The sensorimotor Dimension of the Networked Flow: an Exploratory Study Using an Interactive Collaborative Platform

The ability to cooperate with another individual in order to achieve a shared goal is crucial for human survival and it is called joint action. It is noted that this process can originate when actors are synchronized at a sensorimotor level. However, experiential correlates of sensorimotor synchronization are nearly unexplored. The aim of this study was to investigate the relationship between the experience of flow and social presence in a sensorimotor collaborative task. 12 female couples (mean age = 22.33; S.D. = .815) and 12 male couples (mean age = 22.88; S.D. = .789) were involved in a tower-building task across 10 consecutive trials using the COLLEGO platform. Couple members alternated their leader/follower role. Platform recorded time stamp (ms) and position of each selected object when it was picked/released, providing a measure of performance. Thereafter, participants’ level of flow (Flow State Scale) and social presence (Networked Minds Social Presence Inventory) were assessed. Flow and Social presence correlated positively at a global level. Having clear goals and perceiving a balance between challenges and skills were associated with a higher performance. The autotelic dimension of flow was negatively related with global performance. At the same time, task duration correlated negatively with cognitive and behavioral dimensions of social presence, but positively with emotional dimensions. Results are discussed in light of the Networked Flow model that assumes a strong positive link between social presence and flow at the base of the highest levels of collaborative performance

CLASSIFICATION OF BIPOLAR DISORDER, MAJOR DEPRESSIVE DISORDER, AND HEALTHY STATE USING VOICE

Objective: In this study, we propose a voice index to identify healthy individuals, patients with bipolar disorder, and patients with major depressive disorder using polytomous logistic regression analysis.Methods: Voice features were extracted from voices of healthy individuals and patients with mental disease. Polytomous logistic regression analysis was performed for some voice features.Results: With the prediction model obtained using the analysis, we identified subject groups and were able to classify subjects into three groups with 90.79% accuracy.Conclusion: These results show that the proposed index may be used as a new evaluation index to identify depression

Hand features extractor using hand contour – a case study

Hand gesture recognition is an important topic in natural user interfaces (NUI). Hand features extraction is the first step for hand gesture recognition. This work proposes a novel real time method for hand features recognition. In our framework we use three cameras and the hand region is extracted with the background subtraction method. Features like arm angle and fingers positions are calculated using Y variations in the vertical contour image. Wrist detection is obtained by calculating the bigger distance from a base line and the hand contour, giving the main features for the hand gesture recognition. Experiments on our own data-set of about 1800 images show that our method performs well and is highly efficient

Brain-Computer Interface for Clinical Purposes : Cognitive Assessment and Rehabilitation

Alongside the best-known applications of brain-computer interface (BCI) technology for restoring communication abilities and controlling external devices, we present the state of the art of BCI use for cognitive assessment and training purposes. We first describe some preliminary attempts to develop verbal-motor free BCI-based tests for evaluating specific or multiple cognitive domains in patients with Amyotrophic Lateral Sclerosis, disorders of consciousness, and other neurological diseases. Then we present the more heterogeneous and advanced field of BCI-based cognitive training, which has its roots in the context of neurofeedback therapy and addresses patients with neurological developmental disorders (autism spectrum disorder and attention-deficit/hyperactivity disorder), stroke patients, and elderly subjects. We discuss some advantages of BCI for both assessment and training purposes, the former concerning the possibility of longitudinally and reliably evaluating cognitive functions in patients with severe motor disabilities, the latter regarding the possibility of enhancing patients' motivation and engagement for improving neural plasticity. Finally, we discuss some present and future challenges in the BCI use for the described purposes

A P5 Approach to m-Health: Design Suggestions for Advanced Mobile Health Technology

In recent years, technology has been developed as an important resource for health care management, especially in regard to chronic conditions. In the broad field of eHealth, mobile technology (mHealth) is increasingly used to empower patients not only in disease management but also in the achievement of positive experiences and experiential growth. mHealth tools are considered powerful because, unlike more traditional Internet-based tools, they allow patients to be continuously monitored and followed by their own mobile devices and to have continual access to resources (e.g., mobile apps or functions) supporting health care management activities. However, the literature has shown that, in many cases, such technology not accepted and/or adopted in the long term by its users. To address this issue, this article reviews the main factors influencing mHealth technology acceptance/adoption in health care. Finally, based on the main aspects emerging from the review, we propose an innovative approach to mHealth design and implementation, namely P5 mHealth. Relying on the P5 approach to medicine and health care, this approach provides design suggestions to address mHealth adoption issues already at the initial stages of development of the technologies

A P5 Approach to m-Health : Design Suggestions for Advanced Mobile Health Technology

In recent years, technology has been developed as an important resource for health care management, especially in regard to chronic conditions. In the broad field of eHealth, mobile technology (mHealth) is increasingly used to empower patients not only in disease management but also in the achievement of positive experiences and experiential growth. mHealth tools are considered powerful because, unlike more traditional Internet-based tools, they allow patients to be continuously monitored and followed by their own mobile devices and to have continual access to resources (e.g., mobile apps or functions) supporting health care management activities. However, the literature has shown that, in many cases, such technology not accepted and/or adopted in the long term by its users. To address this issue, this article reviews the main factors influencing mHealth technology acceptance/adoption in health care. Finally, based on the main aspects emerging from the review, we propose an innovative approach to mHealth design and implementation, namely P5 mHealth. Relying on the P5 approach to medicine and health care, this approach provides design suggestions to address mHealth adoption issues already at the initial stages of development of the technologie

A mobile cloud computing framework integrating multilevel encoding for performance monitoring in telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment