Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters

Reimer, Ulrich; Laurenzi, Emanuele; Maier, Edith; Ulmer, Tom Florian

doi:10.24251/hicss.2017.435

Cited by 13 publications

(5 citation statements)

References 45 publications

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“…Notably, Sano et al introduce an inventive stress recognition scheme that seamlessly combines wearables and smartphones to conduct a comprehensive stress analysis [59]. Similarly, Reimer et al contribute a stress recognition pilot system that merges physiological signals such as HRV with contextual information through wearables [60]. This innovative approach enables a nuanced assessment of stress levels during smartphone interactions.…”

Section: Discussionmentioning

confidence: 99%

Accessibility first: detecting frustration in web browsing for visually impaired and sighted smartphone users

Abdelaal,

Al-Thani

2023

Univ Access Inf Soc

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Smartphone adoption has become increasingly prevalent in modern society, reflecting the widespread integration of these devices into various aspects of daily life, yet accessibility and usability problems persist. Smartphones pose numerous challenges to individuals with visual impairments (VI). This is due to smaller screen sizes, lack of physical buttons, and prevalent problems. These challenges frustrate users as they are unable to accomplish their goals. In this paper, the physiological response of VI and sighted individuals are compared and examined. The participant's frustration is measured through physiological signals. The GSR is the signal captured to detect the user's frustration caused by the superimposed accessibility and usability web-related issues. GSR, being less obtrusive, is an effective way to measure frustration. There were 13 VI and 16 sighted participants. Using a within-subject and between-subject design, participants completed four tasks, each was completed under frustrating and non-frustrating conditions while wearing an Empatica E4 wristband to collect GSR data. Challenges were both group-specific and mutual. A slowed internet connection and constant page refreshes are frustrating tasks for sighted participants. VI participants faced an unsearchable drop-down list and an inaccessible menu. Pop-up ads and session timeout are common tasks. Among VI participants, there was no significant difference between frustrating and non-frustrating tasks. When VI participants experienced a session timeout, they significantly showed a higher level of arousal. When comparing the physiological responses of sighted and VI participants in the page refresh and session timeout tasks, the results show that the arousal level is significantly higher in VI participants, primarily due to accessibility issues.

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Section: Discussionmentioning

confidence: 99%

Accessibility first: detecting frustration in web browsing for visually impaired and sighted smartphone users

Abdelaal,

Al-Thani

2023

Univ Access Inf Soc

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“…Stress detection, by means of classifying these physiological responses into levels of stress via machine learning, continues to evolve and is motivated by the potential utility of continuously monitoring stress levels in real-time [12,21]. Stress detection systems have been developed for drivers in semi-urban scenarios [22,23], patients undergoing virtual reality therapy [24], individuals in working environments [25], and people that need help managing daily stress [21,[26][27][28][29][30]. Stress detection can also be applied to a variety of humanmachine interfaces (HMIs) which may monitor stress, but also infer the cognitive state of the user to adapt system functionality [31].…”

Section: B Stress Detectionmentioning

confidence: 99%

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

et al. 2023

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When training for hazardous operations, real-time stress detection is an asset for optimizing task performance and reducing stress. Stress detection systems train a machine-learning model with physiological signals to classify stress levels of unseen data. Unfortunately, individual differences and the time-series nature of physiological signals limit the effectiveness of generalized models and hinder both posthoc stress detection and real-time monitoring. This study evaluated a personalized stress detection system that selects a personalized subset of features for model training. The system was evaluated post-hoc for realtime deployment. Further, traditional classifiers were assessed for error caused by indirect approximations against a benchmark, optimal probability classifier (Approximate Bayes; ABayes). Healthy participants completed a task with three levels of stressors (low, medium, high), either a complex task in virtual reality (responding to spaceflight emergency fires, n=27) or a simple laboratory-based task (N-back, n=14). Heart rate, blood pressure, electrodermal activity, and respiration were assessed. Personalized features and different interval window sizes were compared. Classification performance was compared for ABayes, support vector machine, decision tree, and random forest. The results demonstrate that a personalized model with time series intervals can classify three stress levels with high accuracy better than a generalized model. However, crossvalidation and holdout performance varied for traditional classifiers vs. ABayes, suggesting error from indirect approximations. The selected features changed with windows size and differed between tasks, but blood pressure contributed to the most prominent features. The capability to account for individual difference is a prominent advantage of personalized models, and will likely have a growing presence in future detection systems.

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“…Smets et al (2018) propose the use of automatic stress detection data in combination with stress control interventions. Reimer et al (2017) consider the use of these devices relevant for the treatment of craving in patients with chemical dependency. However, as far as we could verify, we did not find any studies applying such proposals and investigating the complete cycle from stress detection, during continuous monitoring, to intervention proposals, which is a path for our research (Can et al, 2019).…”

Section: Notementioning

confidence: 99%

Dispositivos Vestíveis para monitoramento psicofisiológico do estresse: uma revisão narrativa

Rodrigues¹,

Armiliato²,

Gonçalves³

et al. 2022

RBTC

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Wearable devices use sensors that continuously capture physiological signals and, once processed, allow the monitoring and development of interventions in various areas of health, including mental disorders. In Clinical Psychology, this type of technology can cooperate to the objective and continuous measurement of stress, as well as to generate feedback when stressful situations occur. This narrative literature review focused on these devices, presenting the main scientific data available, as well as opportunities and difficulties in implementing these devices in stress assessment and health treatments. The reviewed research indicated that it is necessary to develop more robust and theoretically based systems that integrate physiological, subjective and contextual responses to implement this type of wearable in clinical contexts. However, the accuracy already demonstrated by wearable sensors in laboratory situations and some continuous monitoring tests, reinforce that these are tools with great potential for application in clinical psychological practice.

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Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters

Cited by 13 publications

References 45 publications

Accessibility first: detecting frustration in web browsing for visually impaired and sighted smartphone users

Accessibility first: detecting frustration in web browsing for visually impaired and sighted smartphone users

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

Dispositivos Vestíveis para monitoramento psicofisiológico do estresse: uma revisão narrativa

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