iFidgetCube: Tangible Fidgeting Interfaces (TFIs) to Monitor and Improve Mental Wellbeing

• Published in: IEEE sensors journal Vol. 21; no. 13; pp. 14300 - 14307
• Main Authors: Woodward, Kieran, Kanjo, Eiman
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Biomedical monitoring; Classifiers; Data models; Deep learning; emotion recognition; fidgeting; Hand tools; Heart rate; Heart rate variability; Mental wellbeing; physiological sensors; Sensors; Stress; subject-independent; tangible user interfaces
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.3031163

The ability to unobtrusively measure mental wellbeing states using non-invasive sensors has the potential to greatly improve mental wellbeing by alleviating the effects of high stress levels. Multiple sensors, such as electrodermal activity, heart rate and accelerometers, embedded within tangible devices pave the way to continuously and non-invasively monitor wellbeing in real-world environments. On the other hand, fidgeting tools enable repetitive interaction methods that may help to tap into individual's psychological need to feel occupied and engaged; hence potentially reducing stress. In this article, we present the design, implementation, and deployment of Tangible Fidgeting Interfaces (TFIs) in the form of computerised iFidgetCubes. iFidgetCubes embed non-invasive sensors along with fidgeting mechanisms to aid relaxation and ease restlessness. We take advantage of our labeling techniques at the point of collection to implement multiple subject-independent deep learning classifiers to infer wellbeing. The obtained performance demonstrates that these new forms of tangible interfaces combined with deep learning classifiers have the potential to accurately infer wellbeing in addition to providing fidgeting tools.