Microsoft Kinect-based differences in lower limb kinematics and temporal characteristics of sit to walking phase of modified TUG test between men with and without Parkinson's disease

http://www.ester.ee/record=b476770

HUMAN GENDER CLASSIFICATION USING KINECT SENSOR: A REVIEW

Human Gender Classification using Kinect sensor aims to classifying people’s gender based on their outward appearance. Application areas of Kinect sensor technology includes security, marketing, healthcare, and gaming. However, because of the changes in pose, attire, and illumination, gender determination with the Kinect sensor is not a trivial task. It is based on a variety of characteristics, including biological, social network, face, and body aspects. In recent years, gender classification that utilizes the Kinect sensor became a popular and essential way for accurate gender classification. A variety of methods and approaches, like machine learning, convolutional neural networks, sport vector machine (SVM), etc., have been used for gender classification using a Kinect sensor. This paper presents the state of the art for gender classification, with a focus on the features, databases, procedures, and algorithms used in it. A review of recent studies on this subject using the Kinect sensor and other technologies is provided, together with information on the variables that affect the classification\u27s accuracy. In addition, several publicly accessible databases or datasets are used by researchers to classify people by gender are covered. Finlay, this overview offers insightful information about the potential future avenues for research on Kinect-based human gender classification

Comparative Analysis of The Effects Of Virtual Reality Active Video Game And Controller-Free Active Video Game Play On Physiological Response, Perceived Exertion, And Hedonic Experience

Over 60% of US adults are overweight or obese. Sedentary lifestyles are considered major contributors to the high rates and increasing prevalence of obesity. Physical activity is a critical component in shifting from sedentary lifestyles. Studies indicate that less than half of U.S. adults meet the CDC/ACSM physical activity recommendations. Interactive video games can increase PA, but no study has yet assessed physiologic effort, hedonics, and perceived exertion for playing immersive virtual reality (VR) and controller-free screen-based active video games (AVGs), compared to treadmill walking and resting. We ran 25 subjects (9 female, 16 male) in 10-minute sessions of five conditions. Head Mounted Display VR: Oculus (Fruit Ninja and Boxing), Screen-based AVG: Kinect (Fruit Ninja and Boxing), and Treadmill walking at 3 mph. One, six-condition (Rest, Treadmill 3.0, Kinect Boxing, Kinect Fruit Ninja, Oculus Boxing, Oculus Fruit Ninja) repeated-measures ANOVA was used to examine differences in HRmean. Three, five-condition (Treadmill 3.0, Kinect Boxing, Kinect Fruit Ninja, Oculus Boxing, Oculus Fruit Ninja) repeated-measures ANOVA were used to examine differences in HRpeak, ratings of perceived exertion (RPE) and Hedonics (Liking). Post hoc analyses using pairwise comparisons were used to further assess significant main effects of the condition. A Pearson\u27s product-moment correlation was run to assess the relationship between activity condition HRmean and RPE VR Boxing elicited the greatest physiological effort, producing vigorous-intensity PA. There was no significant difference in average heart rate for the Treadmill, Kinect Fruit Ninja, Kinect Boxing, and VR Fruit Ninja. Thus, the Kinect and VR sport and casual games are comparable to treadmill walking PA levels and qualify as moderate-intensity activity. The VR Fruit Ninja, VR Boxing, Kinect Fruit Ninja were the most enjoyed activities. Despite having the highest Heart rate and the highest self-reported Rating of Perceived Exertion (RPE), VR Boxing was significantly more enjoyable than Treadmill Walking. There was no statistically significant correlation between Activity Condition HRmean and RPE. Both casual and sports VR and AVG activities are enjoyable activities for adults, stimulating moderate-to-vigorous activity through a traditionally sedentary medium. This research extends previous works in active video gaming effects on physiological cost, perceived exertion and hedonics and fills the gap relating virtual reality active video games. The significance of the research outcomes is that this analysis provides a scientifically validated approach to support the establishment of physical activity level goals and guidelines in the development of active video games as a response and/or remedy to address the sedentary lifestyles that are contributing to American and global obesity

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Spotting prejudice with nonverbal behaviours

Despite prejudice cannot be directly observed, nonverbal behaviours provide profound hints on people inclinations. In this paper, we use recent sensing technologies and machine learning techniques to automatically infer the results of psychological questionnaires frequently used to assess implicit prejudice. In particular, we recorded 32 students discussing with both white and black collaborators. Then, we identified a set of features allowing automatic extraction and measured their degree of correlation with psychological scores. Results confirmed that automated analysis of nonverbal behaviour is actually possible thus paving the way for innovative clinical tools and eventually more secure societies

Implementation of Open Source applications “Serious Game” for rehabilitation

Serious Games and Virtual Reality (VR) are present nowadays as an alternative to traditional rehabilitation therapies. This project describes the workflow to develop videogames for health monitoring as well as a source of entertainment for physiotherapy patients, primarily patients that suffer hemiparesis caused by a neurological disease like a stroke. We propose the last version of Microsoft Kinect sensors as low cost game controller and the software Unity to develop Open Source Rehabilitation Serious Games. These Serious Games try to imitate physiotherapy sessions performed in movement recovery therapies, reducing the waiting list of patients together with time and costs to hospitals. The premise is that the gameplay makes patients execute upper body exercises alongside equilibrium training, meanwhile they are monitored extracting useful data and results for the physicians.Ingeniería Biomédic