“You Feel Like You Kind of Walk Between the Two Worlds”: A Participatory Study Exploring How Technology Can Support Emotion Regulation for Autistic People

An increasing amount of technological solutions aiming to support emotion regulation are being developed for Autistic people. However, there remains a lack of understanding of user needs, and design factors which has led to poor usability and varied success. Furthermore, studies assessing the feasibility of emotion regulation technology via physiological signals for autistic people are increasingly showing promise, yet to date there has been no exploration of views from the autistic community on the benefits/challenges such technology may present in practice. Focus groups with autistic people and their allies were conducted to gain insight into experiences and expectations of technological supports aimed at supporting emotion regulation. Reflexive thematic analysis generated three themes: (1) communication challenges (2) views on emotion regulation technology (3) ‘how’ technology is implemented. Results provide meaningful insight into the socio-emotional communication challenges faced by autistic people, and explore the expectations of technology aimed at supporting emotion regulation.

A New Method for Detecting Sleep Apnea

Sleep apnea is a sleep disorder that can induce hypertension, coronary heart disease, stroke and other diseases, so the detection of sleep apnea is clinically important for the prevention of these diseases. In order to improve the detection performance and verify which physiological signals are better for sleep apnea detection, this paper uses multi-channel signal superposition and channel summation to improve the content of valid information in the original signal. Thirty features are analyzed by Relief feature selection algorithm. Finally, 15 features were used to build a classification model and support vector machine (SVM) was used for classification. The experimental results showed that the highest accuracy of 96.24% was achieved when electrocardiogram (X2) and electroencephalogram (C3-A2) channels were used for channel summation.

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.