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In modern wars, the unmanned aerial vehicles (UAVs) have become the main means of local high-precision strike. The ground tracked vehicle and the ground wheeled vehicle are widely used to assist soldiers in ground operations because the vehicles can carry a variety of weapons while still move quickly. Therefore, in order to realize the strike from the UAV, it is of great significance to classify ground targets correctly. In this paper, a classification method based on micro-Doppler signatures and the improved Ensemble Empirical Mode Decomposition (IEEMD) is proposed. At first, models are built to describe the micro-Doppler characteristics of ground targets. Measured data of corresponding targets is analyzed. Secondly, principle of IEEMD is given and comparison of IEEMD and Ensemble Empirical Mode Decomposition (EEMD) is also made to prove the superiority of IEEMD in accuracy and calculation. Thirdly, three micro-Doppler fractal features are extracted from different Intrinsic Mode Functions (IMFs) obtained by IEEMD. Combined with Genetic algorithm-back propagation (GA-BP) neural network, accurate classification of ground targets is realized. Last but not least, classification experiments in different cases are carried out to indicate the effectiveness of proposed method. Comparison with current algorithms under various signal-to-noise ratios (SNRs) demonstrates that method in this paper has higher accuracy and better anti-noise performance.

One of the critical advantages of a high-altitude airship (HAA) is its ability to carry multiple-task payloads for remote sensing monitoring on the plateau. We developed a multi-payload observation system based on the HAA (ASQ-MPHAAS) for remote sensing monitoring of the Qinghai plateau. In this paper, we demonstrate the design, development, implementation of the ASQ-MPHAAS. The ASQ-MPHAAS includes two parts; one part is our HAA (ASQ-HAA380), and the other part is our multi-payload observation system (ASQ-MPOS-1). The ASQ-MPOS-1 consists of two hyperspectral push-broom imaging sensors, a multispectral imaging sensor, a high-resolution RGB camera, a video sensor, a Position and Orientation System (POS), and other components. The ASQ-MPOS-1 is installed under the ASQ-HAA380. We also introduce some key methods of data processing we propose to solve unique problems brought by HAA. Four real-world field experiments are presented in this paper to demonstrate that the ASQ-MPHAAS can capture different kinds of remote sensing data at a high spatial resolution of a few or dozen centimeters.

This paper reports on a method for strain calibration of fiber optic sensors which have been adhesively bonded to a structural member. Generally, the coefficient of strain transfer ( $k_{\mathrm {ST}}$ ) can be used to calibrate the strain sensitivity of the surface bonded FBG. Here we propose an alternative method to determine $k_{\mathrm {ST}}$ which is based solely on observing the temperature-dependent behaviour of the adhesively mounted FBG sensor. In this paper we prove the validity of our method by comparing $k_{\mathrm {ST}}$ determined via a conventional strain (mechanical) calibration and via our alternative temperature (thermal) calibration. A set of 18 surface bonded FBGs with coefficients ranging from 0.5 to 1 are investigated. The average deviation of $k_{\mathrm {ST}}$ determined via a temperature calibration versus a mechanical strain calibration was ~5%, with a maximum deviation of 13% obtained from the set of bonded sensors measured in this work. This led to an average relative accuracy of < 6%, when the strain sensors were calibrated using our temperature-based calibration method.

Bat navigation via echolocation has inspired numerous acoustic-based obstacle sensing techniques. In this paper, we use a low-cost, small size, single frequency active binaural system for simultaneous detection of distance and direction of extended obstacles using the time of flight cue. For a binaural system and given size of the transducer, the minimum tolerable separation distance between the emitter and receiver as well as best frequency range for effectively sensing a barrier of any orientation will be assessed by simulation. With the given size of transducers of 6.30 mm in radius and for a working distance of 1 m, the minimum separation distance between the emitter and receiver should not be less than 5 cm and the emitter frequency should not exceed higher than 20 kHz. It will be shown in this article that higher frequency limits the lateral distance detection ability of the system. We experimentally verified the technique for detecting a wall of any orientation with respect to the system’s axis. The approach developed in this paper could be useful for mobile robotics, indoor navigation, and personalized navigation, where a compact configuration is of interest.

Robotic manipulators face significant challenges when handling objects of different sizes and shapes. Incorporating a sense of touch into these devices has the potential to improve performance and dexterity. In this paper, a bio-inspired approach is presented for slip detection and suppression during object manipulation. The method was inspired by the behavior of FA-I afferents located in the glabrous skin that encode sliding motion of objects over the skin. The proposed slip detection method encodes object motion captured by a slip sensor into spikes, following principles of neuromorphic sensing. The spikes are used as the feedback signal for an event-based closed-loop control system. The controller behaves in a reflex-like manner and actively engages the robotic fingers to increase grip force and suppress slip. A Dynamic Adaptive Threshold method was designed to improve slip detection for different surface properties of grasped objects. The performance of the method was evaluated following situations of dynamic slip caused by a sudden or gradual increase in object weight. The results demonstrated the feasibility of the proposed method. Slip events were suppressed before complete object slippage in 80% of all experimental trials. The response time ( Delta {\text {t}} < 85$ ms) was compatible with the time for grip force adjustments in humans. This paper explored event-based touch applicable to the problem of manipulation, which is less explored than event-based tactile perception and shows promising prospects for both robotics and prosthetics. - 2001-2012 IEEE. Scopus

This paper presents a novel variable reluctance differential solenoid transducer (VRDST) that offers improved high-speed sensing performance over linear voltage differential transformers (LVDTs) and differential variable reluctance transformers (DVRTs). The VRDST has the unique ability to measure both position and velocity simultaneously using two independent measurements. This paper investigates a basic geometry for a VRDST. The position and velocity measurement methods are derived and implemented in a Simulink simulation. The simulated VRDST model is augmented with an FEA simulation to predict the magnetic characteristics of the investigated design. The results and predictions established by the simulation and analytical models are validated experimentally with a physical prototype. The similarity between the experimental and simulated results suggest the proposed FEA and Simulink simulations can be used to accurately predict the performance of a physical VRDST. The findings from the analytical modeling, simulation study and experimental validation all unanimously prove the position measurement performs well when measuring low-speed displacements, while the integrated-velocity measurement is useful for measuring high-speed displacements. The differing frequency ranges of these two independent measurements are found to complement each other and suggest the VRDST is superior compared to DVRTs or LVDTs for applications requiring wide bandwidth position measurements.

Due to the rising cases of a prescribed drug overdose and mishandling of the medication container there is an urgent need to devise a smart technological solution to assist this issue. One of the possible solutions to the above issue is the use of low-cost pressure sensor integrated with the medicine container whose data can be communicated to the dedicated smartphone Android application. Once the patient taps the medicine out, the pressure sensor sends the data to the smartphone which can be monitored by the patient’s family and the sensor can then be discarded. In this work, a simple all paper based pressure sensor to monitor drug dosage is fabricated using Molybdenum disulfide (MoS2) grown on paper with an interdigitated electrode drawn using graphite pencil on paper as electrodes. The sensitivity of the fabricated pressure sensor was found to be 0.32 kPa−1 which is comparable and even better than the pressure sensor fabricated using sophisticated techniques. Repeatability of the sensor over 2000 cycles indicates an excellent long-term stability. Bending cycle studies over 1000 cycles observes a negligible change in the performance suggesting the high reliability of the sensor. The fabricated pressure sensor was integrated successfully in the medicine package to monitor the real time pills intake by a patient which shows high promise for its use in health care industry. The successful demonstration of the smart drug dosage system is a step ahead in health care industry which opens up many avenues of research in the similar field.

The Internet of Things (IoT) is a wide network consisting of Internet-connected objects using installed software, such as home appliances, vehicles, and other entities embedded with sensors, actuators, radio-frequency identification (RFID), and electronics to exchange data. In the last two decades, numerous IoT solutions have been developed by small, medium-sized, and large enterprises to make our lives easier. Furthermore, private and academic researchers have extensively investigated some practical IoT solutions. The rapid expansion of IoT solutions accompanies numerous security concerns because the underlying IoT protocols and communication technologies have not considered security. Recently, blockchain has emerged to become one of the promising technologies that might overcome some of the IoT limitations (security limitations, in particular). Blockchain technology is a database ledger that uses a peer-to-peer (P2P) network and stores transactions and asset registries. Blockchain can be described as a mounting list of records (i.e., blocks) with the following properties: distributed, decentralized, immutable, and shared. This paper surveyed recent security advances to overcome IoT limitations using blockchain. In this article, the blockchain attempts to overcome IoT limitations that are related to cyber security have been classified into four categories: end-to-end traceability; data privacy and anonymity; identity verification and authentication; and confidentiality, data integrity, and availability (CIA). Intended as a guideline for future research, this paper also explores systematic processes.

This paper deals with the leader-following control of multiple autonomous mobile robots based on a new sensing method. In this method, we have mounted a light source on the leader robot and installed a photovoltaic sensor at the front end of the follower robot. The photovoltaic sensor consisting of seven micro solar modules acts as a light sensor for the follower robot which helps it to move in accordance with the leader robot by detecting the light source of the leader robot. From the relative angle measurement and ultrasonic measurement of the relative distance, angle offset and distance information between leader robot and follower robot can be estimated. Then, based on an intuitive control method for angle steering proposed in this paper, the follower robot can be controlled smoothly to achieve leader following purpose. The new leader-following control method could overcome the disadvantages of other existing formation control methods. The experimental results show that the leader following based on light source tracking is a comparatively efficient, accurate, and cost-effective method.

Detection and tracking of different moving objects in the surrounding environment is an essential capability for any robotic perception system. This enables forecasting of object trajectories and safe navigation of a mobile robot, such as a self-driving car, toward its goal. In this paper, we demonstrate a multi-object tracker from image data in a tracking-by-detection paradigm. The tracking algorithm imposes no prior requirements on the tracked objects. This paper demonstrates how with a simple and intuitive cost function, competitive and real-time performance is achieved. Furthermore, the multi-object tracker is combined with a stereo visual odometry system to obtain a more complete knowledge of the environment. Beside 2D object detections and tracking in the image domain, this provides robot motion estimates and 3D object trajectories in the world frame. Evaluation and discussion of the system is presented along with a study on the effect of removing features lying on tracked objects on visual odometry performance.

In this paper, a task allocation strategy for multi-target inverse synthetic aperture radar (ISAR) imaging in radar network is proposed by using the potential game theory. The multi-target ISAR imaging task allocation problem in this paper refers to that the targets are in different radar beams and the purpose is to accomplish all the imaging tasks with high resolution within the limited time. First of all, the multi-target imaging task allocation problem is formulated. According to the imaging task allocation problem, a resolution optimization game is proposed to search for a satisfied strategy profile for all the targets, and the proposed game is proven to be an exact potential game which has at least one Nash equilibrium (NE). The optimal and suboptimal solution of the imaging task allocation problem can converge to a pure-strategy NE. Then, a best response-based imaging task allocation algorithm is proposed to solve the proposed game model. The simulation experiments demonstrate that the proposed game approach which can achieve a satisfied strategy profile for the targets with a lower computation complexity is an effective method for the multi-target imaging task allocation problem.

This paper presents, for the first time, a microwave gas sensor utilizing Poly (styrene-co-maleic acid) partial isobutyl/methyl mixed ester (PSE), which is a sensing material, as a coating on an interdigital resonator. The proposed sensor is based on a near-field transmission-line technique to detect the changing of the dielectric properties of the sensing material. The sensor was designed based on a DiClad 880 substrate with an interdigital resonator, operating at 4 GHz, on the middle of an ungrounded coplanar waveguide transmission line structure with rejection characteristic by coating the PSE solution as a sensed-gas material. The measured transmission coefficient, $S_{\mathrm {21}}$ , was changed depending on the dielectric properties of the PSE change due to various concentrations of gas targeted in a range of 50 to 1,000 ppm (50 ppm, 200 ppm, 500 ppm, 1,000 ppm) under a static system in a closed chamber at room temperature. The extracted results of differential transmission coefficient change, $\Delta S_{\mathrm {21}}$ , from four samples of ammonia concentration can be separated impressively. To confirm the selectivity performance of ammonia gas, three interfering gases, i.e., acetone, ethanol and methanol, were investigated. The measured results show that the proposed sensor can discriminate ammonia gas from three interfering gases over the entire concentration range in only three minutes. Moreover, it has many advantages, such as low cost, real-time monitoring and room temperature operating. The PSE-coated sensor described in this paper is suitable for a high-performance selectivity in ammonia gas sensing applications both in industrial processes and in agricultural products.

Cylindrical shell vibration gyroscope is a kind of solid wave gyroscope which uses the inertia effect of elastic wave to detect the rotation angular velocity. Precision dynamic balance is the key to improve the stability of gyro standing wave vibration. In this paper, precision dynamic mass-stiffness balancing along the working mode axis at the top of the resonator cup-wall is studied. The radial region of different trimming types and the variation of resonator natural frequency and frequency split with trimming hole depth are determined by the simulation. The femtosecond laser precision trimming-ablation experiment verifies the simulation results. The experiment and simulation have good consistency. This paper clearly clarifies the precision dynamic mass-stiffness balancing law of trimming along the working mode axis, which provides a guarantee for high efficiency and high precision trimming.

This paper presents a novel shunt tuning method suitable for transformer-less ultrasonic sensors in park assist systems. The method enables tuning of an inductive part of LR shunt that is used for a transducer damping. The method relies on the knowledge of a transducer own serial resonance frequency and the measurement of a parallel resonance frequency, which corresponds to the transducer parasitic capacity and shunt inductance. From the ratio of these two resonance frequencies, the method predicts how the inductance must be tuned to achieve optimal damping performance. To enable measurement of the parallel resonance frequency inductance scaling by a factor of four is employed. The scaled inductance shifts the parallel resonance frequency away from the serial resonance frequency and thus distinguishes them. This paper is supported by practical experiments using a fabricated test chip with a tunable synthetic inductor that confirms its performance and shows improvements compared to previous solutions without adaptive tuning mechanisms.

This paper aims at comparing micromachined cantilever structures with the purpose of providing design guidelines towards high performance energy harvesters such that they provide a good output power, resonant frequency and volume tradeoff, while considering microfabrication process limitations. Increasing the power output of piezoelectric energy harvesters by tapering the beams has been presented as promising solution in the literature. This paper investigates the power output of several geometric variations of cantilever beams, and examines the advantages of balancing the strain distribution throughout the beam. A comparison of the impact of different geometries is presented, and recommendations are given. Namely, eight rectangular and trapezoidal T-shaped designs are fabricated and benchmarked. Their resonant frequencies and power outputs are compared for the same available area (1800 μm× 800 μm). Measurements show that the trapezoidal designs can have a higher output power depending on the beam length to mass length ratio, in comparison to the rectangular T-designs that have lower frequencies. Resonant frequencies ranging from 2.9 to 7.2 kHz and power outputs ranging from 2.2 to 7.1 nW are reported.

A new cuff-less blood pressure (BP) sensor is designed and optimized in this paper. This BP sensor is in a shape of a circular disc of polymer enclosing an electrode. As the sensor is attached to the wrist artery, the dynamic bending of the sensor due to small artery pulsation induces variation in the electrode strain, and then, its resistance varies. With adequate amplification to sense resistance variation, the pulsation signals can be successfully obtained. With the measured pulsation representing well the blood volume pulsation (BVP), BP can be estimated next via the pulse wave velocity (PWV) theory. Effort is dedicated by this paper to optimize the sizes and geometry of the proposed sensor to maximize the electrode curvature change for largest measured signal-to-noise ratio (SNR) possible, which maximizes the accuracy of BP estimation. To this end, the optimization via the finite element modeling and the Taguchi method is carried out. In results, the optimal sizes in height/radius, fillet, hardness, and the electrode position of the polymer disc are successfully derived, which are in the height of 2 mm, the radius of 6mm, the distance between the planar electrode and the bottom surface of sensor being 1 mm, the hardness of the encapsulating polymer being 25 in shore A hardness, and the fillets at the bottom of the polymer disc being 1 mm in radii. BP is measured for 59 subjects. The resulted errors are 3.60 ± 5.40 mmHg (mean difference ± standard deviation) and 1.15 ± 1.63 mmHg for SBP and DBP, respectively. The correlations are 0.8787 and 0.9648, demonstrating well the favorable performance of the developed sensor.

Electrical impedance tomography (EIT) imaging modality has great potential on industrial applications with the advantages of being high temporal resolution. It is especially useful in cases, such as geophysical detection, landmine detection, and detections on non-transparent region, where the measurement data are only available from single surface, for data acquisition. Instead of the circular EIT model that uses the traditional circular electrode model, in this paper, planar array EIT is implemented, aiming to visualize a pipeline transporting a two-phase flow. The planar array can explore spatial information within its detectable region by producing 3D images, which have a higher spatial resolution in the axis direction than a traditional EIT with a dual-plane electrode sensor. However, in solving the inverse problem of a 3D subsurface EIT using a planar array, the images may be degraded, especially in cases where the location of the target is relatively deep. The total variation (TV) algorithm as block prior assumption-based regularization method has the potential to improve the image quality, and some works have shown that TV reconstructs sharper images, which provides an advantage when representing spatial information. In this paper, the performance of subsurface EIT using the TV algorithm for 3D visualization is presented based on simulations and experiments, and the results of quantitative measurement of depth are discussed.

Surface electromyography (sEMG)-based hand gesture recognition (HGR) is finding its application as a general-purpose wearable human machine interface (HMI). However, the same is rarely explored for foot gesture recognition (FGR). The FGR is interesting as a hands-free controller, e.g. in case of an electric guitar player who controls the musical effects by foot pedals. Yet, the FGR is challenging, since general leg movements such as walking can be classified as foot gestures. In this paper, the application of a minimalistic sEMG wearable footband is demonstrated as a hands-free HMI. With only two channels and a support vector machine (SVM) classifier, the system is able to classify five foot gestures. In addition, we added a locking/unlocking mechanism and also controlled by one of the gestures, which eliminates undesired gesture classification during general leg movements. We show that the gesture-controlled locking feature is robust, and system does not unlock during walking, jumping, climbing the stairs, and similar. This paper covers the system design, the real-time classification algorithm, the selection of the target muscles, and experimental results. Sessions independence, accuracy, and robustness of the device are tested in a total of 18 sessions with three volunteers having different usage skills. As case study, the system was interfaced with a Window 10 application developed for controlling musical effects with the foot wearable band as a hands-free alternative for the DJ mixer equipment.

This paper formulates and solves a multi-objective joint optimization problem where both the sum-throughput and fairness of a radio frequency (RF) energy harvesting wireless powered communication network (WPCN) are maximized using multiple beamforming hybrid access points (H-APs) and backscatter communication-enabled combination sensors. The paper proposes the multi-source, multi-sensor blind adaptive beamforming with combination sensors (MS2-BABF/combo) protocol. The protocol is analyzed to determine its performance with metrics, including WPCN sum-throughput, fairness in the achievable rates by sensors, sum-throughput and fairness tradeoff, and sensor dropout rate. Numerical results show that the MS2-BABF/combo protocol delivered up to approximately 296% increase in sum-throughput compared to the reference time-switching (TS) RF energy harvesting protocol in a dynamic environment, achieved a dropout rate of 0% instead of 28.5% by the reference TS protocol at 10mW H-AP transmission power, and increased Jain’s fairness index from J = 0.76 to up to 0.90 and 0.80 with and without sensors operating in backscatter mode, respectively. The findings are significant for future widespread adoption of WPCN systems by increasing its performance thus the versatility of applicable scenarios in the real world.

Vibrating ring gyroscopes (VRG) have been shown to achieve a high scale-factor with low non-linearity while some structural modification can help increase their shock resistance in space applications. With some tuning, their quadrature error can be nearly eliminated, thus, minimizing device sensitivity to external factors while keeping a good alignment between the drive and sense-mode frequencies. VRG design is optimized for high scale-factor, low non-linearity, and high shock-resistance while satisfying process related constraints. When the device wafer is processed independent of the capping wafers, the process imposes topology constraints on the device trenches and forces anchors and other stator features to be on the outside of the device. In this paper, we have shown how a VRG that has been designed with a radially outwards topology, having its anchor in the center as the innermost component, can be reconfigured into a VRG with a radially inwards topology with its anchors at the rim as the outermost component. This design transformation can be construed as a VRG design migration from a three-wafer process to a single-wafer process. The process-driven reconfiguration comes at the cost of a lower scale-factor but with no impact on the operating range of the original design. The paper further proposes the use of directional masses to achieve better inertial modal control of the VRG. The resulting radially inwards VRG has been fabricated in the standard SOIMUMPS process, and preliminary experimental results have shown the functionality of the device under atmospheric pressure.

The development of compact and low-cost dissolved oxygen (DO) sensors is essential for the continuous in situ monitoring of environmental water quality and wastewater treatment processes. The optical detection of dynamic and reversible quenching of fluorescent dyes by oxygen has been used for DO sensing. In this paper, we have optimized a multilayer optofluidic device based on the measurement of fluorescence quenching in a Ruthenium-based oxygen sensitive dye by employing total internal reflection (TIR) of the excitation light to achieve sensitivity enhancement for the detection of 0-20-ppm DO in water. The incident angles of light and sensitive layer thickness are optimized experimentally in order to increase the path length of light in the sensitive layer of the device through multiple reflections. A model is developed to demonstrate how light propagates through different layers of the device at varying angles of excitation and to describe the mechanism of fluorescence generation for each of the types of TIR observed. The design principles identified in this paper may be applied to the development and optimization of new multilayered optofluidic sensors by employing TIR for sensitivity enhancement.

This paper delineates a technique for characterizing the dielectric properties of biological materials. A novel design of co-axial fork-type probe with tilted spikes on either side of the central coaxial line to enhance directivity is proposed. The simulation studies were carried out in CST microwave studio to evaluate far-field radiation, S-parameters, and Smith chart. To compute the dielectric parameters of biological sample, a shift in resonant frequency and quality factor of the probe were observed. A theoretical model is developed to calculate the permittivity and conductivity of samples. The model was developed using the simulation analysis and validated on 27 samples. Values of dielectric parameters were obtained from the simulations and compared with standard reported values. The experimental measurements were carried out on 23 samples at room temperature over the frequency range from 5.5 to 7.5 GHz. The repeatability of the measured results has been analyzed by doing different runs, and in this paper, we have observed standard deviation in permittivity is up to two units, which confirms that measurements are consistent and repeatable. To understand the enzyme activity on proteins, the dielectric constant of mix solution of protein (such as egg white, egg yolk, plant protein, and insulin) and papain enzyme at the ratio of 10:1 was investigated through the proposed microwave technique. The decrement in dielectric constant was observed when proteins were added with papain enzyme. This paper on dielectric properties may provide an insight to understand the interaction of proteins with the enzyme. In addition, a microwave surface imaging approach is also used to characterize the dielectric properties of inhomogeneous and composite biological substances.

Cross-range scaling is very important for the inverse synthetic aperture radar (ISAR) imaging of non-cooperative target, and it is useful for the target feature extraction and recognition. In this paper, a novel approach for the ISAR cross-range scaling of maneuvering target is proposed. It assumes that the target has a non-uniform rotation, and the initial rotation velocity and rotation acceleration can be estimated. Through the rotational correlation of the instantaneous ISAR images sequence generated by the range-instantaneous-Doppler (RID) method, an approximate linear relationship between the time and the rotation velocity can be obtained to estimate the rotational parameters. The experimental results demonstrate the effectiveness of the novel algorithm proposed in this paper.

Notice of Violation of IEEE Publication Principles "Miniaturized Five-Axis Isotropic Tactile Sensor for Robotic Manipulation" by Zhongyi Chu, Lin Su, Gen Chen, Jing Cui, and Fuchun Sun in IEEE Sensors Journal, Vol. 19, No. 22, Nov 2019 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper contains portions of text from the paper(s) cited below. A credit notice is used, but due to the absence of quotation marks or offset text, copied material is not clearly referenced or specifically identified. "Tactile Sensing for Gecko-Inspired Adhesion" by X. Alice Wu, Srinivasan A. Suresh, Hao Jiang, John V. Ulmen, Elliot W. Hawkes, David L. Christensen and Mark R. Cutkosky in the Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Miniaturized multi-axis (force & torque) tactile sensors are paramount for the robot system to interact safely with the external environment, especially for the controlled adhesive robots. However, there exists a drawback--measurement anisotropy, which prevents sensors from performing equally well in all the directions and the high degree of integration. Based on the plate capacitive mechanism, this paper introduces a miniaturized 5-axis tactile sensing method with a novel doublelayer sensitive structure. Practically, shear force is detected by measuring the change of overlap area in the upper-layer-sensing cell, while the normal force and moment can be obtained by the variable space between two plates arranged in a 2 × 2-grid in the lower-layer-sensing cell. Moreover, in order to achieve the measurement isotropy, the relationship between the force/torque and the capacitance is clarified to facilitate the independent adjustment of the sensitivity of each axis. Based on the methodology, a miniature 5-axis flat tactile sensor is manufactured. The experimental results show that the shear sensitivity of the prepared sensor is over 0.2557pF/N within 7 N, reaching the same magnitude as normal force (0.2859pF/N over 3N range). The sensitivities of torque are over 1.8406 (N · m) -1 with a full-scale range of 0.04 N·m. The results demonstrate that the miniaturized tactile sensor is capable of isotropic measurement among 5-axis for robotic manipulation.

International audience; This paper presents a 3D printing technique based on stereolithography and direct light processing for the fabrication of low resonance frequency thin plates suitable for acoustic metamaterials applications. It was possible to achieve a better resolution with respect to other 3D printing methods such as fusion deposition modeling and to obtain plates with a thickness of 70 μm. The plates were characterized using three different methods: laser Doppler vibrometer supported by modal analysis, impedance tube measurements backed by a transfer matrix model and nanoindentation. All results are in good agreement. The physical parameters retrieved through the characterization methods can be used for future designs and integrated into finite element analysis to better predict the noise impact of these materials. Thanks to the small radius and thickness of the plates presented in this paper and to their low resonance frequency, it is suggested that they could be arranged in various configurations and used as unit cells in acoustic metamaterials applications for noise attenuation in small-scale electroacoustic devices.

The paper presents a novel fast barycentric-based back projection (BP) algorithm for radar imaging using the synthetic aperture radar (SAR) technique. The innovation in the proposed method is in the determination of the reverse virtual synthetic aperture beam for each point of the observed radar scene and in classifying which portion of raw radar data must be considered to complete imaging processing. This approach allows the classical well-known BP algorithm to be significantly accelerated, which is one of the most computationally demanding methods in SAR imaging. The proposed barycentric-based BP method gives the opportunity to control the resolution of the processed image by changing the width of the calculated beam. Moreover, the described solution is still highly parallelizable such as the classical BP algorithm. The solution can be adapted to a real-time SAR system with modern high-end computational units such as graphical processor units (GPUs) that support parallel computing. In this paper, the BP algorithm is clearly described with its barycentric-based modification. The proposed method is compared with a classical BP algorithm based on the measurement of simulated and real data.

Accurate rotor position is essential for a permanent magnet synchronous motor (PMSM) to execute vector control. Due to the big volume, the resolver and the encoder as standard position sensors cannot be used in the space-limited application. The sensor-less methods whose characteristic are sensitive to the parameter of the motor cannot be used in mass-produce. Therefore, this paper presents tunnel magnetoresistance (TMR) sensors as a sensing element embedded into the PMSM to detect the magnetic field applied by the built-in magnets and high-frequency time pulses served as a measurement standard to measure the position. TMR sensors excited by the altering voltage are integrated with the PMSM without taking up extra space. Besides, the rotation position is measured by counting the time pulses corresponding to the phase shift of the TMR sensor’s output signal, which is a simple and effective method to detect the position. Related design aspects include the corresponding signal circuit, and the sensing element is introduced in detail in this paper. Experimental results prove the validity of the proposed method, and multi-group TMR sensors are used to get higher detection accuracy. Although developed for the PMSM, the presented embedded position detecting approach can be used for where the space is limited or the other types of actuators to make it integration.

Machine learning has become an emerging method for next-generation smart technologies. It also promotes a development-paradigm shift in sensing technologies, which are essential in various smart applications. In this paper, we develop a data-driven method for a monolithic ion-sensitive field-effect transistor (ISFET) to have both photon and pH bi-detection capabilities simultaneously. The proposed methods are executed based on sequential-bias-reconfiguration of the ISFET. Utilizing support vector machine and back-propagation neural network, the photocurrent and ion-induced current can be calculated and decoupled. To evaluate the proposed method, semi-quantification by classification methods and quantification by regression methods are both examined. This paper has experimentally demonstrated the dual-detection capability of a pH range from 5 to 9 and an intensity range from 0 to $760~\mu \text{W}$ /cm2 with prediction error less than 1.5%. To fulfill low-computation requirement for applications, we further optimize the proposed algorithm by feature reduction to balance performances between accuracy, complexity of data acquisition, and data processing. With the developed machine-learning sensing device, therefore, we successfully demonstrate potentials of data-driven sensing devices in future applications.

Precise heart rate (HR) estimation of individuals during physical exercise from PPG sensors using direct methods often produces erroneous output due to the occurrence of exceedingly high motion artifacts (MAs). The effect of MAs can be neutralized using non-direct algorithms such as optimization techniques using some a priori about MA. In this paper, we propose a dual-optimization technique using adaptive Fourier decomposition in conjunction with sparse spectral reconstruction (AFODSS) using a multiple measurement vectors (MMVs) model and a regularized bi-conjugate gradient focal underdetermined system solver (BCG-FOCUSS) algorithm. Using AFODSS, the average absolute error (AAE) of HR obtained on the dataset recorded from 23 subjects during their intense exercise is 1.35 beats/min. The BCG-FOCUSS algorithm used in this paper helps to speed up the convergence rate and reduce time complexity when compared with other FOCUSS algorithms.

Respiratory rate (RR) monitoring of an adult or an infant during sleep or in steady position can be lifesaving, especially in home care systems. In this paper, we examine the potentials of wireless radio frequency (RF) signals to monitor the RR. A new noncontact RR monitoring system is proposed. The system includes a simple motion detection algorithm based on adaptive thresholding to eliminate the effects of the large-scale body movements on the RR estimation. In the proposed system, the high resolution subspace-based parametric spectral estimation approaches, estimation of signal parameters by rotational invariance technique (ESPRIT) and multiple signal classification (MUSIC), are presented as the RR estimation algorithms. According to our knowledge, the ESPRIT algorithm, which estimates the spectrum without searching, is used for the first time in this paper for RR estimation. It is shown that ESPRIT is computationally efficient and works approximately 49 times faster than the MUSIC algorithm. It is also shown with various experiments conducted with ten volunteers that the proposed noncontact RR monitoring system attains 0.13 breath per minute (bpm) error rate with the limited number of data and outperforms the periodogram method commonly used as the benchmark.

The asymmetric transmission has fascinated wide interest because of its novel applications. In this paper, we propose and present the design procedure and the simulation results of the tunable dual-band polarization-sensitive graphene chiral metasurface. The resonance wavelength depends on and is controlled by the graphene chemical potential. The two-dimensional chiral response of the structure, circular conversion dichroism (CCD), is tunable and is reached to 0.2. As an application of the tunable chiral structure, a novel tunable polarization-sensitive refractive index sensor with the maximum sensitivities of 7000–10000 nm/RIU is proposed and analyzed. For different kinds of the circularly polarized incident electromagnetic waves, the sensitivities are high and noticeable. This paper exposes a new way to propose other polarization-sensitive structures, such as switches and absorbers in the terahertz (THz) region. In addition, the proposed structure and its application could be useful in polarization-sensitive devices and systems.

Lane-level localization is a fundamental task for autonomous driving. As front cameras are easily disturbed by dynamic objects in urban environments, this paper presents an accurate lane-level localization approach using the around view monitoring (AVM) system. The paper proposes to detect the road features (i.e., road boundaries and road markings) based on pixel-wise semantic segmentation of raw fisheye images. The method can detect various types of road features and exclude dynamic objects from the localization. To address the problem of AVM-based localization with road features of different characteristics, this paper proposes coarse-scale localization (CSL) and fine-scale localization (FSL) methods for high-accurate localization. The CSL method leverages the road boundaries to provide an initial position; the FSL method estimates a high-accuracy position by matching nearby road markings with the map. The experiments in urban environments demonstrate that the proposed approach achieves centimeter-level localization accuracy with five centimeters in the lateral direction and seventeen centimeters in the longitudinal direction.

The wireless vehicle detectors (WVDs) based on the magnetic sensor for outdoor parking spaces have been studied in many papers. However, the magnetic signal has a blind zone between the front and rear wheels of vehicles, thus it is difficult for the WVDs to judge whether the magnetic signal is affected by the vehicle on the current or adjacent parking space. This paper proposes a robust vehicle detection method, which combines the data feature of global positioning system (GPS) satellites’ signal-to-noise ratios (SNRs) and magnetic signals to achieve vehicle detection. Since the high energy consumption of GPS receivers, the reference background of GPS-based method is calculated in the wireless access point (WAP), and the WVDs spend no energy on the background calculation in the proposed method. Moreover, the GPS receiver is activated to assist in the vehicle detection, when the data feature of magnetic signals is insufficient for the WVDs to make an accurate decision on the vehicle status, which improves the vehicle detection accuracy and reduces the average energy consumption of WVDs. Experiments show that the vehicle detection accuracy of our proposed method is up to 99.83%, when the sampling rate of magnetic sensor is 1 Hz, and it has a strong practical significance for the battery-powered WVDs.

Location information is generally considered to be indispensable information in wireless sensor networks, but existing algorithms have notable limitations in their positioning accuracy in 3-dimensional (3D) spaces. To improve the positioning accuracy of nodes in a 3D scenario, this paper proposes a new method called N2-3DDV-Hop (non-dominated sorting genetic algorithm II with 3D distance-vector hop) that builds on the 3D-DV-Hop algorithm by adding multi-objective model and NSGA-II. In this paper, it is analyzed that the limitations of the traditional single-objective positioning model and showed the relationship among the average hop distance, the number of sensor nodes, and the theoretical average distance. In addition then, the multi-objective positioning model incorporating the NSGA-II algorithm is presented. To evaluate its performance relative to other current methods, we compared our method by testing all of the methods with three different complex network topologies. Simulation results demonstrate that the N2-3DDV-Hop offered the best overall positioning performance compared with other algorithms and better robustness than the 3DDV-Hop algorithm.

Over the past decade, the Internet of Things (IoT) has attracted enormous interest from the research community and industry. IoT requires a synergy of various technologies, and wireless sensor networks (WSNs) are poised to play a critical role in many IoT applications like weather monitoring, smart-grids, smart-city and so on. The synchronization of local clocks of the WSN nodes is essential in many network functionalities, and thus a time synchronization protocol is required in WSNs. Although several synchronization protocols have been proposed for WSNs, most of them are simulation-based works. They make many assumptions at a high-abstraction level and do not consider the conditions of the line-of-sight (LOS) in the network. These factors significantly affect the performance of these protocols. Thus, conclusive experimental proof of the effectiveness of these protocols for different LOS conditions is required. In this direction, this paper proposes a time synchronization protocol called efficient and simple algorithm for time synchronization (E-SATS) for a cluster-based WSN. In this paper, E-SATS has been tested on a large-sized WSN testbed in different LOS scenarios and compared with the existing state-of-the-art protocols. E-SATS outperformed existing protocols by achieving up to six times better accuracy as compared to existing protocols with significantly lesser computations and energy consumption.

Vehicle classification based on magnetic sensors can be effectively applied to intelligent transportation and realize intelligent management of traffic. The use of representative vehicle signal features is a prerequisite and guarantee for accurate vehicle classification. This paper uses a single 3-axis magnetic sensor to acquire vehicle signals and extract a large number of features from the vehicle signals. In order to obtain a set of features that are simple and without loss of classification accuracy, we propose a Filtering algorithm based on Feature Pairing Elimination (FPE-Filter). In addition, choosing the right classifier is also crucial for models with high classification accuracy. In this paper, we compare four common classification models: SVM, RF, KNN, and C4.5. The experimental results show that the SVM performs best and the classification accuracy reaches 95%.

This paper investigates a novel method for gait gender recognition using a visual camera sensor. Numerous recent studies have focused on visual gait analysis involving a single gait energy image (GEI), yet these methods lose temporal movement information, which results in inferior accuracy under complex conditions. Therefore, in this paper, we introduce a new subGEI map by calculating from fewer frames than a gait cycle, extract the synthetic optical flow of multi subGEIs as temporal features, and utilize a two-stream CNN to combine use the GEI and the optical flow information for further gait analysis. In addition, this paper adopts three kinds of CNN networks, namely, VGG-16, Inception-V3, and a newly designed CNN and develops three types of subGEIs for comparison experiments. The experimental results on gait dataset CASIA show that: 1) the two-stream network produces better performance than that of the CNN-based methods; 2) the combination of temporal and spatial features significantly improves visual gait performance; and 3) the visual based approaches have great advantages than traditional sensors-based methods.

The spectrometer is applied in this paper to get the in-process information of welding defects during the high-power disk laser welding process. The high-dimensional signal captured by the spectrometer is fed into a data-driven framework based on stacked auto-encoder (SAE) to automatically extract salient features for performing real-time welding defects detection. The particle swarm optimization (PSO) algorithm is employed to optimize the proposed data-driven framework by acquiring the global optimal parameters to strengthen its capability in extracting the representative features from the original high-dimensional signal. The extracted features are classified by a softmax classifier to get the real-time identifications of the welding defects. The proposed framework is compared with the conventional shallow artificial intelligent methods, such as back-propagation (BP) neural network and support vector machine (SVM), and reveals better performance. The actual welding experiments under different welding parameters are implemented to validate the detection accuracy of our proposed data-driven framework. This paper provides an effective framework for the detection of the high-power disk laser welding status in real-time.

Non-contact sensors are negating the use of wearables or cameras and providing a rewarding and accepting environment to assist in biomedical applications, such as physiological examinations, physiotherapy, home assistance, rehabilitation success determination, compliance, and health diagnostics. In this paper, physiological parameter identification of human gait has been demonstrated through an edge-based sensor and a heuristic approach. Impulse radio ultra-wide band (IR-UWB) pulsed Doppler radar has been employed with a focus on understanding human walking patterns. This paper extracts an individual’s gait trait from the associated biomechanical activity and differentiates lower limb movement patterns from other body areas via a radar transceiver. It is observed that Doppler shifts alone are not reliable to detect human gait because of frequency shifts taking place across the entire body (including breathing, heartbeat, and arm movements) where movement occurs. Thus, a heuristic spherical trigonometrical approach has been proposed to augment radar principles and short-term Fourier transformation (STFT) to identify the gait trait. The experiment presented includes data gathering from a number of male and female participants in both ideal and real environments. Subsequently, the proposed gait identification and parameter characterization has been analyzed, tested, and validated against popularly accepted smartphone applications where the variations are less than 5%.

Optimization of piezoelectric energy harvester (PEH) to convert the ambient vibrational energy into maximum electrical energy has been of continued interest. The integration of the proof mass with the optimum cantilever width significantly enhances the output power of PEH. In this paper, we propose an optimized design of PEH that outperforms the existing AlN-based design in terms of power density. We analytically optimize the design of: 1) cantilever-to-harvester length and 2) cantilever-to-harvester width (proof mass width) ratios for the maximum output power. The optimized harvester is fabricated using a novel integration scheme for the bottom electrode with Au as an interlayer. The Au interlayer is used to grow a good quality of AlN film with piezoelectric coefficients, $d_{33} = 12$ pm/V and $d_{31} = -2.37 $ pm/V. It is found that in order to achieve the optimum output from PEH, the fractional length and width occupied by cantilever are 28%–40% and 38%–45%, respectively. The optimized designs are fabricated using a CMOS compatible process. The maximum power density measured from the fabricated PEHs is found to be 9.36 $\mu $ W/mm3, which is better than the similar reported data. The optimized and compact low-power PEHs reported in this paper have high potential to be integrated with the system on chip (SOC) and other wireless sensor applications.

This paper proposes a new aviation unusual-weather detection system constructed to augment existing aviation unusual-weather alert systems. Due to a lack of ground meteorological stations with sufficient ground altitude near airports, existing aviation unusual-weather alert systems can only detect unusual-weather conditions near the ground surface. Thus, this paper uses the automatic dependent surveillance—broadcast (ADS-B) signal transmitted by commercial aircraft to acquire vertical weather information for low-level weather conditions. Specifically, we propose an aviation unusual-weather detection model to establish the system using both the aircraft irregular-movement detection algorithm and the machine learning method. The performance of the proposed unusual-weather detection model is validated with actual ADS-B signals from several flights collected at the airport. The experiment results show the accuracy rates of aviation normal/unusual-weather classification above 96% and false positive rates below 1% for decent flight phases.

In recent years, wearable devices have attracted attention because of their ability to enhance the quality of life. This disruptive technology has helped healthcare professionals with intervening early in chronic diseases, especially amongst independently living patients, and has facilitated real-time monitoring of patients’ vital signs remotely. One of the major bottlenecks that hamper the adoption of wearable device is the continuous power supply. Most wearable devices solely depend on battery supply. When the energy stored in the battery is depleted, the operation of wearable devices is affected. To overcome this limitation, efficient energy harvesters for wearable devices are crucial. The paper primarily aims to present a comprehensive classification of different energy sources that can be capitalized to power wearable devices. In addition, this research paper deals with the key challenges that must be considered in the development of autonomous wearable devices for telemedicine applications with a proposed system design for wearable device that uses energy harvesting technology.

Additive manufacturing (AM) offers new manufacturing solutions for the integration of smart functionalities in engineering structures. In this paper, an analytical model is presented for an embedded load sensing element based on a liquid-filled capillary. During the additive manufacturing process, the capillary is integrated in the region where the strain is to be determined. The embedded capillary deforms as the structure deforms under an applied load, as such altering the pressure inside the capillary. The monitoring of the capillary pressure allows monitoring the loads and thus usage of the component. This paper presents a model describing the behavior of the sensing element under uniform tensile stress. The sensitivity of the load sensing element per unit longitudinal strain depends on the bulk modulus of the liquid inside the capillary and the Poisson coefficient of the surrounding material. The current work further compares the analytical model against static tension-compression tests of powder bed fused stainless steel (AISI 316L) test specimen with an integrated capillary filled with a liquid (water). Similarly, the validation of the model is then checked against a dynamic four-point bending test on a Ti-6Al-4V specimen produced by powder bed fusion.

Permanent magnet localization (PML) is a nascent method of wireless motion tracking that can estimate the 5D state (3D position and 2D orientation) of a cylindrical magnet from its magnetic field. PML is well suited for applications where a wireless tracking is crucial, and in particular for tongue motion which opens up many new interesting applications. To allow its usage outside of a research lab, our tongue tracking system relies on the PML to avoid impeding tongue’s natural motion due to its wireless tracking method and ensure safe use inside the mouth. Our tracking module is embedded in a headset to be portable and simple to use while being affordable by relying on the mass-produced components. The classical implementation of PML has many shortcomings that limit its practicality for real-world applications because it is computationally intensive due to its iterative algorithm, subject to local minimum convergence issues, and sensitive to its initial state and calibration parameters. Additionally, its physical model is an approximation that is only valid in limited tracking conditions. In this paper, we investigated the potential of deep learning to create a PML model for tongue tracking by training a feedforward neural network (3 layers, 100 neurons per layer) on a dataset composed of ∼1.7 million states spanning a volume of $10\times 10\times 10$ cm3. Our PML was validated on 337 000 states in a $4\times 6\times 7$ cm3 volume and tested on 100 000 samples emulating a more natural tongue motion with curves and twists. A fully automated 5D positional stage was engineered by our team to collect these large datasets of the 5D magnet states. Our PML prediction, limited to the magnet’s 3D positions in this paper, reaches positional errors of 1.4 mm (median) and 1.8 mm (Q3).

Radar technology plays a vital role in contact-less detection of hand gestures or motions, which forms an alternate and intuitive form of human–computer interface. Air-writing refers to the writing of linguistic characters or words in free space by hand gesture movements. In this paper, we propose an air-writing system based on a network of millimeter wave radars. We propose a two-stage approach for extraction and recognition of handwriting gestures. The extraction processing stage uses a fine range estimate combined with the trilateration technique to detect and localize the hand marker, followed by a smoothening filter to create a trajectory of the character through the hand movement. For the recognition stage, we explore two approaches: one extracts the time-series trajectory data and recognizes the drawn character using long short term memory (LSTM), bi-directional LSTM (BLSTM), and convolutional LSTM (ConvLSTM) with connectionist temporal classification (CTC) loss function, and the other approach reconstructs a 2D image from the trajectory of drawn character and uses deep convolutional neural network (DCNN) to classify the alphabets drawn by the user. ConvLSTM-CTC performs best among LSTM variants on time-series trajectory data achieving 98.33% classification accuracy similar to DCNN over the chosen character set. This paper employs real data using a network of three 60-GHz millimeter wave radar sensor to demonstrate the success of the proposed setup and associated algorithm with design consideration.

The electrode polarization-based flow meter is a simple and cost-effective flow meter, which utilizes the effect of flow of fluid on the polarization impedance that exists between the two electrodes for the measurement of flow rate of conductive fluid. It consists of two pairs of electrodes which are placed diametrically opposite to each other. Among these four electrodes, two diagonally placed electrodes are driven electrodes and other two diagonally placed electrodes serve as an output pickup. The output of polarization impedance-based flow metergets influence of excitation frequency, current density, ions constituents, conductivity of electrolyte solution, and temperature and area of electrodes. In this paper, we have analyzed and investigated the influence of electrodes area on the characteristics of electrode polarization-based flow meter. The experiment has been performed after the theoretical investigation and its results are reported in this paper. The performance of the flow meter is investigated by finite element method (FEM) usingComsol 5.4 Multiphysics simulation software. The simulation result shows that the variation in output of flow sensor with respect to the variation of diameter. The experiment results match quite well with simulations. It has been also observed that the result derived from the experiment exactly follows the theoretically derived equations.

As a promising component for body sensor networks, the wearable sensors for hand gesture recognition have increasingly received great attention in recent years. By interpreting human intentions through hand gestures, the natural human–robot interaction can be realized in the smart home where the youth and the elderly can perform hand gestures to control the household robot or the robotic wheelchair. Here, a wearable wrist-worn camera sensor was shown to recognize hand trajectory gestures. The moving velocity of the user’s hand was deduced from the matched speeded up robust features keypoints of the moving background of the video sequence. Furthermore, the segmentation of continuous gestures was achieved by detecting the predefined gesture starting signal from the hand region of the image, which was segmented by the lazy snapping algorithm. In this paper, 10 types of gestures and 1350 gesture samples collected from 15 subjects at three different scenes were classified by the dynamic time warping algorithm and the results achieved an average recognition accuracy up to 97.6%. Moreover, the practicability of the proposed system was further demonstrated by controlling a cooperative robot to draw letters on paper.

In this paper, we elaborate the design, implementation, and testing of a system— FallSense , which is able to detect accidental falls of human beings in a precise way. The distinguishing feature of FallSense is its endeavors beyond the scope of accelerometer, which is a component of traditional body sensor network. Along with this acceleration measuring unit, FallSense uses the benefits of an Internet-of-Things-enabled environment, which consists of a number of infrared transmitter–receiver pairs and ultrasonic sensors. Employing a fuzzy inference system, FallSense fuses the data from multiple sensors and becomes over sure before inferring that a fall has occurred. Depending on the inputs from multiple sensors, FallSense generates a value between 0 and 1, which signifies the chance of fall. Results show that multi-sensor-based FallSense achieves overall 16% improvement in comparison with the existing approaches, on an average. Beyond the theoretical modeling, this paper also practically implemented the same with the help of the real-sensing units.

For human gesture recognition applications, 3D-sensor-based approaches have received considerable attention and are crucial for future applications of an advanced body sensor network (BSN). A practical gesture recognition system is to apply active gesture patterns of a gesture-making user for body action classifications. 3D-sensor-based gesture recognition, categorized as biometric recognition in BSN applications, is greatly lack of the extensible cognition ability due to substandard recognition accuracy on the gesture-making user identity. To overcome this problem, this paper proposes an active gesture-based user identity recognition approach using a robust feature design, called user adaptation (UA) features, derived from a UA process. Two different UA features, namely Eigen Centroid-UA and Eigen Transform-UA features, were developed in this paper to accurately represent the adaptive learning tendency of gesture recognition for a specific gesture-making user. Compared with traditional 3D sensor gesture-based identity recognition approaches that employ only the feature of fixed body skeleton information without any UA designs, the presented UA-feature can exhibit fine adaptation learning continuously to the specific action user; therefore, superior identity recognition accuracy will be constantly ensured. To demonstrate the efficiency and effectiveness of the developed robust UA features in this paper, experiments on gesture-making user identification by Gaussian mixture model applying the proposed Eigen Centroid-UA feature and verification by support vector machine applying the proposed Eigen Transform-UA feature were conducted.

The human-centered body sensor network (BSN) becomes one of the Internet of Things (IoT) development directions in the future due to interdisciplinary advantages in the fields of wireless communication technology, embedded microelectronic technology, and mobile Internet technology. Only depending on the terminal perception and transmission technology is not enough to make up the weak data storage, computing, and analysis capacity. Thus, in combination with green sensors, smart clouds computing, artificial intelligence technology, and BSN, this paper proposes a concept of green cognitive BSN (Green-CBSN). Starting from the three aspects including green active sensor, energy harvesting and efficient data collection, and health big data recognition and interaction, this paper introduces the architecture and thought of Green-CBSN in detail. Then, we invite some volunteers equipped with wearable devices, such as smart clothing and smartphone, and carry out the physiological signal collection, heart rate monitoring, and physiological data emotion analysis experiments of electrocardiograph and photoplethysmography. At last, this paper summarizes the open issues and research directions about IoT security, health big data recognition algorithm optimization, and energy saving and green energy harvesting, to provide the reference for Green-CBSN.