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The gravitational wave detectors must be isolated from the Earth’s constant vibrations to be able to sense low-frequency gravitational waves. The combined performances of passive and active isolation stages allow getting close to the target requirements in terms of seismic vibration isolation. A new active platform is currently designed. Its embedded inertial sensors measure the ground motion, which is then actively canceled by actuators. The inertial sensors require a large dynamic measurement range, which can be achieved by lowering the sensor resonance frequency while increasing the resonance frequency of internal modes away from the operational range.
The leaf-spring suspension composing the gravity compensator system of a vertical inertial sensor, the Compact Interferometric Inertial Sensor (μVINS), is numerically and experimentally characterized. The end-purpose of the study is to extend the dynamic measurement range of that particular inertial sensor by evaluating the impact on its resonance frequencies of various parameters related to the leaf-spring suspension. Following the numerous studies, numerical and experimental, performed in the context of this work, design guidelines are devised, enabling the suspension to be tuned into a low resonance frequency quasi-zero stiffness mechanism. The μVINS design can be modified to be competitive with the already existing sensors, while being more compact. Optimizing the leaf-spring suspension length and clamping location enabled the resonance frequency to be decreased by one order of magnitude. The measurement bandwidth is thus also increased by one order of magnitude. With the capacity to measure lower frequency displacements, μVINS can feed a wider range of data to the active stage and ensure an effective low-frequency isolation.

Inertial sensor --- low-frequency measurements --- broad-band seismometry --- leaf-spring suspension --- quasi-zero stiffness --- Ingénierie, informatique & technologie > Ingénierie aérospatiale

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Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization.

Technology: general issues --- inertial sensor --- gait --- validity --- functional calibration --- accuracy --- wearable electronic devices --- kinematics --- head rotation --- ecological research --- gait analysis --- characterization --- smart insole --- vertical ground reaction forces --- force sensitive resistors --- piezoelectric sensors --- sensor calibration --- heart rate --- photoplethysmography --- PPG --- time-domain --- wearable device --- concurrent validity --- outcome assessment --- feasibility --- rehabilitation --- data fusion --- MARG --- IMU --- eye tracker --- self-contained --- head motion measurement --- motor variability --- actigraphy --- triaxial accelerometers --- jumping --- human-computer interaction --- affective technologies --- interaction design --- biosensing --- actuation --- somaesthetics --- design toolkits --- serious videogames --- motion capture --- upper limbs --- physical rehabilitation --- telerehabilitation --- inertial sensors --- inertial measurement unit (IMU) --- state of the art --- inertial sensor measurement systems --- motion accuracy --- Baduanjin --- physical education --- physiotherapy --- e-health --- motion sensing --- wireless charging --- wireless connectivity --- low power --- trunk --- upper extremity --- compensation --- reaching --- Kinect --- video motion capture --- reliability --- n/a

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The intense development of novel data-driven and hybrid methods for structural health monitoring (SHM) has been demonstrated by field deployments on large-scale systems, including transport, wind energy, and building infrastructure. The actionability of SHM as an essential resource for life-cycle and resilience management is heavily dependent on the advent of low-cost and easily deployable sensors Nonetheless, in optimizing these deployments, a number of open issues remain with respect to the sensing side. These are associated with the type, configuration, and eventual processing of the information acquired from these sensors to deliver continuous behavioral signatures of the monitored structures. This book discusses the latest advances in the field of sensor networks for SHM. The focus lies both in active research on the theoretical foundations of optimally deploying and operating sensor networks and in those technological developments that might designate the next generation of sensing solutions targeted for SHM. The included contributions span the complete SHM information chain, from sensor design to configuration, data interpretation, and triggering of reactive action. The featured papers published in this Special Issue offer an overview of the state of the art and further proceed to introduce novel methods and tools. Particular attention is given to the treatment of uncertainty, which inherently describes the sensed information and the behavior of monitored systems.

Technology: general issues --- probabilistic data-interpretation --- Bayesian model updating --- error-domain model falsification --- iterative asset-management --- practical applicability --- computation time --- swarm-based parallel control (SPC) --- Internet of Things (IoT) --- soil–structure interaction (SSI) --- semi-active control --- adjacent buildings --- Bayesian inference --- model updating --- modal identification --- structural dynamics --- bridges --- sensor placement optimisation --- structural health monitoring --- damage identification --- mutual information --- evolutionary optimisation --- inertial sensor fusion --- instrumented particle --- MEMS --- sediment entrainment --- sensor calibration --- frequency of entrainment --- varying environmental and operational conditions --- damage detection and localization --- Gaussian process regression --- autoregressive with exogenous inputs --- distributed sensor network --- mode shape curvatures --- n/a --- soil-structure interaction (SSI)

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The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy.

Technology: general issues --- History of engineering & technology --- fog computing --- cloud computing --- e-health --- healthcare --- Internet of Things --- paddle stroke analysis --- motion reconstruction --- inertial sensor --- data fusion --- body sensor network --- gait analysis --- gyroscope --- information fusion --- hidden Markov model --- human activity recognition --- out of distribution --- anomaly detection --- open set classification --- physiotherapy --- inertial sensors --- smart watch --- rehabilitation --- machine learning --- COPD --- wearable sensors --- SenseWear Armband --- physical activity --- weekday-to-weekend --- energy expenditure --- stress --- wearable device --- heart rate variability --- electrocardiogram --- scapula neuromuscular activity and control --- rotator cuff related pain syndrome --- anterior shoulder instability --- scapular dyskinesis --- electromyographic biofeedback --- cardio-respiratory monitoring --- wearable system --- smart textile --- IMU --- respiratory rate --- heart rate --- accelerometers --- Bland-Altman plots --- gait speed --- interclass correlation coefficient --- low frequency extension filter --- Stepwatch --- smart walker --- obstacle detection --- aging --- fog computing --- cloud computing --- e-health --- healthcare --- Internet of Things --- paddle stroke analysis --- motion reconstruction --- inertial sensor --- data fusion --- body sensor network --- gait analysis --- gyroscope --- information fusion --- hidden Markov model --- human activity recognition --- out of distribution --- anomaly detection --- open set classification --- physiotherapy --- inertial sensors --- smart watch --- rehabilitation --- machine learning --- COPD --- wearable sensors --- SenseWear Armband --- physical activity --- weekday-to-weekend --- energy expenditure --- stress --- wearable device --- heart rate variability --- electrocardiogram --- scapula neuromuscular activity and control --- rotator cuff related pain syndrome --- anterior shoulder instability --- scapular dyskinesis --- electromyographic biofeedback --- cardio-respiratory monitoring --- wearable system --- smart textile --- IMU --- respiratory rate --- heart rate --- accelerometers --- Bland-Altman plots --- gait speed --- interclass correlation coefficient --- low frequency extension filter --- Stepwatch --- smart walker --- obstacle detection --- aging

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The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application.

Technology: general issues --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group --- falls --- slips --- trips --- postural perturbations --- wearables --- stretch-sensors --- ankle kinematics --- rowing --- technology --- inertial sensor --- accelerometer --- performance --- signal processing --- sEMG --- knee --- random forest --- principal component analysis --- back propagation --- estimation model --- knee angle --- deep learning --- neural networks --- gait-phase classification --- electrogoniometer --- EMG sensors --- walking --- gait-event detection --- automotive radar --- machine learning --- walking analysis --- seated posture --- cognitive engagement --- stress level --- load cells --- embedded systems --- sensorized seat --- flexion-relaxation phenomenon --- surface electromyography --- wearable device --- WBSN --- automatic detection of the FRP --- Internet of Things (IoT) --- human activity recognition (HAR) --- motion analysis --- wearable sensors --- cerebral palsy --- hemiplegia --- motor disorders --- gait variability --- coefficient of variation --- surface EMG --- statistical gait analysis --- activation patterns --- co-activation --- Parkinson’s disease --- activity recognition --- rate invariance --- Lie group