Towards affective computing that works for everyone

Missing diversity, equity, and inclusion elements in affective computing datasets directly affect the accuracy and fairness of emotion recognition algorithms across different groups. A literature review reveals how affective computing systems may work differently for different groups due to, for instance, mental health conditions impacting facial expressions and speech or age-related changes in facial appearance and health. Our work analyzes existing affective computing datasets and highlights a disconcerting lack of diversity in current affective computing datasets regarding race, sex/gender, age, and (mental) health representation. By emphasizing the need for more inclusive sampling strategies and standardized documentation of demographic factors in datasets, this paper provides recommendations and calls for greater attention to inclusivity and consideration of societal consequences in affective computing research to promote ethical and accurate outcomes in this emerging field.Comment: 8 pages, 2023 11th International Conference on Affective Computing and Intelligent Interaction (ACII

A Biosymtic (Biosymbiotic Robotic) Approach to Human Development and Evolution. The Echo of the Universe.

In the present work we demonstrate that the current Child-Computer Interaction paradigm is not potentiating human development to its fullest – it is associated with several physical and mental health problems and appears not to be maximizing children’s cognitive performance and cognitive development. In order to potentiate children’s physical and mental health (including cognitive performance and cognitive development) we have developed a new approach to human development and evolution. This approach proposes a particular synergy between the developing human body, computing machines and natural environments. It emphasizes that children should be encouraged to interact with challenging physical environments offering multiple possibilities for sensory stimulation and increasing physical and mental stress to the organism. We created and tested a new set of computing devices in order to operationalize our approach – Biosymtic (Biosymbiotic Robotic) devices: “Albert” and “Cratus”. In two initial studies we were able to observe that the main goal of our approach is being achieved. We observed that, interaction with the Biosymtic device “Albert”, in a natural environment, managed to trigger a different neurophysiological response (increases in sustained attention levels) and tended to optimize episodic memory performance in children, compared to interaction with a sedentary screen-based computing device, in an artificially controlled environment (indoors) - thus a promising solution to promote cognitive performance/development; and that interaction with the Biosymtic device “Cratus”, in a natural environment, instilled vigorous physical activity levels in children - thus a promising solution to promote physical and mental health

Network properties of Informal Support Networks in Mental Health

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2023, Tutora: Franziska PeterWe analyze a dataset of citizens’ relations to a set of situations related to mental health. The dataset was collected by a digital tool created in the framework of Citizen Social Science. We generate two networks from it and compute different metrics to study them. In particular, we answer two particular questions: Do the experiences of young people in mental health differ from those of old people? and Are young people and old people in two different bubbles, i.e. are the mental health experiences they know from their respective surroundings different from each other? Their answers were determined by computing Newman’s assortativity coefficient but generalized to weighted networks

Mental health inpatient care: how should services be organised in a NHS?

Research mastersOrganisation of mental health care provided by hospitals can be done with concentration of services in a few units or with several hospitals providing them. The trade-o↵ to be made is between being closer to patients having several units of low volume activity each or benefiting from economies of scale to obtain better outcomes. We address here the magnitude of the scale e↵ects in mental health care. This provides important information to address the above-mentioned trade-o↵. We also analyse the importance of integrated continuous care services in mental health as a complement to inpatient care by computing the potential savings to the National Health Service (NHS). These services are a set of sequential interventions in mental health and/or social support, focusing on rehabilitation and recovery of patients with psychosocial disability. Analysing both economies of scale and integrated continuous care are relevant issues for mental health system financing. We use a diagnosis related group (DRG) dataset from 2001 to 2013 considering only mental health inpatient discharges, from an European country with a case-mix based funding system (Portugal). Using a conditional risk set model, we find a scale e↵ect for each DRG that ranges between 0 and 1 day. The magnitude of the scale e↵ect is not sufficiently high to justify the centralisation of psychiatric services in higher volume hospitals. We find potential savings for the NHS if integrated continuous care was in place. The focus of mental health system redesign should be on promoting integrated mental health care, with concentration of hospital services not being particularly relevant

“Sorry I Didn’t Hear You.” The Ethics of Voice Computing and AI in High Risk Mental Health Populations

This article examines the ethical and policy implications of using voice computing and artificial intelligence to screen for mental health conditions in low income and minority populations. Mental health is unequally distributed among these groups, which is further exacerbated by increased barriers to psychiatric care. Advancements in voice computing and artificial intelligence promise increased screening and more sensitive diagnostic assessments. Machine learning algorithms have the capacity to identify vocal features that can screen those with depression. However, in order to screen for mental health pathology, computer algorithms must first be able to account for the fundamental differences in vocal characteristics between low income minorities and those who are not. While researchers have envisioned this technology as a beneficent tool, this technology could be repurposed to scale up discrimination or exploitation. Studies on the use of big data and predictive analytics demonstrate that low income minority populations already face significant discrimination. This article urges researchers developing AI tools for vulnerable populations to consider the full ethical, legal, and social impact of their work. Without a national, coherent framework of legal regulations and ethical guidelines to protect vulnerable populations, it will be difficult to limit AI applications to solely beneficial uses. Without such protections, vulnerable populations will rightfully be wary of participating in such studies which also will negatively impact the robustness of such tools. Thus, for research involving AI tools like voice computing, it is in the research community\u27s interest to demand more guidance and regulatory oversight from the federal government

Chapter 13 The value of the imagined biological in policy and society

Attending the World Economic Forum this past week, I was struck by two trends. The first was that brain research has emerged as a hot topic. Not only was brain science or brain health a new theme at the meeting, research on the brain emerged in discussions about next generation computing, global cooperation, and even models of economic development as well as being linked to mental health or mindfulness. In a meeting frequented largely by economists and business leaders, I was surprised by the number of non-scientists who have become enchanted by brain science. Clearly this is the era of the brain, with mental health now part of a much broader discussion

Technology and College Student Mental Health: Challenges and Opportunities

In recent years, there has been an increase in symptoms of depression, anxiety, eating disorders, and other mental illnesses in college student populations. Simultaneously, there has been a steady rise in the demand for counseling services. These trends have been viewed by some as a mental health crisis requiring prompt investigation and the generation of potential solutions to serve the needs of students. Subsequently, several studies linked the observed rise in symptoms with the ubiquitous rise in use of personal computing technologies, including social media, and have suggested that time spent on these types of technologies is directly correlated with poor mental health. While use of personal computing technologies has dramatically shifted the landscape in which college students connect with one another and appears to have some detriments to mental health, the same technologies also offer a number of opportunities for the enhancement of mental health and the treatment of mental illness. Here, we describe the challenges and opportunities for college student mental health afforded by personal computing technologies. We highlight opportunities for new research in this area and possibilities for individuals and organizations to engage with these technologies in a more helpful and wellness-promoting manner

Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine" RTI2018-101674-B-I0