Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design

Izumi, Keisuke; Minato, Kazumichi; Shiga, Kiko; Sugio, Tatsuki; Hanashiro, Sayaka; Cortright, Kelley; Kudo, S.; Fujita, Takanori; Sado, Mitsuhiro; Maéno, Takashi; Takebayashi, Toru; Mimura, Masaru; Kishimoto, Toshifumi

doi:10.3389/fpsyt.2021.611243

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…This study is part of a research project titled “Unobtrusive Sensing Technology for Quantifying Stress and Wellbeing to Promote a Healthy Workplace”. The concept, methodology, and overall goals of the study are described elsewhere [ 37 ]. The main purpose of this study was to examine the relationships among stress, well-being, and biological signals such as HRV, voice, and electrodermal activity from various perspectives.…”

Section: Methodsmentioning

confidence: 99%

Subjective well-being and month-long LF/HF ratio among deskworkers

et al. 2021

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The importance of workers’ well-being has been recognized in recent years. The assessment of well-being has been subjective, and few studies have sought potential biomarkers of well-being to date. This study examined the relationship between well-being and the LF/HF ratio, an index of heart rate variability that reflects sympathetic and parasympathetic nerve activity. Pulse waves were measured using photoplethysmography through a web camera attached to the computer used by each participant. The participants were asked to measure their pulse waves while working for 4 weeks, and well-being was assessed using self-reported measures such as the Satisfaction With Life Scale (SWLS), the Positive and Negative Affect Schedule (PANAS), and the Flourishing Scale (FS). Each of the well-being scores were split into two groups according to the median value, and the LF/HF ratio during work, as well as the number of times an LF/HF ratio threshold was either exceeded or subceeded, were compared between the high and low SWLS, positive emotion, negative emotion, and FS groups. Furthermore, to examine the effects of the LF/HF ratio and demographic characteristics on well-being, a multiple regression analysis was conducted. Data were obtained from 169 participants. The results showed that the low FS group had a higher mean LF/HF ratio during work than the high FS group. No significant differences were seen between the high and low SWLS groups, the high and low positive emotion groups, or the high and low negative emotion groups. The multiple regression analysis showed that the mean LF/HF ratio during work affected the FS and SWLS scores, and the number of times the mean LF/HF ratio exceeded +3 SD had an effect on the positive emotion. No effect of the LF/HF ratio on negative emotions was shown. The LF/HF ratio might be applicable as an objective measure of well-being.

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Section: Methodsmentioning

confidence: 99%

Subjective well-being and month-long LF/HF ratio among deskworkers

et al. 2021

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“…Regarding work-related environments, Kuutila et al [48] used software repositories to predict well-being without collecting audio data. Izumi et al [49] took a multi-modal approach including audio and speech data. In summary, there has been research on automated well-being prediction in work contexts leveraging speech features.…”

Section: Individual Well-being Data Analysismentioning

confidence: 99%

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Zeulner,

Hagerer,

Müller

et al. 2024

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Current methods for assessing individual well-being in team collaboration at the workplace often rely on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal was to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We focus on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to explore which features are predictors of self-reported satisfaction scores. We applied the toolchain to a case study, where we collected videos of 20 teams with 56 participants collaborating over a four-day period in a team project in an educational environment. Our audiovisual speaker diarization extracted individual speech features from a noisy environment. As the dependent variable, team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores were predicted using speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain was able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

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“…Regarding work-related environments, Kuutila et al [37] used software repositories to predict well-being without collecting audio data. Izumi et al [38] took a multi-modal approach including audio and speech data. In summary, there is research on automated well-being prediction in work contexts leveraging speech features.…”

Section: Individual Well-being Data Analysismentioning

confidence: 99%

Predicting Individual Well-Being in Teamwork Contexts Based on Multi-Modal Speech Features

Zeulner,

Hagerer,

Mueller

et al. 2024

Preprint

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Current methods for assessing individual well-being in team collaboration at the workplace rely often on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal is to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We are focusing on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to find which features are predictors of self-reported satisfaction scores. We apply the toolchain to a case study where we collected videos of 22 teams with 56 participants collaborating over a four-day period in a team project. Our audiovisual speaker diarization extracts individual speech features in a noisy environment. As the dependent variable team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores have been predicted with speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain is able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

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Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design

Cited by 8 publications

References 27 publications

Subjective well-being and month-long LF/HF ratio among deskworkers

Subjective well-being and month-long LF/HF ratio among deskworkers

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Predicting Individual Well-Being in Teamwork Contexts Based on Multi-Modal Speech Features

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