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The main objective of this Thesis is to provide a more efficient as precise alternative to classic dynamic analysis. Conventionally, it is performed by means of Fourier Analysis, in which time series of the loading are analysed, then brought into the frequency domain (by Fourier Transform), applied to the specific structure (characterised by its Transfer Function, which depend on structural parameters only) to get response in the frequency domain, then Inverse Fourier Transform is applied to recover response in time domain.
As it can be clearly understood, this process becomes very heavy when dealing with (real) structures having many degrees of freedom - because this double transformation has to be done for each degree of freedom to be able to reconstruct the entire structural response (supposing to perform analysis in the Modal Base, which is almost always the case since with nowadays F.E.M. software, recovering the Modal Matrix is no more time consuming as it could have been some years ago).
Therefore, specially for the pre-design stages of a civil engineering project, an alternative method, faster as well as reliable, able to compute or characterise structural response is needed. In this framework, this Thesis takes its place. 
It will be shown and proved, as in other previous works, an alternative dynamic analysis method based on the Background and Resonant responses, under the assumption of stationary Gaussian loading. It is basically based on the decomposition of the response in its two major components, which are by their own computed based on 
main statistical quantities of the loading (mean value and Power Spectral Density Function or, equally, variance). This way, the previous Double time-frequency transformation is avoided: once loading is known (i.e. measured or simulated), response can be reconstructed by statistical analysis. 
However, this decomposition is no more valid when the loading has non-Gaussian distribution. Therefore, the aim of this Thesis is to finally validate an extension of the previous approach to more general cases in which loading is non-Gaussian. Still, the response will be decomposed in its Background and Bi-resonant components in the frequency space, where they will be connected to higher order statistical quantities of the loading.

Wind Engineering --- Dynamics of Structures --- Bispectrum --- Response Spectra --- Stochastic Analysis --- Non-Gaussian --- Random Processes --- Fourier Analysis --- Ingénierie, informatique & technologie > Ingénierie civile

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This book provides a collection of comprehensive research articles on data analytics and applications of wearable devices in healthcare. This Special Issue presents 28 research studies from 137 authors representing 37 institutions from 19 countries. To facilitate the understanding of the research articles, we have organized the book to show various aspects covered in this field, such as eHealth, technology-integrated research, prediction models, rehabilitation studies, prototype systems, community health studies, ergonomics design systems, technology acceptance model evaluation studies, telemonitoring systems, warning systems, application of sensors in sports studies, clinical systems, feasibility studies, geographical location based systems, tracking systems, observational studies, risk assessment studies, human activity recognition systems, impact measurement systems, and a systematic review. We would like to take this opportunity to invite high quality research articles for our next Special Issue entitled “Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases” as a part of Sensors journal.

Humanities --- Social interaction --- eHealth --- wearable --- monitoring --- services --- integration --- IoT --- Telemedicine --- wearable sensors --- multivariate analysis --- longitudinal study --- functional decline --- exercise intervention --- accidental falls --- fall detection --- real-world --- signal analysis --- performance measures --- non-wearable sensors --- accelerometers --- cameras --- machine learning --- smart textiles --- healthcare --- talking detection --- activity recognition and monitoring --- patient health and state monitoring --- wearable sensing --- orientation-invariant sensing --- motion sensors --- accelerometer --- gyroscope --- magnetometer --- pattern classification --- artificial intelligence --- supervised machine learning --- predictive analytics --- hemodialysis --- non-contact sensor --- heart rate --- respiration rate --- heart rate variability --- time-domain features --- frequency-domain features --- principal component analysis --- behaviour analysis --- classifier efficiency --- personal risk detection --- one-class classification --- actigraphy --- encoding --- data compression --- denoising --- edge computing --- signal processing --- wearables --- activity monitoring --- citizen science --- cluster analysis --- physical activity --- sedentary behavior --- walking --- energy expenditure --- wearable device --- impedance pneumography --- neural network --- mechanocardiogram (MCG) --- smart clothes --- heart failure (HF) --- left ventricular ejection fraction (LVEF) --- technology acceptance model (TAM) --- physical activity classification --- free-living --- GENEactiv accelerometer --- Gaussian mixture model --- hidden Markov model --- wavelets --- skill assessment --- deep learning --- LSTM --- state space model --- probabilistic inference --- latent features --- human activity recognition --- MIMU --- genetic algorithm --- feature selection --- classifier optimization --- bispectrum --- entropy --- feature extraction --- heat stroke --- filtering algorithm --- physiological parameters --- exercise experiment --- biomedical signal processing --- wearable biomedical sensors --- wireless sensor network --- respiratory monitoring --- optoelectronic plethysmography --- biofeedback --- biomedical technology --- exercise therapy --- orthopedics --- mobile health --- qualitative --- human factors --- inertial measurement unit --- disease prevention --- occupational healthcare --- P-Ergonomics --- precision ergonomics --- musculoskeletal disorders --- wellbeing at work --- electrocardiogram --- conductive gels --- noncontact electrode --- myocardial ischemia --- pacemaker --- ventricular premature contraction --- upper extremity --- motion --- action research arm test --- activities of daily living --- IoT wearable monitor --- health --- posture analysis --- spinal posture --- wearable sensor --- embedded system --- recurrent neural networks --- physical workload --- wearable systems for healthcare --- machine learning for real-time applications --- actigraph --- body worn sensors --- clothing sensors --- cross correlation analysis --- healthcare movement sensing --- wearable devices --- calibration --- inertial measurement units --- human movement --- physical activity type --- real-life --- GPS --- GIS --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book provides a collection of comprehensive research articles on data analytics and applications of wearable devices in healthcare. This Special Issue presents 28 research studies from 137 authors representing 37 institutions from 19 countries. To facilitate the understanding of the research articles, we have organized the book to show various aspects covered in this field, such as eHealth, technology-integrated research, prediction models, rehabilitation studies, prototype systems, community health studies, ergonomics design systems, technology acceptance model evaluation studies, telemonitoring systems, warning systems, application of sensors in sports studies, clinical systems, feasibility studies, geographical location based systems, tracking systems, observational studies, risk assessment studies, human activity recognition systems, impact measurement systems, and a systematic review. We would like to take this opportunity to invite high quality research articles for our next Special Issue entitled “Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases” as a part of Sensors journal.

eHealth --- wearable --- monitoring --- services --- integration --- IoT --- Telemedicine --- wearable sensors --- multivariate analysis --- longitudinal study --- functional decline --- exercise intervention --- accidental falls --- fall detection --- real-world --- signal analysis --- performance measures --- non-wearable sensors --- accelerometers --- cameras --- machine learning --- smart textiles --- healthcare --- talking detection --- activity recognition and monitoring --- patient health and state monitoring --- wearable sensing --- orientation-invariant sensing --- motion sensors --- accelerometer --- gyroscope --- magnetometer --- pattern classification --- artificial intelligence --- supervised machine learning --- predictive analytics --- hemodialysis --- non-contact sensor --- heart rate --- respiration rate --- heart rate variability --- time-domain features --- frequency-domain features --- principal component analysis --- behaviour analysis --- classifier efficiency --- personal risk detection --- one-class classification --- actigraphy --- encoding --- data compression --- denoising --- edge computing --- signal processing --- wearables --- activity monitoring --- citizen science --- cluster analysis --- physical activity --- sedentary behavior --- walking --- energy expenditure --- wearable device --- impedance pneumography --- neural network --- mechanocardiogram (MCG) --- smart clothes --- heart failure (HF) --- left ventricular ejection fraction (LVEF) --- technology acceptance model (TAM) --- physical activity classification --- free-living --- GENEactiv accelerometer --- Gaussian mixture model --- hidden Markov model --- wavelets --- skill assessment --- deep learning --- LSTM --- state space model --- probabilistic inference --- latent features --- human activity recognition --- MIMU --- genetic algorithm --- feature selection --- classifier optimization --- bispectrum --- entropy --- feature extraction --- heat stroke --- filtering algorithm --- physiological parameters --- exercise experiment --- biomedical signal processing --- wearable biomedical sensors --- wireless sensor network --- respiratory monitoring --- optoelectronic plethysmography --- biofeedback --- biomedical technology --- exercise therapy --- orthopedics --- mobile health --- qualitative --- human factors --- inertial measurement unit --- disease prevention --- occupational healthcare --- P-Ergonomics --- precision ergonomics --- musculoskeletal disorders --- wellbeing at work --- electrocardiogram --- conductive gels --- noncontact electrode --- myocardial ischemia --- pacemaker --- ventricular premature contraction --- upper extremity --- motion --- action research arm test --- activities of daily living --- IoT wearable monitor --- health --- posture analysis --- spinal posture --- wearable sensor --- embedded system --- recurrent neural networks --- physical workload --- wearable systems for healthcare --- machine learning for real-time applications --- actigraph --- body worn sensors --- clothing sensors --- cross correlation analysis --- healthcare movement sensing --- wearable devices --- calibration --- inertial measurement units --- human movement --- physical activity type --- real-life --- GPS --- GIS --- n/a