Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors

Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks understanding of the optimal and minimal window length in data segmentation which would allow for more timely, continuous state detection. This study presents a comparative analysis of ultra-short (30 s or less) window lengths in cognitive load detection with a wearable device. Heart rate, heart rate variability, galvanic skin response, and skin temperature features are extracted at six different window lengths and used to train an Extreme Gradient Boosting classifier to detect between cognitive load and rest. A 25 s window showed the highest accury (67.6%), which is similar to earlier studies using the same dataset. Overall, model accuracy tended to decrease as the window length decreased, and lowest performance (60.0%) was observed with a 5 s window. The contribution of different physiological features to the classification performance and the most useful features that react in short windows are also discussed. The analysis provides a promising basis for future real-time applications with wearable sensors

Feature Use in Mobile Video Creation

Abstract. Today's mobile phones are also video cameras. People are using these ubiquitous cameras to document everyday surroundings as well as create more artistic videos. This paper examines emergent mobile film making patterns by tracking video composition and recording activities in ecologically valid contexts of use. We report the findings of a user study on user created mobile videos, where the actions of 11 active mobile video users were documented for 2 weeks. The collected material included diaries, device logs, and altogether 255 videos. Our findings characterize the features of a typical mobile video. Additionally, our study uncovers common practices, user motivations and pitfalls during filming and editing in the mobile contex

Influence of Personality and Differences in Stress Processing Among Finnish Students on Interest to Use a Mobile Stress Management App : Survey Study

* These authors contributed equally.Background: Excessive stress has a negative impact on many aspects of life for both individuals and societies, from studying and working to health and well-being. Each individual has their unique level of stress-proneness, and positive or negative outcomes of stress may be affected by it. Technology-aided interventions have potential efficacy in the self-management of stress. However, current Web-based or mobile stress management solutions may not reach the individuals that would need them the most, that is, stress-sensitive people. Objective: The aim of this study was to examine how personality is associated with stress among Finnish university students and their interest to use apps that help in managing stress. Methods: We used 2 structured online questionnaires (combined, n=1001) that were advertised in the University of Helsinki's mailing lists. The first questionnaire (n=635) was used to investigate intercorrelations between the Big Five personality variables (neuroticism, extraversion, openness, agreeableness, and conscientiousness) and other stress-related background variables. The second questionnaire (n=366) was used to study intercorrelations between the above-mentioned study variables and interest in using stress management apps. Results: The quantitative findings of the first questionnaire showed that higher levels of extraversion, agreeableness, and conscientiousness were associated with lower self-reported stress. Neuroticism, in turn, was found to be strongly associated with rumination, anxiety, and depression. The findings of the second questionnaire indicated that individuals characterized by the Big Five personality traits of neuroticism and agreeableness were particularly interested to use stress management apps (r=.27, P Conclusions: Our results indicated that personality traits may have an influence on the adoption interest of stress management apps. Individuals with high neuroticism are, according to our results, adaptive in the sense that they are interested in using stress management apps that may benefit them. On the contrary, low agreeableness may lead to lower interest to use the mobile stress management apps. The practical implication is that future mobile stress interventions should meaningfully be adjusted to improve user engagement and support health even among less-motivated users, for instance, to successfully engage individuals with low agreeableness.Peer reviewe

Unobtrusive stress detection on the basis of smartphone usage data

Peer reviewe

VTT Electronics

Sensor-based context recognition for mobile applications VTT PUBLICATIONS 511 Sensor-based context recognition for mobile application