arXiv subjects

Takayuki Nagaya

Publications and source records attributed to Takayuki Nagaya.

2 recordsLinked to original sources

arXiv2026

The Well-Being Palette: An Action-Word Selection Tool Designed for Low-Burden Reflection on Workplace Well-Being

Background: Workplace well-being interventions need formats that can be used repeatedly with minimal disruption to daily work. We developed the Well-Being Palette, a web-based action-word selection tool designed for brief, low-burden reflection on workplace well-being. Methods: In a three-month exploratory field study at a private-sector corporate research institute in Japan, 88 analyzed participants selected up to three well-being-related action words after reflecting on positive actions or experiences from each workday. We examined application usage, PERMA Profiler scores, selected-word patterns across departments, selected-word diversity using Shannon entropy, and exploratory associations with sharing workshops. Results: During the formal intervention period, the application captured 3,480 input records and 10,104 selected words, and all 72 available action words were selected. Overall PERMA scores increased from baseline to post-intervention, and no clear decline was observed at the one-month follow-up among available cases. Application logs revealed departmental differences in selected-word categories. Cumulative selected-word diversity increased over time, and sharing workshops showed exploratory associations with more sustained PERMA patterns and broader cumulative selected-word diversity. Conclusion: The Well-Being Palette was feasible for repeated use in a real workplace and provided complementary log-based information on how workers recognized and labeled well-being-related experiences. The findings should be interpreted as exploratory and hypothesis-generating, because the study did not include a randomized control condition and did not directly measure perceived burden or completion time.

cs.HC↗

arXiv2025

Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation

Estimating emotional states from physiological signals is a central topic in affective computing and psychophysiology. While many emotion estimation systems implicitly assume a stable relationship between physiological features and subjective affect, this assumption has rarely been tested over long timeframes. This study investigates whether such relationships remain consistent across several months within individuals. We developed a custom measurement system and constructed a longitudinal dataset by collecting physiological signals -- including blood volume pulse, electrodermal activity (EDA), skin temperature, and acceleration--along with self-reported emotional states from 24 participants over two three-month periods. Data were collected in naturalistic working environments, allowing analysis of the relationship between physiological features and subjective arousal in everyday contexts. We examined how physiological-arousal relationships evolve over time by using Explainable Boosting Machines (EBMs) to ensure model interpretability. A model trained on 1st-period data showed a 5\% decrease in accuracy when tested on 2nd-period data, indicating long-term variability in physiological-arousal associations. EBM-based comparisons further revealed that while heart rate remained a relatively stable predictor, minimum EDA exhibited substantial individual-level fluctuations between periods. While the number of participants is limited, these findings highlight the need to account for temporal variability in physiological-arousal relationships and suggest that emotion estimation models should be periodically updated -- e.g., every five months -- based on observed shift trends to maintain robust performance over time.

cs.HC↗