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The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users’ perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users’ perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely PROTEIN, and the mTAM constructs towards behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of two months, were subjected to regression analysis to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users’ behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1-H6), respectively. The regression results have shown that all hypotheses H1-H6 can be accepted (), exhibiting adjusted coefficient of determination () within the range of 0.224-0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiologists experts and marketing experts for app design/development towards behavior change.

Food analysis resides at the core of modern nutrition recommender systems, providing the foundation for a high-level understanding of users’ eating habits. This paper focuses on the sub-task of ingredient recognition from food images using a variational framework. The framework consists of two variational encoder-decoder branches, aimed at processing information from different modalities (images and text), as well as a variational mapper branch, which accomplishes the task of aligning the distributions of the individual branches. Experimental results on the Yummly-28K data-set showcase that the proposed framework performs better than similar variational frameworks, while it surpasses current state-of-the-art approaches on the large-scale Recipe1M data-set.

People with various degrees of disabilities (e.g., visually or hearing impaired) often find it difficult to access mainstream products and services and thus they are excluded from enjoying audio-visual services on an equal basis as people without disabilities. These people feel marginalized in today's digital society and they are unable to reach their maximum potential personally, professionally and socially. EasyTV aims to address these issues by providing numerous services that not only ease the access of people with disabilities to multimedia services, but also enhance interaction with the media through a multi-language approach that adapts content based on user's preferences. In this work, the main EasyTV services are described in detail. Furthermore, an evaluation of the main services by end users is presented and discussed in order to highlight the importance of offering accessibility services to people with disabilities.

In this paper, we propose a novel cross-modal variational alignment method in order to process and relate information across different modalities. The proposed approach consists of two variational autoencoder (VAE) networks which generate and model the latent space of eachmodality. The first network is a multi-modal variational-autoencoder that maps directly one modality to the other, while the second one is a single-modal variational autoencoder. In order to associate the two spaces, we apply variational alignment, which acts as a translation mechanismthat projects the latent space of the first VAE onto the one ofthe single-modal VAE through an intermediate distribution.Experimental results on four well-known datasets, covering two different application domains (food image analysisand 3D hand pose estimation), show the generality of theproposed method and its superiority against a number of state-of-the-art approaches.

Past research has now provided compelling evidence pointing towardscorrelations among individual eating styles and the development of (un)healthyeating patterns, obesity and other medical conditions. In this setting, an automatic,non-invasive food bite detection system can be a really useful tool in the handsof nutritionists, dietary experts and medical doctors in order to explore real-lifeeating behaviors and dietary habits.Unfortunately, the automatic detection of foodbites can be challenging due to occlusions between hands and mouth, use of differentkitchen utensils and personalized eating habits. On the other hand, althoughaccurate, manual bite detection is time-consuming for the annotator, making itinfeasible for large scale experimental deployments or real-life settings. To thisregard, we propose a novel deep learning methodology that relies solely on humanbody and face motion data extracted from videos depicting people eating meals.The purpose is to develop a system that can accurately, robustly and automaticallyidentify food bite instances,with the long-term goal to complement or even replacemanual bite-annotation protocols currently in use. The experimental results on alarge dataset reveal the superb classification performance of the proposed methodologyon the task of bite detection and paves the way for additional research onautomatic bite detection systems.

Parkinson’s disease (PD) is a progressive neurological disorder and the second most common age-related neurodegenerative disease after Alzheimer's disease. The primary symptoms of the disease are associated with the loss of motor skills affecting patients’ movement and coordination and disrupting their daily life. Unfortunately, such motor symptoms cannot be fully relieved by therapeutic options. On the other hand, studies have shown that regular training and exercising can prove neuroprotective in PD patients helping them maintain independent longer. Based on recent studies stating that computer-based physical therapy games can be used as an option for facilitating PD rehabilitation exercise programs, we present the development of a body motion based videogame, using the Kinect sensor, targeted for PD patients. We tested twelve patients with advanced forms of PD motor symptoms (UPDRS motor score>20) and six initial stage PD patients (UPDRS motor score

Sign language recognition (SLR) refers to theclassification of signs with a specific meaning performed by thedeaf and/or hearing-impaired people in their everydaycommunication. In this work, we propose a deep learningbased framework, in which we examine and analyze thecontribution of video (image and optical flow) and skeletal(body, hand and face) features in the challenging task of isolated SLR, in which each signed video corresponds to asingle word. Moreover, we employ various fusion schemes inorder to identify the optimal way to combine the informationobtained from the various feature representations and proposea robust SLR methodology. Our experimentation on two signlanguage datasets and the comparison with state-of-the-artSLR methods reveals the superiority of optimally combiningskeletal and video features for SLR tasks.

The accuracy of modern depth sensors, the robustnessof skeletal data to illumination variations and the superbperformance of deep learning techniques on several classificationtasks have sparkled a renewed interest towards skeleton basedaction recognition. In this paper, we propose a four streamdeep neural network based on two types of spatialskeletal features and their corresponding temporal representationsextracted by the novel Grassmannian Pyramid Descriptor(GPD). The performance of the proposed action recognitionmethodology is further enhanced by the use of a meta-learnerthat takes advantage of the meta knowledge extracted fromthe processing of the different features. Experiments on severalwell-known action recognition datasets reveal that ourproposed methodology outperforms a number of state-of-the-artskeleton-based action recognition methods.

Sign language recognition (SLR) is a challenging, but highly important research field for several computer vision systems that attempt to facilitate the communication among the deaf and hearing impaired people. In this work, we propose an accurate and robust deep learning-based methodology for sign language recognition from video sequences. Our novel method relies on hand and body skeletal features extracted from RGB videos and, therefore, it acquires highly discriminative for gesture recognition skeletal data without the need for any additional equipment, such as data gloves, that may restrict signer’s movements. Experimentation on a large publicly available sign language dataset reveals the superiority of our methodology with respect to other state of the art approaches relying solely on RGB features.

In this paper, the user requirements, along with the methodology adopted towards their identification within the i-PROGNOSIS framework (www.i-prognosis.eu), are presented. The latter are placed within the concept of active and healthy ageing (AHA), focusing on the case of Parkinson’s Disease (PD) patients’ support. The bases for the user requirements identification were face-to-face sessions, focus groups and a large scale Web-survey. Towards the efficient user requirements identification and i-PROGNOSIS components development, exemplified usage scenarios and related business processes the stakeholders of i-PROGNOSIS can perform, are discussed. Overall, 122 functional and non-functional requirements were identified, serving as a basis for the spiral development model of i-PROGNOSIS, revealing the beneficial role of the users in designing solutions within the AHA concept.

In this paper, we present an emotion recognition methodology that utilizes information extracted from body motion analysis to assess affective state during gameplay scenarios. A set of kinematic and geometrical features are extracted from joint-oriented skeleton tracking and are fed to a deep learning network classifier. In order to evaluate the performance of our methodology, we created a dataset with Microsoft Kinect recordings of body motions expressing the five basic emotions (anger, happiness, fear, sadness and surprise) which are likely to appear in a gameplay scenario. In this five emotions recognition problem, our methodology outperformed all other classifiers, achieving an overall recognition rate of 93 %. Furthermore, we conducted a second series of experiments to perform a qualitative analysis of the features and assess the descriptive power of different groups of features.

Early and accurate detection and localization of flame is an essential requirement of modern early fire warning systems. Video-based systems can be used for this purpose; however, flame detection remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. In this paper, we present a new algorithm for video based flame detection, which employs various spatio-temporal features such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. Various background subtraction algorithms are tested and comparative results in terms of computational efficiency and accuracy are presented. Experimental results with two classification methods show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio. Finally, a 3D visualization tool for the estimation of the fire propagation is outlined and simulation results are presented and discussed.

People with various degrees of disabilities (e.g., visually or hearing impaired) often find it difficult to access mainstream products and services and thus they are excluded from enjoying audio-visual services on an equal basis as people without disabilities. These people feel marginalized in today’s digital society and they are unable to reach their maximum potential personally, professionally and socially. EasyTV aims to address these issues by providing numerous services that not only ease the access of people with disabilities to multimedia services, but also enhance interaction with the media through a multi-language approach that adapts content based on user’s preferences. In this work, the main EasyTV services are described in detail. Furthermore, an evaluation of the main services by end users is presented and discussed in order to highlight the importance of offering accessibility services to people with disabilities.