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Self-supervised learning has shown value for uncovering informative movement features for human activity recognition. However, there has been minimal exploration of this approach for affect recognition where availability of large labelled datasets is particularly limited. In this paper, we propose a P-STEMR (Parallel Space-Time Encoding Movement Representation) architecture with the aim of addressing this gap and specifically leveraging the higher availability of human activity recognition datasets for pain-level classification. We evaluated and analyzed the architecture using three different datasets across four sets of experiments. We found statistically significant increase in average F1 score to 0.84 for pain level classification with two classes based on the architecture compared with the use of hand-crafted features. This suggests that it is capable of learning movement representations and transferring these from activity recognition based on data captured in lab settings to classification of pain levels with messier real-world data. We further found that the efficacy of transfer between datasets can be undermined by dissimilarities in population groups due to impairments that affect movement behaviour and in motion primitives (e.g. rotation versus flexion). Future work should investigate how the effect of these differences could be minimized so that data from healthy people can be more valuable for transfer learning. [ABSTRACT FROM AUTHOR]

Anxiety symptoms are important contributors to the global health-related burden. Low-intensity interventions have been proposed to reduce anxiety symptoms in the population. Among these, biofeedback (BF) offers an effective approach to reducing anxiety. In the present study, BF was integrated into a novel virtual reality (VR) architecture to enhance BF's effectiveness to 1) evaluate the feasibility of a VR-based single-session BF in teaching participants to self-regulate; 2) compare the BF aiming at reducing sympathetic (measured though the tonic level of skin conductance, SCL) versus increasing cardiac vagal (i.e., normalized high frequency of heart rate variability, HFnu-HRV) activation, and 3) evaluate which of the two VR-BF single-sessions was most effective in reducing perceived state anxiety. 20 healthy participants underwent both SCL- and HFnu-based in a single session VR-BF. Results showed the feasibility of a short single-session VR-BF and the effectiveness of both VR-BF sessions in reducing perceived state anxiety. Moreover, SCL-based VR-BF determined a significant reduction in sympathetic activation and in sympathovagal balance as well as a greater reduction in perceived state anxiety compared to HFnu-based VR-BF. SCL-based VR-BF represents a safe and effective intervention in reducing anxiety while enhancing adaptive psychophysiological activation. [ABSTRACT FROM AUTHOR]

Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear. [ABSTRACT FROM AUTHOR]