Artificial Intelligence for Psychological State Recognition: A Perspective based on Theravada Meditational Practices in Sri Lanka for Health Intervention

 The World Health Organization defines health as a state of complete physical, mental and social wellbeing, and not merely the absence of disease or infirmity. The emphasis of physical fitness is not the sole basis of being healthy and it includes being mentally and emotionally fit, which is also a part of the overall lifestyle of a person. Mental stress is a key contributing psychological factor to health problems, and a vast amount of research literature links stress to adverse health outcomes. Stress can directly affect health through autonomic and neuroendocrine responses and indirectly through changes in health behaviours [1].Research on scientific conceptualizations of meditational practices as an alternative mind-body therapy is a key area related to psychological health that received scientific attention recently. There are several types of meditation practices, and they are not limited to the different traditions of Buddhism, but practiced in some other Asian religions, including Hinduism [2]. Buddhist meditation traditions include Theravada, Mayahana and other related traditions, such as Vajirayana. Physiologically, meditation affects heart rate,respiratory rate, blood pressure, cortical activity, metabolism, respiration and skin conductance, while psychological effects include perceptual ability, memory and intelligence, and creativity and selfactualization [2],[3]. As revealed by studies, different meditation traditions have exclusive influences on activating the autonomic system and attentional mechanisms. Theravada and Mahayana types of meditation increase the parasympathetic activation that results in a relaxation response. On the other hand, a robust arousal response is triggered by enhanced sympathetic activation caused by Hindu Tantric practice [2].The new developments in artificial intelligence have changed how psychological states are identified, classified, and managed for human well-being. Shatte et al [4] conducted a review on machine learning in mental health with over 300 studies and identified, (i) detection and diagnosis (ii) prognosis, treatment and support (iii) public health and (iv) research and clinical administration as the main research domains. Support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering werethe main machine learning techniques used, and common diseases addressed include depression, schizophrenia, and Alzheimer’s disease. In a similar study by Su et al [5] with over 135 deep learning research, it was revealed that the analysis of genetics and genomics data for understanding psychological health status, vocal and visual expression data analysis for diagnosis, and assessment of the risk of psychological disorders using social media data is possible in addition to the above findings. These studies prove, along with evidence for improvements, the potential of artificial intelligence in enhancing the diagnosis and treatment of patients with psychological health conditions. An important area of research in recognizing human emotional states is machine learning classifiers and signals extracted from bio signals like an electroencephalogram (EEG) [6]. EEG signals provide rich information about mental states and conditions, while high resolution, low cost and convenience are additional advantages [7]. Amihai and Kozhevnikov [2] identified the unique influences of different meditative traditions through EEG patterns that could be differentiated from other altered states.Investigations of the influences of the specific cultural and philosophical contexts in which meditation practices arose on psychophysiological outcomes using artificial intelligence-based interventions is an emerging area of research by cognitive scientists and neuroscientists. The scientific examination of the meditative practices on Zen, Yoga and recent trends in mindfulness are prominent in the literature; however, the influences of Theravada meditation on the physiology and behaviour of the practitioners are still at the early stages of receiving attention from the scientific community. States of mind resulting from meditation can vary based on numerous factors. Different states of mind that can be achieved through Theravada meditation are (i) relaxed and calm state of mind arising from long-term practice of meditation, (ii) concentrative and mindfulness state (iii) perfect states of concentrative meditation or ‘dhayana’ (iv) mindfulness or ‘vidarshana’ state. These states and the different psychological impacts are some of the areas of Theravada tradition where artificial intelligence and associated technologies are required to explore the possibilities of meditative mind-body therapy. To the best of my knowledge, there is no literature on recognising the psychological states when themeditator is in varied and advanced states of mind. Amihai and Kozhevnikov [2] have pointed out that studies are needed to investigate the mechanisms underlying changes of the autonomic nervous system during meditation to unveil the precise factors that cause the arousal and relaxation responses, such as the respiration changes. Since meditation is increasingly being used as a form of therapy, a transformation that should occur through artificial intelligence-related applications is to change the state of mind to achieve a certain level of psychological states such as relaxation and relief from anxiety and tension. This raises the opportunity to change the brain signal pattern or lifestyle using an artificial way in a form of a mechanism to alert the psychological states by identifying brain waves. Identifying the psychological states of meditators in their real-life and normal behaviour rather than in the controlled environment is a challenging area of future research. Although most studies have induced stress in controlled environments, developing a protocol that sustains real scenarios such as virtual reality and an online system with stress recognition in real-time will be better than offline experiments [7]. Technological advances like social media, smartphones, wearable and neuroimaging provide opportunities to obtain an extensive range of data at a rapidly growing rate which could be used for psychological state identification. One such research area under investigation is the development of models for optimal emotion recognition based on machine learning algorithms and EEG-video fusion [8]. Research on emotion recognition, multimedia feature fusion and human perception based on artificial intelligence and associated technologies have potential for meditative mind-body therapy. The impact of meditation in different states of mind has been proven to some extent by research. Theravada traditions of meditation that underscore ‘internally steadying’ or stabilize the ‘unstable mind’ can potentially intervene in the health of the practitioner strikingly. Artificial intelligence and associated technologies have a greater potential to recognise psychological states and thereby change the brain signal pattern in an artificial way to cultivate the state of quiescence and tranquility

Role of Artificial Intelligence (AI) art in care of ageing society: focus on dementia

open access articleBackground: Art enhances both physical and mental health wellbeing. The health benefits include reduction in blood pressure, heart rate, pain perception and briefer inpatient stays, as well as improvement of communication skills and self-esteem. In addition to these, people living with dementia benefit from reduction of their noncognitive, behavioural changes, enhancement of their cognitive capacities and being socially active. Methods: The current study represents a narrative general literature review on available studies and knowledge about contribution of Artificial Intelligence (AI) in creative arts. Results: We review AI visual arts technologies, and their potential for use among people with dementia and care, drawing on similar experiences to date from traditional art in dementia care. Conclusion: The virtual reality, installations and the psychedelic properties of the AI created art provide a new venue for more detailed research about its therapeutic use in dementia

Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

Philosophical Signposts for Artificial Moral Agent Frameworks

This article focuses on a particular issue under machine ethics—that is, the nature of Artificial Moral Agents. Machine ethics is a branch of artificial intelligence that looks into the moral status of artificial agents. Artificial moral agents, on the other hand, are artificial autonomous agents that possess moral value, as well as certain rights and responsibilities. This paper demonstrates that attempts to fully develop a theory that could possibly account for the nature of Artificial Moral Agents may consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency. At the very least, the said philosophical concepts may be treated as signposts for further research on how to truly account for the nature of Artificial Moral Agents

Building the case for actionable ethics in digital health research supported by artificial intelligence

The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research

Triple Helix, Fall 2018

Table of Contents: Science Agenda: The Politics of Grant Writing / by Kavya Rajesh (p. 4) -- From the Experts / by Katherine Bruner (p. 5) -- 3D Printed Drugs: The Future of Pharmaceuticals / by Ethan Wang (p. 6) -- Computerized Markets: Wall Street Takeover / by James Kiraly (p. 10) -- The Evolution of Fear / by Alisha Ahmed (p. 14) -- ADDing Up / by Victor Liaw (p. 18) -- The Clone Wars / by Jina Zhou (p. 22) -- Physician-Assisted Suicide: Drawing the Line / by Haley Wolf (p. 26) -- Supervised Injection Sites / by Alex Gajewski (p. 30) -- On Emerging Medicalization and Health Care / by Patrick Lee (p. 33) -- The Future of Human Gene Modifications / by Elizabeth Robinson (p. 36)College of Natural SciencesUT LibrariesLiberal Art