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Due to its non-invasiveness and mobility, photo volumetric tracing (PPG)-based heart rate measurement has drawn a lot of attention. Heart rate is a crucial physiological signal in health monitoring. However, motion artifacts can seriously interfere with PPG signals in a sports setting, which significantly reduces the accuracy of heart rate estimates. This research proposes a hybrid artifact removal (HMR), signal reconstruction (SR), and deep learning (DL)-based heart rate estimate approach (DL$+$HMR$+$SR) to tackle this issue. To effectively analyze complex artifacts in dynamic settings, the technique combines the fine-grained optimization mechanism of signal reconstruction, the artifact separation approach of hybrid artifact removal, and the feature extraction capabilities of deep learning. Six datasets — running, hand waving, elliptical training, deep squatting, mixed motion, and beckoning — are used in this work to validate the method’s performance. It is then compared to several traditional approaches, including RandF, Temko, TROIKA, JOSS, EEMD, and CorNet. In terms of average error, trend matching, and artifact elimination, the experimental results demonstrate that the method in this paper performs better than the current methods. The average error of $2.4\pm 1.3$ BPM is significantly lower than that of the classical methods (the average error of TROIKA is $11.72\pm 15.98$ BPM). The approach presented in this study also shows excellent robustness and application in downstream tasks like emotion recognition, and its average F1 score outperforms that of the other methods under comparison. The findings demonstrate that the $\mathrm{\text{DL}}+\mathrm{\text{HMR}}+\mathrm{\text{SR}}$ technique effectively supports health monitoring in wearable devices due to its excellent generalization ability, high accuracy, and robustness in handling dynamic artifacts. This study establishes the groundwork for future advancements in motion artifact elimination approaches and offers fresh ideas for heart rate estimation solutions.

This study presents part of an experimental and analytical survey of candidate methods for damage detection of composite structural. Embedded piezoceramic (PZT) sensors were excited with the high power ultrasonic wave generator generating a propagation of stress wave along the composite plate. The same embedded piezoceramic (PZT) sensors are used as receivers for acquiring stress signals. The effects of center frequency of embedded sensor were evaluated for the damage identification capability with known localized defects. The study was carried out to assess damage in composite plate by fusing information from multiple sensing paths of the embedded network. It was based on the Hilbert transform, signal correlation and probabilistic searching. The obtained results show that satisfactory detection of defects could be achieved by proposed method.

We propose in this paper a novel architecture for human activity monitoring, following conceptual, technical and experimental claims. From a conceptual viewpoint, we propose to approach the interpretation of sensor data as embedded into a multidimensional frame involving functional and non-functional requirements. Functional requirements involve considering the monitored person's specificities as well as the task to be performed. Non-functional requirements qualify the system activity. This frame of interpretation is continuously refined, to cope with evolving situations or expectations from the Observer. From a technical viewpoint, we propose to develop a multi-Agent architecture as a means for dependable, flexible monitoring. This paradigm allows to handle multiple, heterogeneous entities in a unified way. The Agents process incoming data with a dynamic population of hypotheses on several abstraction levels. This reasoning is abductive and fuzzy in nature. From the experimental viewpoint, we propose a dedicated evaluation approach to estimate the interpretative process unfolding. Functional and non-functional properties are presented to discuss the system's effectiveness, informativeness, sensitivity, efficiency and robustness, some of which are supported by qualitative, analytical discussions, others by quantitative measures.

Port structures such as pile supported piers and quay walls generally face severe conditions, and deterioration of port structures is often observed. For an example, at a sheet pile quay wall, sheet pile is a structural component which is easily deteriorated by corrosion. As a result of deterioration of sheet pile, the backfilled soil would be washed out due to the tidal action. Suffusion, the phenomena of the washed out of fine particles and loosening of soil layer, is a problem to be considered in the maintenance of a quay wall. Therefore, a health monitoring method for the backfill of a sheet pile quay wall is necessary. In this study, the applicability of a surface-wave method for health monitoring of a sheet pile quay wall was examined. First, the variation of shear wave velocity in the ground due to suffusion and variation in water content is measured from bender element tests. Then, 2D finite element method (FEM) dynamic analysis was conducted to simulate a surface-wave measurement on a sheet pile quay wall. From the result of the analysis, the estimated shear wave velocity profile based on surface-wave method agrees fairly well with the assumed soil profile. Finally, surface-waves were measured at a sheet pile quay wall, and the measured results were compared with the result of Swedish weight sounding test at the site. Furthermore, the difference between the shear wave velocity profiles at ebb tide and that at high tide was successfully measured by the surface-wave method. Thus, the applicability of the surface-wave method for a sheet pile quay wall is demonstrated in this study.

A novel method was proposed for the moving load identification of bridges based on the influence line theory and distributed optical fiber sensing technique. The method of load and vehicle speed identification was firstly theoretically studied, and then numerical simulation was also performed to study its accuracy and robustness. The numerical results showed that this method was characterized by high accuracy and excellent resistance to noise. Finally, the load identification of an actual continuous pre-stressed concrete beam bridge was carried out with the proposed method. The bridge consists of four pre-stressed box beams. At the same time, a weigh-in-motion system was also installed about 200 m in front of the bridge to measure the speed and moving loads with a purpose of comparing the load identification of the proposed method. Long gauge fiber Bragg grating (FBG) sensors with a gauge length of 1.0 m were adhered to the bottom of the beams. The individual loaded vehicles and the corresponding structure response were mainly monitored as standard samples, and the speed and weight of the sample vehicles were monitored and identified with the proposed method. The results revealed that the distributed long gauge FBG sensors were capable of sensing the structure response precisely and identifying the traffic load. On the basis of the design information and ambient vibration testing results, a refined model was established and the response under unit moving load was acquired for load identification. It was also shown that the sensors in different positions can achieve accurate vehicle speed and weight, the relative error of which are within 10% and 15%, respectively.

For high-speed railway bridges in operation, it is necessary to reveal the coupling dynamic performance of train–bridge systems in order to avoid extreme vibrations, which are not conducive to bridge safety. With the opening of long-span heavy-haul and complex-type bridges to traffic, the train–bridge interaction can hardly be explained by a mature and unified theory. Notably, field testing and monitoring analysis have become popular in tracking the dynamic performance of train–bridge systems. The vibration of railway bridges depends on the train-track configuration and the inherent characteristics of bridges. The inherent characteristics of bridges, which are reflected by the modal parameters, are extracted via operational modal analysis in this paper. In addition, the modal characteristics of bridges while the train is passing through are also investigated to explain the coupling dynamic effect with the help of the train configuration. Considering that the measured vibration responses are seriously polluted by non-white noise or excitation, the variational mode decomposition (VMD) technique is developed to extract the state-driven vibrations for modal analysis. Since VMD is a univariate technique that hardly ensures that the weak component can be obtained from each measuring channel, the multi-channel variational mode decomposition (MVDM) technique is extended in this paper. The field monitoring data of a high-speed railway bridge are taken for modal identification and vibration analysis. The results show that the weak structural modes can be tracked, even though the forced vibrations due to the passage of regularly spaced axles are dominant. In addition, the dynamic effects in train-induced vertical vibrations of bridges are closely related to the train speed, heavy axle loads and the span length.

In this paper, a smart structure is developed by integrating a semi-active control strategy with an online synchronization-based damage detection method. In this algorithm, the structural damages are identified in real-time with the synchronization-based method using displacement and velocity measurements of the structure. Then, a fuzzy logic controller is applied for determination of the control forces according to the occurrence of damages. A five-story linear shear building equipped with magneto-rheological (MR) dampers is studied numerically to verify the performance and efficiency of the proposed integrated method for both damage detection and vibration suppression. One damage scenario and four earthquake records are used for such purpose. Results demonstrate that the proposed algorithm has the capability of identifying structural damages satisfactorily while exerting suitable control forces to compensate for the damages occurrence and mitigating the dynamic responses of the structure. Furthermore, it is shown that in comparison with the long-established method of only vibration control, the total energy consumption is significantly reduced, an issue of concern in optimal control of structures.

This paper presents a new way to determine road profile and detect bridge damage using accelerations from a fleet of passing vehicles. Using off-bridge data, a Bayesian approach updates estimates of the road profile and vehicle properties. The profile elevations and vehicle properties are shown to be insensitive to random noise in acceleration measurements. On-bridge data, with recently updated vehicle properties, are used to estimate bridge damage. Bearing damage and local crack damage in a bridge are simulated. For bearing damage, the results show that this method can quantify the damage level of a bearing and infer other bridge properties. For local crack damage, the levels and the location of the damage are inferred from the simulated measurements.

Infrared thermal imaging has in recent years become more accessible and affordable as a means of remote sensing for human body temperature such as in identifying a person with fever. The implementation and operational guidelines for identifying a febrile human using a screening thermograph as documented in the ISO/TR 13154:2009 ISO/TR 80600 has been deployed for the screening of a total of 402 children. It was found that there was a significant difference between the temperatures measured in non-fevered patients and those with known fever, with the thermal imaging of the eye region being the most rapid non-contact site for measurement.

Knee joints play an indispensable role in the activities of daily living. In particular, the knee joints of the elderly and the physically challenged require continuous care in order to ensure a healthy daily life. This study proposes a health monitoring system for knee joints, which is able to classify lower extremity movements using the angle and acceleration components of these joints. The proposed monitoring system consists of a wearable frame placed on the knee joint, consisting of a sensor part for monitoring the knee joint angle and acceleration and a wireless communication part for transferring bio signals to a smart device. Knee joint angles and accelerations are measured using potentiometers installed at the hinges of the upper and lower parts of the wearable frame and an inertial sensor (IMU) attached to the thigh. Data thus measured are transferred via Bluetooth to an application on a smart device. The proposed system incorporates a classification algorithm for lower extremity movements, which can distinguish users’ actions such as sitting, lying, and standing by using real-time measurements of knee joint angles and accelerations. This study shows that the proposed monitoring system detects postures that negatively affect knee joints and informs a user when these postures are adopted, thereby helping to maintain healthy knee joints.

A telemedicine system will provide sustainable, comprehensive, low-cost, fast, private, and convenient access to medical consultation and diagnosis for patients from remote locations. The telemedicine system addressed in this paper consists of a sensor jacket, which is worn by the patient for medical monitoring. The signals sensed through the jacket are processed and transmitted through a public telecommunication link, to a medical professional in a hospital at distance. The medical professional interacts with the patient through audio and video links, and simultaneously examines the data transmitted by the monitoring system. Medical assessment, diagnosis, and prescription are carried out on this basis. Sensing and signal processing are paramount to providing the patient data to the medical professional in an accurate and effective manner. This paper presents some relevant issues and techniques. Specific examples of electrocardiograms and respiratory signals are provided to illustrate the applicable signal conditioning approaches. Results are presented to demonstrate the feasibility and the effectiveness of these methods.

Civil structures could undergo hysteresis cycles due to cracking or yielding, when subjected to severe earthquake motions. Seismic instrumentation, structural monitoring and system identification techniques have been used in the past years to assess civil structures under lateral loads. The present research makes use of a continuous-time Least Squares Method, modified herein to consider the nonlinear behaviour of the structure. The proposed on-line algorithm simultaneously estimates the hysteretic component and structural parameters such as damping and stiffness of a shear building structure. Simulations are carried out using the El Centro and the Mexico City seismic records. Fair convergence speed is obtained for the identification of the parameters and the estimation of the hysteretic component.

Flexoelectricity in dielectrics suggests promising smart structures for sensors, actuators and transducers. In this review, dielectric materials, structures and the associated flexoelectric characterization methods are presented. First of all, we review structures and methods to measure different flexoelectric coefficients, including ${\mu }_{1122},{\mu }_{1111},{\mu }_{1211},{\mu }_{3121},{\mu }_{2312},{\mu }_{1123}$, etc., via direct or converse flexoelectric effect. The flexoelectric materials in the form of bulk, thin films and 2D materials and the reported flexoelectric properties of these dielectrics will then be discussed. Semiconductor materials and the associated flexoelectric studies will also be reviewed. The progress of flexoelectric device study will next be presented, followed by the flexoelectricity research challenges and future trend.

A load cell is the representative converter that changes load to the quantity of electricity. The load cell is used to a large mechanical structure and offshore structures to measure the force. Currently, the load cell using electrical strain gauges are commonly used. Basic measuring principle of electrical strain gauge is the electrical method. A load cell with electrical strain gauges is not available in the electromagnetic and corrosion environment. A Fiber Bragg Grating (FBG) sensor is not affected by the EMI (Electro Magnetic Interference)/EMC (Electro Magnetic Compatibility) and is strong in corrosion under the sea water. In this paper, we use the FBG sensors to make a load cell under the sea water condition and the electromagnetic environment and show FBG sensors' availability.

This paper envisions the possibility of a Conscious Aircraft: an aircraft of the future with features of consciousness. To serve this purpose, three main fields are examined: philosophy, cognitive neuroscience, and Artificial Intelligence (AI). While philosophy deals with the concept of what is consciousness, cognitive neuroscience studies the relationship of the brain with consciousness, contributing toward the biomimicry of consciousness in an aircraft. The field of AI leads into machine consciousness. The paper discusses several theories from these fields and derives outcomes suitable for the development of a Conscious Aircraft, some of which include the capability of developing “world-models”, learning about self and others, and the prerequisites of autonomy, selfhood, and emotions. Taking these cues, the paper focuses on the latest developments and the standards guiding the field of autonomous systems, and suggests that the future of autonomous systems depends on its transition toward consciousness. Finally, inspired by the theories suggesting the levels of consciousness, guided by the Theory of Mind, and building upon state-of-the-art aircraft with autonomous systems, this paper suggests the development of a Conscious Aircraft in three stages: Conscious Aircraft with (1) System-awareness, (2) Self-awareness, and (3) Fleet-awareness, from the perspectives of health management, maintenance, and sustainment.

As an attempt towards damage detection in composites, in this chapter, an experimental investigation is presented aiming at extending the current understanding of how delamination will affect the vibration characteristics of carbon fiber-reinforced plastics (CFRP). Different percentages of delamination have been applied artificially on CFRPs, and experimental modal analysis has been performed for both natural frequency and modal loss factor parameters. It is a well-established fact that, due to the global nature of lower modes’ natural frequencies, small defects have small effects on these modal parameters and hence, generally speaking, natural frequency is not a good feature for damage indication. On the other hand, modal loss factor can be considered as a good damage index (DI), especially, if the healthy structure is of low damping characteristic. However, the problem with modal loss factor is that it is hard to identify reliable values for this parameter and usually identified values have high scatter. In order to be able to identify reliable modal loss factors, the modal parameter extraction techniques with high order of accuracy in damping identification, i.e. circle fit (CF) and line fit (LF) methods, are used, leading to the rationalization process being optimized, resulting in the extended line fit method, (ELFM). Using ELFM, it has been shown that both natural frequency and modal loss factor have changed due to delamination. While, as expected, natural frequency has experienced insignificant changes, modal loss factor has proved to be a highly sensitive indicator, undergoing major changes even at initial damage stages. Modal damping mechanisms and their relationship with mode shapes have been examined. The results reveal that delamination severity can be detected using modal loss factor variations.

Many studies of monitoring and detection of damages to structures have been conducted through piezoelectric sensors, and a number of damage detecting methods have been developed and renovated. This study aims to develop a monitoring system for a crane, focusing on that a crane moves in the direction of the work task. This means the actuating points of the crane are not fixed. Because actuating points are moving all over the girder supporting the crane, the points of damage to the structures can possibly be detected wherever they exist. The damage detection method is experimented, using a simply supported beam with a bolted splice at the center of beam. An impact load and a moving load test are performed for the crane girder. Both of loosening of the blots on the web and the flange connection plate of the crane girder are assumed as damaged incidents. The detection of potential damages is carried out in accordance with output measurement data of FFT (Fast Fourier Transformation) processing in the frequency domain. A coherence-based NDT method is used to identify the damage. The frequency of the first mode decreases as the damage increases. It is found that the ratio of the magnitude of the third mode to the first mode could be used to detect the loosening of the bolt.

The port crane is of key importance to port loading and unloading operations. It directly affects the working efficiency of the port as well as the personnel's safety. Therefore, the structural health of large hoisting machinery and the method of safety evaluation is of particular importance. This paper mainly introduces the strategy of monitoring the structure's surface, major structure strength, stiffness and fatigue damage, and presents the corresponding evaluation. Relevant experts are then consulted to determine the index weight value, which is combined with fuzzy comprehensive evaluation for rating. This provides a significant reference source for safety supervision and inspection of large hoisting machinery.