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This study investigates accessible and sensitive electrode solutions for detecting touches and squeezes on soft interfaces based on commercially available conductive polyurethane foam. Various electrode materials and configurations are explored, and for electrodes made of conductive threads, the static and dynamic electrical behaviors are studied in depth. In contrast to existing approaches that aim to minimize or stabilize contact resistance, we propose leveraging contact resistance to significantly enhance sensing sensitivity. Suggestions for future researchers and developers when building squeeze sensors based on this material are provided. Our findings offer insights for DIY enthusiasts and researchers, enabling them to develop sensitive soft interfaces for touch and squeeze interactions in an affordable and accessible manner and provide a completely soft user experience.

Abstract Amongst the various characteristics of a speech signal, the expression of emotion is one of the characteristics that exhibits the slowest temporal dynamics. Hence, a performant speech emotion recognition (SER) system requires a predictive model that is capable of learning sufficiently long temporal dependencies in the analysed speech signal. Therefore, in this work, we propose a novel end-to-end neural network architecture based on the concept of dilated causal convolution with context stacking. Firstly, the proposed model consists only of parallelisable layers and is hence suitable for parallel processing, while avoiding the inherent lack of parallelisability occurring with recurrent neural network (RNN) layers. Secondly, the design of a dedicated dilated causal convolution block allows the model to have a receptive field as large as the input sequence length, while maintaining a reasonably low computational cost. Thirdly, by introducing a context stacking structure, the proposed model is capable of exploiting long-term temporal dependencies hence providing an alternative to the use of RNN layers. We evaluate the proposed model in SER regression and classification tasks and provide a comparison with a state-of-the-art end-to-end SER model. Experimental results indicate that the proposed model requires only 1/3 of the number of model parameters used in the state-of-the-art model, while also significantly improving SER performance. Further experiments are reported to understand the impact of using various types of input representations (i.e. raw audio samples vs log mel-spectrograms) and to illustrate the benefits of an end-to-end approach over the use of hand-crafted audio features. Moreover, we show that the proposed model can efficiently learn intermediate embeddings preserving speech emotion information.

BackgroundEmotions and mood are important for overall well-being. Therefore, the search for continuous, effortless emotion prediction methods is an important field of study. Mobile sensing provides a promising tool and can capture one of the most telling signs of emotion: language. ObjectiveThe aim of this study is to examine the separate and combined predictive value of mobile-sensed language data sources for detecting both momentary emotional experience as well as global individual differences in emotional traits and depression. MethodsIn a 2-week experience sampling method study, we collected self-reported emotion ratings and voice recordings 10 times a day, continuous keyboard activity, and trait depression severity. We correlated state and trait emotions and depression and language, distinguishing between speech content (spoken words), speech form (voice acoustics), writing content (written words), and writing form (typing dynamics). We also investigated how well these features predicted state and trait emotions using cross-validation to select features and a hold-out set for validation. ResultsOverall, the reported emotions and mobile-sensed language demonstrated weak correlations. The most significant correlations were found between speech content and state emotions and between speech form and state emotions, ranging up to 0.25. Speech content provided the best predictions for state emotions. None of the trait emotion–language correlations remained significant after correction. Among the emotions studied, valence and happiness displayed the most significant correlations and the highest predictive performance. ConclusionsAlthough using mobile-sensed language as an emotion marker shows some promise, correlations and predictive R2 values are low.

BackgroundGlaucoma, the silent thief of sight, is a major cause of blindness worldwide. It is a burden for people in low-income countries, specifically countries where glaucoma-induced blindness accounts for 15% of the total incidence of blindness. More than half the people living with glaucoma in low-income countries are unaware of the disease until it progresses to an advanced stage, resulting in permanent visual impairment. ObjectiveThis study aims to evaluate the acceptability of the Glaucoma Easy Screener (GES), a low-cost and portable visual field screening platform comprising a smartphone, a stereoscopic virtual reality headset, and a gaming joystick. MethodsA mixed methods study that included 24 eye care professionals from 4 hospitals in Southwest Ethiopia was conducted to evaluate the acceptability of GES. A pre-post design was used to collect perspectives before and after using the GES by using questionnaires and semistructured interviews. A Wilcoxon signed-rank test was used to determine the significance of any change in the scores of the questionnaire items (two-tailed, 95% CI; α=.05). The questionnaire and interview questions were guided by the Unified Theory of Acceptance and Use of Technology. ResultsPositive results were obtained both before and after use, suggesting the acceptance of mobile health solutions for conducting glaucoma screening by using a low-cost headset with a smartphone and a game controller. There was a significant increase (two-tailed, 95% CI; α=.05) in the average scores of 86% (19/22) of postuse questionnaire items compared with those of preuse questionnaire items. Ophthalmic professionals perceived GES as easy to use and as a tool that enabled the conduct of glaucoma screening tests, especially during outreach to rural areas. However, positive evaluations are contingent on the accuracy of the tool. Moreover, ophthalmologists voiced the need to limit the tool to screening only (ie, not for making diagnoses). ConclusionsThis study supports the feasibility of using a mobile device in combination with a low-cost virtual reality headset and classic controller for glaucoma screening in rural areas. GES has the potential to reduce the burden of irreversible blindness caused by glaucoma. However, further assessment of its sensitivity and specificity is required.

INTRODUCTION: Low income countries bear 90% of the burden of avoidable blindness. In particular, glaucoma is one of the major causes of visual impairment. Early detection is critical to avoid irreversible blindness due to this underdiagnoseddisease. Mobile and connected technologies yield potential to support early detection. However, to realize this potential, they should be designed from a deep understanding of the needs of the local population, and the challenges unique to lowincome countries.OBJECTIVES: To gain a contextualized understanding of eye health care needs, and the challenges and opportunities for designing mobile health solutions for screening of glaucoma.METHODS: We conducted a qualitative user study at three different healthcare centres in Southwest Ethiopia, including semi-structured interviews with eight eye care health professionals and observations and informal interviews with 125 patients, at three different locations. Interview transcripts were analysed through inductive thematic analysis involving three iterations.RESULTS: Seven themes were initially identified after two iterations. The themes provided insight into the organization of eye health care and highlighted the challenges faced by eye care professionals and patients. An additional of three themes were identified in the third iteration which were formulated as paradoxes (“Money is not an issue, yet there is no money for glaucoma”, “Shortage of eye health care, yet the work is repeated over and over”, “The willing but uncooperative patient tested by the willing but unexperienced nurse”).CONCLUSION: Findings from this research bear the potential to inspire future designs of mobile health solutions targeting rural communities in Ethiopia and other LICs.