Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students’ Mental Health

Kim, Tae‐Wan; Kim, Haesoo; Lee, Ha Yeon; Goh, Hwarang; Abdigapporov, Shakhboz; Jeong, Mingon; Cho, Hyunsung; Han, Kyungsik; Noh, Youngmin; Lee, Sung-Ju; Hong, Hwajung

doi:10.1145/3491102.3517701

Cited by 15 publications

(19 citation statements)

References 43 publications

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“…Some studies have been conducted using biometric information that can be acquired by wearable devices to detect stress [ 19 , 22 , 23 , 25 , 39 , 40 ]. The accuracy of the results varied from 50 to 90% [ 18 , 57 , 58 ] due to differences in environmental factors and the impact of different datasets. These methods sometimes result in a high-burden system for the elderly because they do not take into account the characteristics of the elderly, who are not accustomed to digital devices and tend to be strict about privacy.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly

Matsumoto

Matsui

Suwa

et al. 2023

Sensors

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It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific activities (e.g., sleep duration, exercise duration/amount, etc.) as explanatory variables and do not consider all daily living activities. It is necessary to link various daily living activities and biometric information in order to estimate the stress state more accurately. Specifically, we construct a stress estimation model using machine learning with the answers to a stress status questionnaire obtained every morning and evening as the ground truth and the biometric data during each of the performed activities and the new proposed indicator including biological and activity perspectives as the features. We used the following methods: Baseline Method 1, in which the RRI variance and Lorenz plot area for 4 h after waking and 24 h before the questionnaire were used as features; Baseline Method 2, in which sleep time was added as a feature to Baseline Method 1; the proposed method, in which the Lorenz plot area per activity and total time per activity were added. We compared the results with the proposed method, which added the new indicators as the features. The results of the evaluation experiments using the one-month data collected from five elderly households showed that the proposed method had an average estimation accuracy of 59%, 7% better than Baseline Method 1 (52%) and 4% better than Baseline Method 2 (55%).

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Section: Discussion and Limitationsmentioning

confidence: 99%

Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly

Matsumoto

Matsui

Suwa

et al. 2023

Sensors

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“…Therefore, we further explore the relationship between stress and other emotions. Considering the limited amount of data sequences for some stress levels in the VAD space (Figure 10), we adopt the k-means clustering [74] method and divide all stress levels into three classes: 1) low stress level (including Levels 0, 1, 2, and 3); 2) middle stress level (including Levels 4, 5, 6); and 3) high stress level (including Levels 7,8,9,10). We summarize the occurrence probability of each emotional state in Table 4 and generate the top-5 word cloud for each stress class (as shown in Figure 11).…”

Section: Analyzing Stress Levels In the 3d Vad Emotional Spacementioning

confidence: 99%

“…[6] Stress plays a vital role in the development of unwanted behaviors and detecting stress at an early stage can help prevent aggression. [7] Inferring users' emotional states [8] is a long-standing problem in humancomputer interaction (HCI) community as it involves the UX design, [9,10] tracking methods, [11] security concerns, [12] and so on. To track the emotional stress changes, some previous research works have been proposed based on facial tracking [11] and touch sensing.…”

Section: Introductionmentioning

confidence: 99%

EmoSense: Revealing True Emotions Through Microgestures

Fang¹,

Xing

Long

et al. 2023

Advanced Intelligent Systems

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Stress is a universally ubiquitous emotional state that takes place everywhere and microgestures (MGs) have been verified to indicate more accurate hidden emotions. However, only limited studies attempted to explore how MGs could reflect stress levels. Herein, EmoSense, an emerging technology for wearable systems containing a three‐layer stress detection mechanism, is proposed: 1) converting the MGs into digital signals; 2) training a machine learning‐based MG detection model; and 3) configuring the stress level based on the MG frequency. To detect the MGs, the swept frequency capacitive sensing technology to is adopted capture the MG signals and the random forest model to detect the MGs effectively is applied. 16 participants are recruited in the pilot study to verify the correlation between stress level and MG frequency. The experimental results further verify that stress level is highly related to other negative emotions that should be studied while handling high stress levels.

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“…However, very little work explores empowering AI-based JITAI with a human-in-the-loop setup [36,49]. There is no prior work leveraging AI-based JITAI in the realm of smartphone overuse, not to mention the human-in-the-loop setup.…”

Section: Introductionmentioning

confidence: 99%

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention

Orzikulova,

Xiao,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time. We conducted an 8-week field experiment (N=71) to evaluate the effectiveness of both the adaptation and explanation aspects of Time2Stop. Our results indicate that our adaptive models significantly outperform the baseline methods on intervention accuracy (>32.8% relatively) and receptivity (>8.0%). In addition, incorporating explanations further enhances the effectiveness by 53.8% and 11.4% on accuracy and receptivity, respectively. Moreover, Time2Stop significantly reduces overuse, decreasing app visit frequency by 7.0∼8.9%. Our subjective data also echoed these quantitative measures. Participants preferred the adaptive interventions and rated the system highly on intervention

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Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students’ Mental Health

Cited by 15 publications

References 43 publications

Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly

Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly

EmoSense: Revealing True Emotions Through Microgestures

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention

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