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Statistical Machine Learning for Human Behaviour Analysis
Statistical Machine Learning for Human Behaviour Analysis
Autore Moeslund Thomas
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 electronic resource (300 p.)
Soggetto topico History of engineering & technology
Soggetto non controllato multi-objective evolutionary algorithms
rule-based classifiers
interpretable machine learning
categorical data
hand sign language
deep learning
restricted Boltzmann machine (RBM)
multi-modal
profoundly deaf
noisy image
ensemble methods
adaptive classifiers
recurrent concepts
concept drift
stock price direction prediction
toe-off detection
gait event
silhouettes difference
convolutional neural network
saliency detection
foggy image
spatial domain
frequency domain
object contour detection
discrete stationary wavelet transform
attention allocation
attention behavior
hybrid entropy
information entropy
single pixel single photon image acquisition
time-of-flight
action recognition
fibromyalgia
Learning Using Concave and Convex Kernels
Empatica E4
self-reported survey
speech emotion recognition
3D convolutional neural networks
k-means clustering
spectrograms
context-aware framework
accuracy
false negative rate
individual behavior estimation
statistical-based time-frequency domain and crowd condition
emotion recognition
gestures
body movements
Kinect sensor
neural networks
face analysis
face segmentation
head pose estimation
age classification
gender classification
singular point detection
boundary segmentation
blurring detection
fingerprint image enhancement
fingerprint quality
speech
committee of classifiers
biometric recognition
multimodal-based human identification
privacy
privacy-aware
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557288403321
Moeslund Thomas  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Virtual Reality in the Assessment, Understanding and Treatment of Mental Health Disorders
Virtual Reality in the Assessment, Understanding and Treatment of Mental Health Disorders
Autore Riva Giuseppe
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Descrizione fisica 1 electronic resource (316 p.)
Soggetto topico Information technology industries
Soggetto non controllato Generalized Anxiety Disorder (GAD)
virtual reality
exposure in virtual reality
cognitive exposure
standardized scenario
personalized scenario
spatial memory
episodic memory
enactment
memory rehabilitation
embodied cognition
aging
body image disturbances
body anxiety
fear of gaining weight
full body illusion
body representation
obesity
health
navigation
neurorehabilitation
systematic review
virtual environment
cognitive and physical rehabilitation
oldest old person
Obsessive-compulsive disorders
multiple errands test
cognitive assessment
executive functions
computational models
decision tree
cross-validation
real phobic images
anxiety disorders
specific phobia
fMRI
neuroimaging
anorexia nervosa
body image distortion
body dissatisfaction
embodiment
interpersonal multisensory stimulation
pain perception
telescoped effect
amputee patients
emotion regulation
treatment
wellbeing intervention
adults
distraction systems
dental anxiety
pain
autism spectrum disorder
body movements
repetitive behaviors
machine learning
dementia
mild cognitive impairment
electroencephalogram
serious game
Alzheimer disease
digital biomarker
hippocampus
MRI
cognitive rehabilitation
computerized assessment
sense of reality
hallucinations
psychosis
derealization
sense of agency
metacognition
stress
bodily-self
mental health
presence
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557760003321
Riva Giuseppe  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui