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Neuroimaging post-stroke has the potential to uncover underlying principles of disturbed hand function and recovery characterizing defined patient groups, including their long term course as well as individual variations. The methods comprise functional magnetic resonance imaging (MRI) measuring task related activation as well as resting state. Functional MRI may be complemented by arterial spin labeling (ASL) MRI to investigate slowly varying blood flow and associated changes in brain function. For structural MRI robust and accurate computational anatomical methods like voxel-based morphometry and surface based techniques are available. The investigation of the connectivity among brain regions and disruption after stroke is facilitated by diffusion tensor imaging (DTI). Intra- and interhemispheric coherence may be studied by electromagnetic techniques such as electroencephalography and transcranial magnetic stimulation. Consecutive phases of stroke recovery (acute, subacute, early chronic and late chronic stages) are each distinguished by intrinsic processes. The site and size of lesions entail partially different functional implications. New strategies to establish functional specificity of a lesion site include calculating contrast images between patients exhibiting a specific disorder and control subjects without the disorder. Large-size lesions often imply poor cerebral blood flow which impedes recovery significantly and possibly interferes with BOLD response of functional MRI. Thus, depending on the site and size of the infarct lesion the patterns of recovery will vary. These include recovery sensu stricto in the perilesional area, intrinsic compensatory mechanisms using alternative cortical and subcortical pathways, or behavioral compensatory strategies e.g. by using the non-affected limb. In this context, behavioral and neuroimaging measures should be developed and employed to delineate aspects of learning during recovery. Of special interest in recovery of hand paresis is the interplay between sensory and motor areas in the posterior parietal cortex involved during reaching and fine motor skills as well as the interaction with the contralesional hemisphere. The dominant disability should be characterized, from the level of elementary to hierarchically higher processes such as neglect, apraxia and motor planning. In summary, this Research Topic covers new trends in state of the art neuroimaging of stroke during recovery from upper limb paresis. Integration of behavioral and neuroimaging findings in probabilistic brain atlases will further advance knowledge about stroke recovery.

stroke recovery --- Motor Imagery --- structural covariance --- Somatosensory Disorders --- perilesional plasticity --- network reorganization --- multimodal neuroimaging --- Neurorehabilitation --- computational biophysical modeling --- motor control

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People with congenital and/or acquired disabilities constitute a great number of dependents today. Robotic platforms to help people with disabilities are being developed with the aim of providing both rehabilitation treatment and assistance to improve their quality of life. A high demand for robotic platforms that provide assistance during rehabilitation is expected because of the health status of the world due to the COVID-19 pandemic. The pandemic has resulted in countries facing major challenges to ensure the health and autonomy of their disabled population. Robotic platforms are necessary to ensure assistance and rehabilitation for disabled people in the current global situation. The capacity of robotic platforms in this area must be continuously improved to benefit the healthcare sector in terms of chronic disease prevention, assistance, and autonomy. For this reason, research about human–robot interaction in these robotic assistance environments must grow and advance because this topic demands sensitive and intelligent robotic platforms that are equipped with complex sensory systems, high handling functionalities, safe control strategies, and intelligent computer vision algorithms. This Special Issue has published eight papers covering recent advances in the field of robotic platforms to assist disabled people in daily or clinical environments. The papers address innovative solutions in this field, including affordable assistive robotics devices, new techniques in computer vision for intelligent and safe human–robot interaction, and advances in mobile manipulators for assistive tasks.

Humanities --- Social interaction --- sensors --- electronic platform --- machine learning --- wearables --- hand motor rehabilitation --- sEMG --- hand pose --- social robot --- physical human-robot interaction --- assistive robotics --- collaborative robots --- brain-machine interfaces --- EEG --- exoskeleton --- motor imagery --- human-robot interaction --- human pose estimation --- robotic rehabilitation --- visually impaired assistance --- navigation system --- knowledge graph --- dialogue system --- NLP --- reasoning --- multimodal interfaces --- robotic exoskeleton --- sensors --- electronic platform --- machine learning --- wearables --- hand motor rehabilitation --- sEMG --- hand pose --- social robot --- physical human-robot interaction --- assistive robotics --- collaborative robots --- brain-machine interfaces --- EEG --- exoskeleton --- motor imagery --- human-robot interaction --- human pose estimation --- robotic rehabilitation --- visually impaired assistance --- navigation system --- knowledge graph --- dialogue system --- NLP --- reasoning --- multimodal interfaces --- robotic exoskeleton

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People with congenital and/or acquired disabilities constitute a great number of dependents today. Robotic platforms to help people with disabilities are being developed with the aim of providing both rehabilitation treatment and assistance to improve their quality of life. A high demand for robotic platforms that provide assistance during rehabilitation is expected because of the health status of the world due to the COVID-19 pandemic. The pandemic has resulted in countries facing major challenges to ensure the health and autonomy of their disabled population. Robotic platforms are necessary to ensure assistance and rehabilitation for disabled people in the current global situation. The capacity of robotic platforms in this area must be continuously improved to benefit the healthcare sector in terms of chronic disease prevention, assistance, and autonomy. For this reason, research about human–robot interaction in these robotic assistance environments must grow and advance because this topic demands sensitive and intelligent robotic platforms that are equipped with complex sensory systems, high handling functionalities, safe control strategies, and intelligent computer vision algorithms. This Special Issue has published eight papers covering recent advances in the field of robotic platforms to assist disabled people in daily or clinical environments. The papers address innovative solutions in this field, including affordable assistive robotics devices, new techniques in computer vision for intelligent and safe human–robot interaction, and advances in mobile manipulators for assistive tasks.

sensors --- electronic platform --- machine learning --- wearables --- hand motor rehabilitation --- sEMG --- hand pose --- social robot --- physical human-robot interaction --- assistive robotics --- collaborative robots --- brain–machine interfaces --- EEG --- exoskeleton --- motor imagery --- human–robot interaction --- human pose estimation --- robotic rehabilitation --- visually impaired assistance --- navigation system --- knowledge graph --- dialogue system --- NLP --- reasoning --- multimodal interfaces --- robotic exoskeleton --- n/a --- brain-machine interfaces --- human-robot interaction

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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

Information technology industries --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower's interpolation formula --- Gower's metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower's interpolation formula --- Gower's metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering

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This is a reprint of articles from the Special Issue published online in the open access journal Journal of Functional Morphology and Kinesiology (ISSN 2411-5142) available at https://www.mdpi.com/journal/jfmk/special_issues/Exercise_Evaluation

Lifestyle, sport & leisure --- anthropometry --- body composition --- DXA --- equation --- fat mass --- soccer --- somatotype --- skinfolds --- activity pacing --- multiple sclerosis --- perceived risk of overactivity --- perceived fatigue --- health-related quality of life --- rehabilitation --- dose --- acute response --- chronic response --- internal load --- external load --- exercise and training prescription --- exercise is medicine --- personalized medicine --- physical activity --- oncology --- unsupervised exercise --- lifestyle --- exercise prescription --- body weight --- obesity --- motor control test --- stability --- Y balance test --- first position --- Nordic walking --- type 2 diabetes --- cardiometabolic fitness --- target heart rate --- RPE --- Talk Test --- hydration --- urine specific gravity --- athletes --- heart rate --- fatigue --- alertness --- BIVA --- phase angle --- R-Xc graph --- tolerance ellipses --- reaction time --- pilots --- motor imagery --- video observation --- renal health --- wearable devices --- technology --- acute kidney injury --- inertial measurement units (IMU) --- energy availability --- relative energy deficiency in sport --- sports nutrition --- women soccer athletes --- nutrition knowledge --- LEAF-Q --- COVID-19 --- prevention --- inactivity --- home-based exercise --- mental health --- psychological well-being --- n/a