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Author:   Jakub Wagner ,  Paweł Mazurek ,  Roman Z. Morawski
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2022
Weight:   0.492kg
ISBN:  

9783030960117

ISBN 10:   3030960110
Pages:   303
Publication Date:   17 April 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1.        Introduction to healthcare-oriented monitoring of persons1.1.        Objectives of healthcare-oriented monitoring 1.2.        Systems for healthcare-oriented monitoring 1.2.1.         Monitoring techniques 1.2.2.         Commercially available monitoring systems 1.3.        Semantic and mathematical modelling of monitoring systems 1.3.1.         Semantic modelling of monitoring systems 1.3.2.         Mathematical modelling of monitoring systems 1.4.        Overview of measurement-data processing in healthcare-oriented monitoring systems 1.4.1.         Localisation of persons by means of impulse-radar sensors 1.4.2.         Localisation of persons by means of depth sensors 1.4.3.         Denoising and differentiation of persons’ movement trajectories 1.4.4.         Fusion and postprocessing of data from impulse-radar sensors and depth sensors 2.        Localisation of persons by means of impulse-radar sensors – basic methods 2.1.        Methods for extraction of signal from impulse-radar data 2.1.1.         Mathematical model of impulse-radar data 2.1.2.         Method based on arithmetic averaging 2.1.3.         Method based on exponential averaging 2.1.4.         Method based on singular-value decomposition 2.2.        Methods for estimation of impulse-radar signal parameters 2.2.1.         Mathematical model of impulse-radar data 2.2.2.         Method based on time-domain template matching 2.2.3.         Method based on frequency-domain template matching 2.2.4.         Method based on maximum-envelope matching 2.3.        Methods for estimation of two-dimensional trajectories 2.3.1.         Methods for smoothing of distance trajectories 2.3.2.         Methods for conversion of two distance trajectories into movement trajectory 2.4.        Chapter conclusions 3.        Localisation of persons by means of impulse-radar sensors – advanced methods 3.1.        Principles of Bayesian inference 3.1.1.         Bayesian methods for measurand reconstruction 3.1.2.         Key role of a priori information 3.1.3.         Recent applications of Bayesian inference in data processing 3.2.        Extraction of signal from impulse-radar data 3.2.1.         Method #1 3.2.2.         Method #2 3.2.3.         Method #3 3.3.        Estimation of impulse-radar signal parameters 3.3.1.         Dealing with varying number of echoes 3.3.2.         Dealing with fixed number of echoes 3.4.        Estimation of two-dimensional trajectories 3.4.1.         Smoothing of distance trajectories 3.4.2.         Conversion of two distance trajectories into movement trajectory 4.        Localisation of persons by means of impulse-radar sensors – comparison of methods 4.1.        Extraction of signal from impulse-radar data 4.1.1.         Methodology of numerical experimentation based on semi-synthetic data 4.1.2.         Numerical experiments based on semi-synthetic data 4.1.3.         Numerical experiments based on real-world data 4.1.4.         Section conclusions 4.2.        Estimation of impulse-radar signal parameters 4.2.1.         Methodology of numerical experimentation based on semi-synthetic data 4.2.2.         Numerical experiments based on semi-synthetic data 4.2.3.         Methodology of numerical experimentation based on real-world data 4.2.4.         Numerical experiments based on real-world data 4.2.5.         Section conclusions 4.3.        Estimation of two-dimensional trajectories 4.3.1.         Methodology of numerical experimentation based on real-world data 4.3.2.         Numerical experiments based on real-world data 4.3.3.         Section conclusions 5.        Denoising and differentiation of persons’ movement trajectories – basic methods 5.1.        Mathematical formulation of denoising and differentiation problems 5.1.1.         Mathematical model of measurement data 5.1.2.         Outline of studied methods for denoising and differentiation 5.1.3.         Classification of studied methods for denoising and differentiation 5.2.        Scalar methods for denoising and differentiation 5.2.1.         Method based on Savitzky-Golay filtering 5.2.2.         Method based on Kalman filtering 5.2.3.         Finite-difference methods for differentiation 5.3.        Vector polynomial methods for denoising and differentiation 5.3.1.         Method based on Tikhonov regularisation 5.3.2.         Method based on regularisation by constraining total variation 5.3.3.         Method based on singular value decomposition 5.3.4.         Method based on Landweber’s algorithm 5.3.5.         Method based on CGLS algorithm 5.4.        Vector non-polynomial methods for denoising and differentiation 5.4.1.         Method based on regularisation by constraining number of basis functions 5.4.2.         Method based on Tikhonov regularisation 5.5.        Catalogue of studied methods for regularised numerical differentiation 6.        Denoising and differentiation of persons’ movement trajectories – advanced methods 6.1.        Implementation of parameter-optimisation strategies 6.1.1.         Strategy based on discrepancy principle 6.1.2.         Strategy based on generalised cross-validation 6.1.3.         Strategy based on L-curve 6.1.4.         Strategy based on normalised cumulative periodogram 6.1.5.         Strategy based on Stein’s unbiased risk estimator 6.2.        Comparative study of parameter-optimisation strategies 6.2.1.         Methodology of experimentation 6.2.2.         Optimisation of degree of approximating polynomial 6.2.3.         Optimisation of parameters of Kalman filter 6.2.4.         Optimisation of differentiation step 6.2.5.         Optimisation of Tikhonov regularisation parameters 6.2.6.         Optimisation of constraint on total variation 6.2.7.         Optimisation of number of retained components of SVD 6.2.8.         Optimisation of number of iterations 6.2.9.         Optimisation of number of basis functions 6.2.10.     Summary of experimentation results 7.        Fusion of data from impulse-radar sensors and depth sensors 7.1.        Basic methods for fusion of data 7.1.1.         Method based on ordinary least-squares estimator 7.1.2.         Method based on total least-squares estimator 7.1.3.         Method based on Kalman filter 7.2.        Advanced methods for fusion of data 7.2.1.         Method based on maximum-a-posteriori estimator 7.2.2.         Method based on maximum-likelihood estimator 7.2.3.         Method based on particle filter 7.3.        Methodology of numerical experimentation based on real-world data 7.3.1.         Acquisition of measurement data 7.3.2.         Criteria for performance evaluation 7.4.        Numerical experimentation based on real-world data 7.4.1.         Straight-line trajectories and square-shaped trajectories 7.4.2.         Serpentine trajectories 7.5.        Chapter conclusions 8.        Gait analysis 8.1.        Objectives and methods of gait analysis 8.1.1.         Methods for characterisation of human gait 8.1.2.         Applicability of spatiotemporal gait analysis in healthcare practice 8.1.3.         Overview of techniques for estimation of self-selected walking speed 8.1.4.         Overview of techniques for estimation of other spatiotemporal gait parameters 8.1.5.         Overview of techniques for gait analysis based on depth sensors 8.2.        Comparison of methods for estimation of walking speed based on impulse-radar sensors and depth sensors 8.2.1.         Methodology of experimentation 8.2.2.         Results of experimentation 9.        Fall detection 9.1.        Techniques for fall detection 9.1.1.         Overview of techniques for fall detection 9.1.2.         Techniques for fall detection based on depth sensors 9.1.3.         Performance of techniques for fall detection 9.2.        Comparison of methods for fall detection based on depth sensors 9.2.1.         Methodology of experimentation 9.2.2.         Results of experimentation 10.     Ethical issues related to monitoring of persons 10.1.    General scheme of making morally significant decisions 10.2.    Bioethical principles and values 10.3.    Technoscientific recommendations 10.3.1.     Measurements 10.3.2.     Data processing 11.     Conclusion 11.1.    Final remarks 11.2.    Further research References Index

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Author Information

​Jakub Wagner, born in 1988 (Poland), received the M.Sc. degree in biomedical engineering in 2013, and the Ph.D. degree in electronics in 2020, both from Warsaw University of Technology (Poland) where he is currently employed as Assistant Professor. His scientific activities are focused on biomedical and healthcare applications of mathematical tools for analysis of measurement data. He has participated in a series of research projects devoted to healthcare-oriented monitoring of independently-living elderly persons. Paweł Mazurek, born in 1989 (Poland), received the M.Sc. degree in telecommunications, in 2014, and Ph.D. degree in electronics in 2019, from Warsaw University of Technology (Poland) where he is currently employed as Assistant Professor. His research interests include the preprocessing and integration of measurement data acquired by means of impulse-radar sensors and infrared depth sensors when applied for unobtrusive monitoring of elderly persons. Roman Z. Morawski, born in 1949 (Poland), Professor of Measurement Science at Warsaw University of Technology, has fifty-year research experience in the field of measurement data modelling and processing, including the development and implementation of algorithms for processing data from various kinds of spectrometric, optoelectronic, acoustic and microwave sensors. He has also considerable experience related to industrial research and development, comprising: the development of algorithms for seismic signal processing applied in petroleum industry (Shell Company, 1987); the algorithms for optical performance monitoring of telecommunication channels (Bookham Technology, 2001); the algorithms of spectrometric data processing for analytical spectrophotometry (Measurement Microsystems, 1998–2008). Since 2014 he has been involved in the projects concerning healthcare-oriented monitoring of elderly persons.

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