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Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges.
Medical equipment & techniques --- inertial measurement unit --- movement analysis --- long-track speed skating --- validity --- IMU --- principal component analysis --- wearable --- scoring --- carving --- balance assessment --- data augmentation --- gated recurrent unit --- human activity recognition --- one-dimensional convolutional neural network --- intermittent claudication --- vascular rehabilitation --- 6 min walking test --- functional walking --- TUG --- kinematics --- fall risk --- logistic regression --- elderly --- inertial sensor --- artificial intelligence --- supervised machine learning --- head rotation test --- neck pain --- cerebral palsy --- dystonia --- choreoathetosis --- machine learning --- home-based --- wearable device --- MLP --- gesture recognition --- flex sensor --- model search --- neural network --- inertial measurement unit—IMU --- movement complexity --- sample entropy --- trunk flexion --- low back pain --- lifting technique --- camera system --- ward clustering method --- K-means clustering method --- ensemble clustering method --- Bayesian neural network --- pain self-efficacy questionnaire --- n/a --- inertial measurement unit-IMU
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The advent of Internet of Things offers a scalable and seamless connection of physical objects, including human beings and devices. This, along with artificial intelligence, has moved transportation towards becoming intelligent transportation. This book is a collection of eleven articles that have served as examples of the success of internet of things and artificial intelligence deployment in transportation research. Topics include collision avoidance for surface ships, indoor localization, vehicle authentication, traffic signal control, path-planning of unmanned ships, driver drowsiness and stress detection, vehicle density estimation, maritime vessel flow forecast, and vehicle license plate recognition. High-performance computing services have become more affordable in recent years, which triggered the adoption of deep-learning-based approaches to increase the performance standards of artificial intelligence models. Nevertheless, it has been pointed out by various researchers that traditional shallow-learning-based approaches usually have an advantage in applications with small datasets. The book can provide information to government officials, researchers, and practitioners. In each article, the authors have summarized the limitations of existing works and offered valuable information on future research directions.
History of engineering & technology --- decision-making --- autonomous navigation --- collision avoidance --- scene division --- deep reinforcement learning --- maritime autonomous surface ships --- internet of things --- crowdsourcing --- indoor localization --- data fusion --- security --- authentication --- Inertial Measurement Units --- road transportation --- traffic signal control --- speed guidance --- vehicle arrival time --- connected vehicle --- unmanned ships --- DDPG --- autonomous path planning --- end-to-end --- at-risk driving --- deep support vector machine --- driver drowsiness --- driver stress --- multi-objective genetic algorithm --- multiple kernel learning --- urban freeway --- hybrid dynamic system --- state transition --- unknown inputs observer --- vehicle density --- maritime vessel flows --- intelligent transportation systems --- deep learning --- automatic license plate recognition --- intelligent vehicle access --- histogram of oriented gradients --- artificial neural networks --- convolutional neural networks --- time-frequency --- Inertial Measurement Unit (IMU) --- road anomalies --- decision-making --- autonomous navigation --- collision avoidance --- scene division --- deep reinforcement learning --- maritime autonomous surface ships --- internet of things --- crowdsourcing --- indoor localization --- data fusion --- security --- authentication --- Inertial Measurement Units --- road transportation --- traffic signal control --- speed guidance --- vehicle arrival time --- connected vehicle --- unmanned ships --- DDPG --- autonomous path planning --- end-to-end --- at-risk driving --- deep support vector machine --- driver drowsiness --- driver stress --- multi-objective genetic algorithm --- multiple kernel learning --- urban freeway --- hybrid dynamic system --- state transition --- unknown inputs observer --- vehicle density --- maritime vessel flows --- intelligent transportation systems --- deep learning --- automatic license plate recognition --- intelligent vehicle access --- histogram of oriented gradients --- artificial neural networks --- convolutional neural networks --- time-frequency --- Inertial Measurement Unit (IMU) --- road anomalies
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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 --- 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
Choose an application
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 advent of Internet of Things offers a scalable and seamless connection of physical objects, including human beings and devices. This, along with artificial intelligence, has moved transportation towards becoming intelligent transportation. This book is a collection of eleven articles that have served as examples of the success of internet of things and artificial intelligence deployment in transportation research. Topics include collision avoidance for surface ships, indoor localization, vehicle authentication, traffic signal control, path-planning of unmanned ships, driver drowsiness and stress detection, vehicle density estimation, maritime vessel flow forecast, and vehicle license plate recognition. High-performance computing services have become more affordable in recent years, which triggered the adoption of deep-learning-based approaches to increase the performance standards of artificial intelligence models. Nevertheless, it has been pointed out by various researchers that traditional shallow-learning-based approaches usually have an advantage in applications with small datasets. The book can provide information to government officials, researchers, and practitioners. In each article, the authors have summarized the limitations of existing works and offered valuable information on future research directions.
History of engineering & technology --- decision-making --- autonomous navigation --- collision avoidance --- scene division --- deep reinforcement learning --- maritime autonomous surface ships --- internet of things --- crowdsourcing --- indoor localization --- data fusion --- security --- authentication --- Inertial Measurement Units --- road transportation --- traffic signal control --- speed guidance --- vehicle arrival time --- connected vehicle --- unmanned ships --- DDPG --- autonomous path planning --- end-to-end --- at-risk driving --- deep support vector machine --- driver drowsiness --- driver stress --- multi-objective genetic algorithm --- multiple kernel learning --- urban freeway --- hybrid dynamic system --- state transition --- unknown inputs observer --- vehicle density --- maritime vessel flows --- intelligent transportation systems --- deep learning --- automatic license plate recognition --- intelligent vehicle access --- histogram of oriented gradients --- artificial neural networks --- convolutional neural networks --- time-frequency --- Inertial Measurement Unit (IMU) --- road anomalies --- n/a
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Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes.
Research & information: general --- Biology, life sciences --- Biochemistry --- gait --- smoothness --- older adults --- accelerometer --- inertial measurement unit (IMU) --- upper extremity --- stroke --- biomechanical phenomena --- kinematics --- inertial measurement systems --- motion analysis --- wearable devices --- e-textile --- gait analysis --- m-health --- plantar pressure --- validation --- Internet of Things --- body sensor network --- inertial sensors --- ground reaction force --- spatio-temporal parameters --- wearable sensors --- decision trees --- foot drop stimulation --- symmetry --- inertial measurement sensor --- wearable inertial sensors --- marker-based optoelectronic system --- ACL --- rehabilitation --- motion capture validation --- upper limb --- Parkinson’s disease --- Box and Block test --- inertial sensors network --- biomechanics analysis --- kinematic data --- hand trajectories --- kinematic --- inertial measurement units --- angle-angle diagrams --- cyclograms --- obesity --- bradykinesia --- real-life --- naturalistic monitoring --- motor fluctuation --- wearable movement sensor --- IMU --- motion capture --- reliability --- clinical --- orthopedic --- sensory–motor gait disorders --- limb prosthesis --- spatial–temporal analysis --- symmetry index --- walking --- 6-min walking test --- wearable system --- inertial sensor --- RGB-D sensors --- optoelectronic system --- movement analysis --- hemiparesis --- n/a --- Parkinson's disease --- sensory-motor gait disorders --- spatial-temporal analysis
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Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes.
gait --- smoothness --- older adults --- accelerometer --- inertial measurement unit (IMU) --- upper extremity --- stroke --- biomechanical phenomena --- kinematics --- inertial measurement systems --- motion analysis --- wearable devices --- e-textile --- gait analysis --- m-health --- plantar pressure --- validation --- Internet of Things --- body sensor network --- inertial sensors --- ground reaction force --- spatio-temporal parameters --- wearable sensors --- decision trees --- foot drop stimulation --- symmetry --- inertial measurement sensor --- wearable inertial sensors --- marker-based optoelectronic system --- ACL --- rehabilitation --- motion capture validation --- upper limb --- Parkinson’s disease --- Box and Block test --- inertial sensors network --- biomechanics analysis --- kinematic data --- hand trajectories --- kinematic --- inertial measurement units --- angle-angle diagrams --- cyclograms --- obesity --- bradykinesia --- real-life --- naturalistic monitoring --- motor fluctuation --- wearable movement sensor --- IMU --- motion capture --- reliability --- clinical --- orthopedic --- sensory–motor gait disorders --- limb prosthesis --- spatial–temporal analysis --- symmetry index --- walking --- 6-min walking test --- wearable system --- inertial sensor --- RGB-D sensors --- optoelectronic system --- movement analysis --- hemiparesis --- n/a --- Parkinson's disease --- sensory-motor gait disorders --- spatial-temporal analysis
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In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included.
smart homes --- Internet of Things (IoT) --- Wi-Fi --- human monitoring --- behavioral analysis --- ambient assisted living --- intelligent luminaires --- wireless sensor network --- indoor localisation --- indoor monitoring --- Graphics Processing Units (GPUs) --- CUDA --- OpenMP --- OpenCL --- K-means --- brain cancer detection --- hyperspectral imaging --- unsupervised clustering --- impaired sensor --- Structural Health Monitoring --- Time of Flight --- subharmonics --- Cascaded-Integrator-Comb (CIC) filter --- FPGA --- fixed point math --- data adaptive demodulator --- motion estimation --- inertial sensors --- simulation --- spline function --- Kalman filter --- eHealth --- software engineering --- gesture recognition --- Dynamic Time Warping --- Hidden Markov Model --- usability --- Cramér–Rao lower bound (CRLB) --- human motion --- Inertial Measurement Unit (IMU) --- Time of Arrival (TOA) --- wearable sensors --- endothelial dysfunction --- photoplethysmography --- machine learning --- computer-assisted screening --- sleep pose recognition --- keypoints feature matching --- Bayesian inference --- near-infrared images --- scale invariant feature transform --- heartbeat classification --- arrhythmia --- denoising autoencoder --- autoencoder --- deep learning --- auditory perception --- biometrics --- computer vision --- web control access --- web security --- human–computer interaction --- n/a --- Cramér-Rao lower bound (CRLB) --- human-computer interaction
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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.
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
Choose an application
The advent of Internet of Things offers a scalable and seamless connection of physical objects, including human beings and devices. This, along with artificial intelligence, has moved transportation towards becoming intelligent transportation. This book is a collection of eleven articles that have served as examples of the success of internet of things and artificial intelligence deployment in transportation research. Topics include collision avoidance for surface ships, indoor localization, vehicle authentication, traffic signal control, path-planning of unmanned ships, driver drowsiness and stress detection, vehicle density estimation, maritime vessel flow forecast, and vehicle license plate recognition. High-performance computing services have become more affordable in recent years, which triggered the adoption of deep-learning-based approaches to increase the performance standards of artificial intelligence models. Nevertheless, it has been pointed out by various researchers that traditional shallow-learning-based approaches usually have an advantage in applications with small datasets. The book can provide information to government officials, researchers, and practitioners. In each article, the authors have summarized the limitations of existing works and offered valuable information on future research directions.
decision-making --- autonomous navigation --- collision avoidance --- scene division --- deep reinforcement learning --- maritime autonomous surface ships --- internet of things --- crowdsourcing --- indoor localization --- data fusion --- security --- authentication --- Inertial Measurement Units --- road transportation --- traffic signal control --- speed guidance --- vehicle arrival time --- connected vehicle --- unmanned ships --- DDPG --- autonomous path planning --- end-to-end --- at-risk driving --- deep support vector machine --- driver drowsiness --- driver stress --- multi-objective genetic algorithm --- multiple kernel learning --- urban freeway --- hybrid dynamic system --- state transition --- unknown inputs observer --- vehicle density --- maritime vessel flows --- intelligent transportation systems --- deep learning --- automatic license plate recognition --- intelligent vehicle access --- histogram of oriented gradients --- artificial neural networks --- convolutional neural networks --- time-frequency --- Inertial Measurement Unit (IMU) --- road anomalies --- n/a
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