Challenges of designing and developing tangible interfaces for mental well-being

Mental well-being technologies possess many qualities that give them the potential to help people receive assessment and treatment who may otherwise not receive help due to fear of stigma or lack of resources. The combination of advances in sensors, microcontrollers and machine learning is leading to the emergence of dedicated tangible interfaces to monitor and promote positive mental well-being. However, there are key technical, ergonomic and aesthetic challenges to be overcome in order to make these interfaces effective and respond to users’ needs. In this paper, the barriers to develop mental well-being tangible interfaces are discussed by identifying and examining the recent technological challenges machine learning, sensors, microcontrollers and batteries create. User-oriented challenges that face the development of mental well-being technologies are then considered ranging from user engagement during co-design and trials to ethical and privacy concern

Treating Emotional Distress through the use of Emotion and Cognitive-Based Therapies

This article will present an integrated approach for treating emotional distress. The authors review the purposes of emotions and explore how they operate in individuals’ lives based on learned responses and inaccurate perceptions. Distinct categories of emotions are identified, including both maladaptive and adaptive forms. Basic ideologies and negative evaluations will also be reviewed to illustrate how these patterns develop and maintain disturbing conditions. The authors will examine the complimentary association between affective and cognitive material and how treating both in therapy can be beneficial. Emotion and cognitive-based interventions will be presented through the use of a case study

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

Detección de estados de ánimo mediante sentiment analysis en hispanohablantes

Esta investigación muestra la elaboración y validación de un modelo computacional diseñado para detectar los estados de ánimo, definidos en la teoría de Christophe André (2010), en hispanohablantes a partir de respuestas que ingresaron estos usuarios en formato de texto. Se emplea la Técnica de Reconocimiento de Palabras Clave –en inglés, Keyword Spotting Technique (KST) – para el tratamiento del texto, y la técnica de Análisis de Sentimiento –en inglés, Sentiment Analysis– basado en solo los conceptos del Procesamiento de Lenguaje Natural –en inglés, Natural Language Processing (NLP)– para el análisis textual. Se utilizó para la recopilación de conversaciones una aplicación móvil con interfaz de chatbot, y un bot que invita al usuario a dar detalles sobre su estado de ánimo a través de preguntas validadas por un experto. Como resultado de la prueba piloto de 30 conversaciones, y luego de la prueba final en la muestra de 49 conversaciones, se obtuvo una correcta clasificación del 70% de los casos, así como una propuesta para el tratamiento de textos en idioma español.The following research shows the elaboration and validation of a computational model designed to detect Spanish speakers’ moods from answers entered by these users in text format. It was used the Keyword Spotting Technique (KST) for text treatment, and Sentiment Analysis based on only Natural Language Processing (NLP) concepts for text analysis. A mobile application with chatbot interface and a bot that invites the user to give details about their mood through questions validated by an expert, are the tools used to collect all the conversations. As a result of the pilot test of 30 conversations, and after the final test in the sample of 49 conversations, there is a correct classification of 70% of the cases, as well as a proposal for the treatment of texts in Spanish

Full Issue

Volume 4, Number 1 (2019

Self-Monitoring of Emotions and Mood Using a Tangible Approach

Nowadays Personal Informatics (PI) devices are used for sensing and saving personal data, everywhere and at any time, helping people improve their lives by highlighting areas of good and bad performances and providing a general awareness of different levels of conduct. However, not all these data are suitable to be automatically collected. This is especially true for emotions and mood. Moreover, users without experience in self-tracking may have a misperception of PI applications’ limits and potentialities. We believe that current PI tools are not designed with enough understanding of such users’ needs, desires, and problems they may encounter in their everyday lives. We designed and prototype the Mood TUI (Tangible User Interface), a PI tool that supports the self-reporting of mood data using a tangible interface. The platform is able to gather six different mood states and it was tested through several participatory design sessions in a secondary/high school. The solution proposed allows gathering mood values in an amusing, simple, and appealing way. Users appreciated the prototypes, suggesting several possible improvements as well as ideas on how to use the prototype in similar or totally different contexts, and giving us hints for future research