TinyDigitalExposome: the opportunities of multimodal urban environmental data and mental wellbeing on constrained microcontrollers

The increasing level of air pollutants (e.g. particulates, noise and gases) within the atmosphere are impacting mental wellbeing. In this poster, we define the term 'TinyDigitalExposome' that takes us closer towards understanding the opportunity between environment and wellbeing using constrained microcontrollers. Specifically, we propose the opportunity of collecting particulate matter to infer mental wellbeing states whilst can have in the real-world

Emotion on the edge: air quality sensors decoded as a real-world emotion indicator

As the research community increasingly focuses on quantifying emotional states in real-world scenarios, there is a growing need for edge computing. In this work, we present a novel approach to on-device emotion classification through the development of a low-cost hand-held device. This device incorporates a range of environmental air quality factors, including Particulate Matter, Nitrogen Dioxide, Carbon Monoxide, Ammonia, and Noise. Our research addresses the current limitations in the field of emotional state measurement by leveraging environmental air quality data, which has been previously linked to affective states. This on-device approach not only offers an alternative to resource-intensive emotion recognition methods but also contributes to the development of more practical and affordable solutions for emotion assessment. The preliminary results of our device's performance in real-world scenarios suggest its effectiveness in quantifying emotional states through air quality factors, with the model achieving 95% accuracy demonstrating accurate on-device classification without the need for external high-processing power

Beyond mobile apps: a survey of technologies for mental well-being

Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time

NeuroPlace: categorizing urban places according to mental states

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture