{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:46:15Z","timestamp":1760060775819,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Indoor environmental factors such as CO2 concentration, temperature, and humidity can significantly influence individuals\u2019 emotional states and productivity. This study continuously collected environmental data using wireless sensors and emotional data from wearable devices in an office-like remote-work setting. Machine learning models, including Random Forest and Gradient Boosting Decision Tree, were developed and interpreted using SHAP (Shapley Additive Explanations). The proposed models achieved estimation accuracies above 85%. SHAP analysis revealed that CO2 concentration, temperature, and humidity were influential factors in predicting pleasant or unpleasant states. These findings demonstrate the feasibility of real-time, data-driven emotion estimation and provide insights into the design of indoor environments that foster comfort and mental well-being.<\/jats:p>","DOI":"10.3390\/bdcc9100243","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T14:32:53Z","timestamp":1758637973000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Emotion Estimation in Indoor Remote Work Environments via Environmental Sensor Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Yuma","family":"Toriyama","sequence":"first","affiliation":[{"name":"Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan"}]},{"given":"Tsumugi","family":"Isogami","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3312-454X","authenticated-orcid":false,"given":"Nobuyoshi","family":"Komuro","sequence":"additional","affiliation":[{"name":"Digital Transformation Enhancement Council, Chiba University, Chiba 263-8522, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.buildenv.2023.110985","article-title":"Association between perceived indoor environmental characteristics and occupants\u2019 mental well-being, cognitive performance, productivity, satisfaction in workplaces: A systematic review","volume":"246","author":"Zhang","year":"2023","journal-title":"Build. 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