Choose an application

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

Touch is one of the fundamental media for interpersonal communication. Over recent decades, scientific efforts have been devoted to establishing the significance of touch, particularly affective touch, in the treatment and prevention of mental disorders and clarifying the underlying mechanisms of touch and massage therapy. This book contributes to this rapidly expanding area of research and gives new insights on recent clinical and experimental findings. A strong plea is made by the editors for well-designed clinical studies which require very special methodologies. A broad spectrum of various touch therapies are already available at present. Modern treatment and prevention of mental disorders should go beyond the pharmacological and psychotherapeutic approaches and should make use of the beneficial effects of touch therapies with the additional benefit of a very small risk of adverse outcomes.

Humanities --- Social interaction --- orienting reflex --- motivational system --- touch therapy --- integrative psychotherapy --- somatic psychology --- touch --- pain --- C-tactile afferents --- fibromyalgia --- anhedonia --- fMRI --- posterior insula --- sports massage --- current emotional state --- mood --- therapist’s sex --- athlete’s sex --- massage therapy --- psychoactive massage --- affect-regulating massage therapy --- affective touch --- depression --- interoception --- C-tactile fibers --- body psychotherapy --- chronic back pain --- oxytocin --- somatoform pain (ICD 10) --- somatic symptom disorder (DSM-5) --- n/a --- therapist's sex --- athlete's sex

Choose an application

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

Comfort during resting, half-closed eyes when feeding on highly palatable feed, or vigorous tail wagging when being brushed are some of the positive indicators that can be used to evaluate the quality of the environment in which domestic animals live. This has been a radical shift from the past as, until now, the welfare assessment on farms has meant assessing negative indicators, namely the number of lame animals, presence of lesions, or frequency of agonistic behaviours. However, the latest research confirms that the absence of a problem or of suffering does not necessarily imply that the animals are experiencing a good life and that their level of welfare is high. To guarantee high welfare standards, animals should experience positive conditions that allow them to live a “life worth living”, and positive indicators are needed to identify these conditions. This Special Issue focuses on the development and validation of indicators of positive welfare or on the refinement of the existing ones, as well as on the identification of suitable living conditions for providing positive welfare to farmed and companion animals.

positive indicators --- animal welfare --- term list --- recognition --- welfare --- Felis catus --- enrichment --- pigs --- free-farrowing --- contact --- circadian rhythm --- dairy cows --- sow --- qualitative research --- positive emotions --- ear posture --- emotions --- welfare assessment protocol --- valence --- cattle --- phonetics --- maternal care --- buffaloes --- mother–offspring --- positive animal welfare indicators --- quality of life --- qualitative behaviour assessment (QBA) --- farm size --- arousal --- free elicitation narrative interviewing --- emotional state --- housing --- ruminants --- farmer knowledge --- happiness --- nosing --- five domains --- piglet --- sheep --- organic --- cat behavior --- observer reliability --- eye white --- goats --- immunoglobulin A --- critical review --- positive animal welfare --- calves --- saliva --- farmer attitudes --- behaviour --- positive welfare --- positive affective engagement

Choose an application

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

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.

sleep stage scoring --- neural network-based refinement --- residual attention --- T-end annotation --- signal quality index --- tSQI --- optimal shrinkage --- emotion --- EEG --- DEAP --- CNN --- surgery image --- disgust --- autonomic nervous system --- electrocardiogram --- galvanic skin response --- olfactory training --- psychophysics --- smell --- wearable sensors --- wine sensory analysis --- accuracy --- convolution neural network (CNN) --- classifiers --- electrocardiography --- k-fold validation --- myocardial infarction --- sensitivity --- sleep staging --- electroencephalography (EEG) --- brain functional connectivity --- frequency band fusion --- phase-locked value (PLV) --- wearable device --- emotional state --- mental workload --- stress --- heart rate --- eye blinks rate --- skin conductance level --- emotion recognition --- electroencephalogram (EEG) --- photoplethysmography (PPG) --- machine learning --- feature extraction --- feature selection --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer’s disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration --- n/a --- Alzheimer's disease