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

Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement

As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system's accuracy and investigate a wireless alternative based on neural networks.Comment: 47 pages, 20 figures, 17 tables (including annexes), part of the MDPI Sesnsors "Special Issue Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living

Improving the Audio Game-Playing Performances of People with Visual Impairments Through Multimodal Training

As the number of people with visual impairments (that is, those who are blind or have low vision) is continuously increasing, rehabilitation and engineering researchers have identified the need to design sensorysubstitution devices that would offer assistance and guidance to these people for performing navigational tasks. Auditory and haptic cues have been shown to be an effective approach towards creating a rich spatial representation of the environment, so they are considered for inclusion in the development of assistive tools that would enable people with visual impairments to acquire knowledge of the surrounding space in a way close to the visually based perception of sighted individuals. However, achieving efficiency through a sensory substitution device requires extensive training for visually impaired users to learn how to process the artificial auditory cues and convert them into spatial information. Methods: Considering all the potential advantages gamebased learning can provide, we propose a new method for training sound localization and virtual navigational skills of visually impaired people in a 3D audio game with hierarchical levels of difficulty. The training procedure is focused on a multimodal (auditory and haptic) learning approach in which the subjects have been asked to listen to 3D sounds while simultaneously perceiving a series of vibrations on a haptic headband that corresponds to the direction of the sound source in space. Results: The results we obtained in a sound-localization experiment with 10 visually impaired people showed that the proposed training strategy resulted in significant improvements in auditory performance and navigation skills of the subjects, thus ensuring behavioral gains in the spatial perception of the environment.Sound of Vision, Horizon 2020 nr. 643636Peer Reviewe

Multispectral Imaging for Skin Diseases Assessment—State of the Art and Perspectives

Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light–tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device

Cloth simulation using soft constraints

This paper describes a new way of using projective methods for simulating the constrained dynamics of deformable surfaces. We show that the often used implicit integration method for discretized elastic systems is equivalent to the projection of regularized constraints. We use this knowledge to derive a Nonlinear Conjugate Gradient implicit solver and a new projection scheme based on energy preserving integration. We also show a novel way of adding damping to position based dynamics and a different view on iterative solvers. In the end we apply these fresh insights to cloth simulation and develop a constraint based finite element method capable of accurately modeling thin elastic materials

A Genetic Algorithm for Automated Service Binding

AbstractBinding concrete web services to the tasks involved in an orchestration model is an important step in dynamic web service composition. In our previous work on automated service binding, we have described a method of combining a powerful technique for computing the QoS of a composite service with our own approach of expressing preferences related to the trade-offs between the various QoS parameters of a composite service. In this paper, we introduce a genetic algorithm that finds the best mapping of concrete services to the tasks involved in the composition. The algorithm uses the method presented in our previous work in order to estimate the fitness of the mappings that make up a population of candidate solutions. We describe how the challenges posed by the use of this method have influenced the design of our algorithm and report the experimental results showing the effectiveness of our approach

QoS-Aware Web Service Semantic Selection Based on Preferences

AbstractThe emergence of service oriented computing brought major research challenges in the process of discovery and selection of web services. Key to success in obtaining the best fitting web service out of a multitude of alternatives is finding the service that both satisfies the requester's functional needs and non-functional requirements. Clients should also be able to indicate how to make trade-offs when some of thir requirements cannot be met. The ability to capture trade-off preferences is critical for selecting the best fitting web service. In this paper, we address the problem of expressing the requester's preferences in a semantic context. We analyze the existing QoS ontologies and choose to extend the OWL-Q ontology to capture trade-off preferences expressed using QoSPref, our own conditional lexicographic approach for QoS preference specification. We extend the QoSSpec specification facet of OWL-Q by adding a new class QoSSelectionWithTradeoffs, that offers the possibility of describing the list of preferences and trade-offs similar to our QoSPref notation, thus offering the possibility of using our algorithm for ranking web service alternatives