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During the last century, navigation systems have become ubiquitous and guide drivers, cyclists, and pedestrians towards their desired destinations. While operating worldwide, they rely on line-of-sight conditions towards satellites and are thus limited to outdoor areas. However, finding a gate within an airport, a ward within a hospital, or a university's auditorium also represent navigation problems. To provide navigation within such indoor environments, new approaches are required. This thesis examines pedestrian 3D indoor localization and navigation using commodity smartphones: A desirable target platform, always at hand and equipped with a multitude of sensors. The IMU (accelerometer, gyroscope, magnetometer) and barometer allow for pedestrian dead reckoning, that is, estimating relative location changes. Absolute whereabouts can be determined via Wi-Fi, an infrastructure present within most public buildings, or by using Bluetooth Low Energy Beacons as inexpensive supplement. The building's 3D floorplan not only enables navigation, but also increases accuracy by preventing impossible movements, and serves as a visual reference for the pedestrian. All aforementioned information is fused by recursive density estimation based on a particle filter. The conducted experiments cover both, theoretical backgrounds and real-world use-cases. All discussed approaches utilize the infrastructure found within most public buildings, are easy to set up, and maintain. Overall, this thesis results in an indoor localization and navigation system that can be easily deployed, without requiring any special hardware components.

Navigation. --- Navigation, Primitive --- Locomotion --- Orientation --- Nautical astronomy --- Naval art and science --- Pilots and pilotage --- Probabilistic Sensor Fusion --- Pedestrian Dead Reckoning --- Wi-Fi Location Estimation --- Indoor Floorplans --- 3D Movement Prediction

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Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods.

Technology: general issues --- filter --- finite memory structure --- infinite memory structure --- smoother --- target tracking --- Indoor Positioning System --- WLAN --- C-Means --- K-Means --- Access Point Selection --- RSS-fingerprint --- smartphone --- pedestrian dead reckoning --- heading estimation --- autoregressive model --- adaptive Kalman filter --- indoor localization --- Wi-Fi received signal strength indicator (RSSI) --- semisupervised learning --- feature extraction --- mobile fingerprinting --- trajectory learning --- localization --- hybrid localization --- Bluetooth Low Energy --- extended kalman filter --- internet of things --- proximity sensors --- smartphone sensors --- pedestrian dead reckoning (PDR) --- Wi-Fi indoor positioning --- sensor fusion frameworks --- Kalman filter --- location fingerprinting --- trilateration --- received signal strength indication (RSSI) --- indoor positioning --- 5G system --- hybrid positioning --- geometric dilution of precision --- closed-form solution --- Cramer-Rao lower bound --- visually impaired (VI) --- computer vision --- deep learning --- multi-label convolutional support vector machine (M-CSVM) --- assistive technology --- visually impaired --- navigational system --- indoor navigation --- markers --- mobile robots --- wireless sensor network --- time of arrival (TOA) --- NLOS --- modified probabilistic data association (MPDA) --- indoor location recognition --- received signal strength (RSS) --- Wi-Fi fingerprint positioning --- deep neural network (DNN) --- optimization methods --- adaptive filter --- hidden Markov models (HMM) --- I/O detection --- GPS signal --- machine learning --- positioning applications. --- PDR --- geomagnetic positioning --- particle filter --- genetic algorithm --- Wi-Fi fine timing measurement --- NLOS identification --- Gaussian model --- carrier phase --- differential pseudolite system --- extended Kalman filter --- reliability --- integrity monitoring --- transparent obstacle recognition --- reflection noise --- laser range finder --- path planning --- mobile robot --- automated data acquisition --- remote sensing technologies --- automated progress reporting --- data fusion --- tracking resources --- bearing estimation --- azimuth estimation --- signal processing --- position estimation --- photodiode array --- indoor ranging algorithm --- channel state information --- received signal strength indicator --- VPR --- fusion navigation --- UWB --- multi-path detection --- NLOS and MP discrimination --- SVM --- random forest --- multilayer perceptron --- LOS --- DWM1000 --- fingerprinting --- smart buildings --- mobile devices --- indoor localization technologies --- model based techniques --- quality control

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The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders.

History of engineering & technology --- inertial measurement units --- gait analysis --- biomedical signal processing --- pattern recognition --- step detection --- physiological signals --- Parkinson’s disease --- pathological gait --- turning analysis --- wearable sensors --- mobile gait analysis --- wearables --- inertial sensors --- traumatic brain injury --- dynamic balance --- gait disorders --- gait patterns --- head injury --- gait symmetry --- gait smoothness --- acceleration --- machine learning --- classification --- accelerometer --- GAITRite --- multi-regression normalization --- SVM --- random forest classifier --- balance --- gait --- transcranial direct current stimulation --- wearable electronics --- IMUs --- cueing --- posture --- rehabilitation --- cerebellar ataxia --- movement analysis --- personalized medicine --- stroke --- asymmetry --- trunk --- reliability --- validity --- aging --- reactive postural responses --- yaw perturbation --- kinematics --- postural stability --- dynamic posturography --- multiple sclerosis --- gait metrics --- test-retest reliability --- sampling frequency --- accelerometry --- autocorrelation --- harmonic ratio --- six-minute walk --- back school --- inertial sensor --- lower back pain --- stability --- timed up and go test --- gait assessment --- tri-axial accelerometer --- CV --- healthy subjects --- test-retest --- trajectory reconstruction --- stride segmentation --- dynamic time warping --- pedestrian dead-reckoning --- near falls --- loss of balance --- pre-impact fall detection --- activities of daily life --- bio-signals --- EEG --- EMG --- wireless sensors --- posturography --- Alzheimer’s disease --- vestibular syndrome --- diagnosis --- symptoms monitoring --- wearable --- home-monitoring --- inertial measurement units --- gait analysis --- biomedical signal processing --- pattern recognition --- step detection --- physiological signals --- Parkinson’s disease --- pathological gait --- turning analysis --- wearable sensors --- mobile gait analysis --- wearables --- inertial sensors --- traumatic brain injury --- dynamic balance --- gait disorders --- gait patterns --- head injury --- gait symmetry --- gait smoothness --- acceleration --- machine learning --- classification --- accelerometer --- GAITRite --- multi-regression normalization --- SVM --- random forest classifier --- balance --- gait --- transcranial direct current stimulation --- wearable electronics --- IMUs --- cueing --- posture --- rehabilitation --- cerebellar ataxia --- movement analysis --- personalized medicine --- stroke --- asymmetry --- trunk --- reliability --- validity --- aging --- reactive postural responses --- yaw perturbation --- kinematics --- postural stability --- dynamic posturography --- multiple sclerosis --- gait metrics --- test-retest reliability --- sampling frequency --- accelerometry --- autocorrelation --- harmonic ratio --- six-minute walk --- back school --- inertial sensor --- lower back pain --- stability --- timed up and go test --- gait assessment --- tri-axial accelerometer --- CV --- healthy subjects --- test-retest --- trajectory reconstruction --- stride segmentation --- dynamic time warping --- pedestrian dead-reckoning --- near falls --- loss of balance --- pre-impact fall detection --- activities of daily life --- bio-signals --- EEG --- EMG --- wireless sensors --- posturography --- Alzheimer’s disease --- vestibular syndrome --- diagnosis --- symptoms monitoring --- wearable --- home-monitoring

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The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders.

inertial measurement units --- gait analysis --- biomedical signal processing --- pattern recognition --- step detection --- physiological signals --- Parkinson’s disease --- pathological gait --- turning analysis --- wearable sensors --- mobile gait analysis --- wearables --- inertial sensors --- traumatic brain injury --- dynamic balance --- gait disorders --- gait patterns --- head injury --- gait symmetry --- gait smoothness --- acceleration --- machine learning --- classification --- accelerometer --- GAITRite --- multi-regression normalization --- SVM --- random forest classifier --- balance --- gait --- transcranial direct current stimulation --- wearable electronics --- IMUs --- cueing --- posture --- rehabilitation --- cerebellar ataxia --- movement analysis --- personalized medicine --- stroke --- asymmetry --- trunk --- reliability --- validity --- aging --- reactive postural responses --- yaw perturbation --- kinematics --- postural stability --- dynamic posturography --- multiple sclerosis --- gait metrics --- test-retest reliability --- sampling frequency --- accelerometry --- autocorrelation --- harmonic ratio --- six-minute walk --- back school --- inertial sensor --- lower back pain --- stability --- timed up and go test --- gait assessment --- tri-axial accelerometer --- CV --- healthy subjects --- test-retest --- trajectory reconstruction --- stride segmentation --- dynamic time warping --- pedestrian dead-reckoning --- near falls --- loss of balance --- pre-impact fall detection --- activities of daily life --- bio-signals --- EEG --- EMG --- wireless sensors --- posturography --- Alzheimer’s disease --- vestibular syndrome --- diagnosis --- symptoms monitoring --- wearable --- home-monitoring