMS-embedded textiles for flexible and snake robots. We demonstrated that without taking up extra space and at the same time remaining lightweight, several MEMS sensors can be integrated into robotic hands to simulate human fingers, gripping, hardness and stiffness sensations. MEMS have high potential of enabling new generation microactuators, microsensors, micro miniature motion-systems (e.g., microrobots) that will be indispensable for health, security, safety and environmental protection.

The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given.

A high-resolution area-change-based capacitive MEMS tilt sensor

A signal shift following solder reflow or temperature cycle stress testing can severely affect micro-electro-mechanical systems (MEMS) product performance in encapsulated package. This is mainly attributed to nonlinear material property of the mold compound, a key low cost packaging material. Due to viscoelastic characteristic, the mold compound exhibits a temperature and time dependent behavior. Above the temperature of glass transition (Tg) of the mold compound, the viscoelastic behavior is more significant. Thus, when the MEMS package is subjected to high temperature such as solder reflow process, a package induced internal stress on the MEMS sensor is generated depending on the maximum temperature reached and cooling rate. This results in the signal shift. Minimizing this signal shift plays an important role in MEMS product quality and performance. In this study, high Tg mold compounds (Tg: 200°C) were evaluated to alleviate the signal shift. Material property characterization tests were performed and test vehicles were assembled with the selected molding compounds. The MEMS sensor signal was measured before and after the temperature cycle test. Also, the viscoelastic material property of the mold compound was used in finite element analysis to investigate the resulting deformation and stress of the MEMS sensor. Compared to a baseline mold compound (Tg: 110°C), the high Tg molding compound did show about 10% less signal shift in the temperature range of -40°C to 125°C.

Inertial Measurement Units (IMUs) using MEMS (Micro Electro Mechanical Systems) sensors, to detect rotation and acceleration, are extremely sensitive to vibration and shock. Because these IMUs are used in aircraft, missiles, and even in gun launched projectiles, the vibration and shock levels can be extremely high. We have been successful in designing isolation systems that protect the MEMS sensors from these harsh environments. These isolation systems attenuate the shock and vibration in frequency ranges that would otherwise degrade IMU performance and even damage the sensors. In designing an isolation system, for an IMU with MEMS devices, the designer must first acquire a good definition of the shock and vibration environment to which the IMU will be exposed. Then the designer must determine the frequencies and levels to which the MEMS devices are sensitive. Finally the designer must have knowledge of IMU properties such as resonant frequencies, mass properties, clock frequencies, etc. By knowing all of this information, and how it affects IMU performance, the designer can determine the proper parameters for the isolation system. A well designed isolation system using these parameters will be capable of protecting the MEMS devices while maintaining IMU performance through out all specified environments.

This paper reports on a grasping forceps with a triaxial Micro Electro Mechanical Systems (MEMS) tactile sensor on a tip. The laparoscopic surgery is minimally invasive because the incisions are smaller than the open surgery. This results in fast recovery. However, it is a problem in the laparoscopic surgery to damage an organ by localized stress generated by grasping with a thin forceps. To avoid excessive stress applying to the organ, real time evaluation of the stress is important. However, there is no acceptable tool to measure the stress. We propose a grasping forceps with a triaxial MEMS tactile sensor on a tip for a measurement tool. We attached a triaxial MEMS tactile sensor which we have developed on a tip of a grasping forceps. The MEMS sensor can measure not only the pressure but also two directional shear stresses applied to the sensor surface. The sensor size is 7 mm × 7 mm × 2 mm. It is enough small to attach the sensor to the tip of a forceps 12 mm in diameter. In this paper, the characteristics of the forceps with the MEMS sensor during grasping, pushing and pulling actions were evaluated. In these experiments, output of each sensor for pressure and shear stress was proportional to the applied stresses, respectively. Moreover, as an in vivo experiment, we measured the shear stress applied to a pig liver block when it is lifted after being grasped with the forceps. We obtained that the shear stress applied to the liver block increased with the increase of the weight of the liver block.

Flexible and composite structures often develop hidden damages, e.g. cracks or delamination between laminae or phases. Such events could be sensed through embedded structural health monitoring (SHM) systems, but past experimental studies in the literature proved that embedding sensors for SHM purposes may decrease the reliability of the structure, as the sensor acts as an inclusion. In former studies, the authors proposed to adopt a surface-mounted SHM approach based on inertial MEMS (micro electro-mechanical systems) sensors, which has two advantages: it is low cost and also it avoids the afore-mentioned detrimental effects on the endurance limit state of the structure. However, the low accuracy of the MEMS sensors and the type of response that they can measure may hinder an effective monitoring of the structural state; this can be overcome through redundancy and an efficient sensor placement. In this article, an automated approach is discussed for obtaining the optimal topology of a sparse MEMS sensor network. In this regard, the scenarios are assumed unknown in terms of extent and location of stiffness degradation due to damage. The optimal sensor locations are obtained via maximization of the global sensitivity of the measurements to damage. The method can be also implemented in a multi-scale framework, to efficiently handle (micro) sensors, (meso) damaged regions and (macro) structural components of (by far) different sizes. Data related to composite specimens and panels are discussed, with the aim of assessing the identifiability of damage through self-adapting theoretical/numerical (digital) twins of the monitored structures.

We describe an experimental and theoretical project centered on the use of nonlinear acoustic shock impulses and microelectromechanical systems (MEMS) sensors to detect and image buried, regular-shaped inclusions at various depths in soil. The use of shock waves is partly motivated by the perceived limited capability of conventional acoustic delivery modes, and by recent research which shows that shock impulses travel efficiently in granular media.

A Map aided Pedestrian Dead Reckoning (PDR) algorithm is proposed to mitigate the drift errors and step detection limitations of pedestrian dead reckoning algorithm with handheld sensors in indoor and outdoor spaces. Specific to this context is the changing lever-arm between the handheld device and the pedestrian center of mass that introduces a misalignment between the inertial sensors and the walking directions. To address these challenges, an adaptive routing graph is built based on possible pedestrian's motions, which depend on personal mobility profile and surroundings. An adaptive decision process is also developed to fuse map data with GNSS positions and PDR outputs in a particle filter. The performance is assessed with 1km walk experiments. Main contributions are (1) the calibration of the PDR step length model using both GNSS and map data during straight line travels with miss/over-detected steps modeled by the particle filter; (2) the estimation of angular misalignment between the walking and the handheld unit pointing directions in geometrically constrained areas; (3) a dynamic choice of opportune periods and measurements to calibrate the PDR outputs and improve the positioning process.