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Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model\u2019s R2 of 0.48 and MAE of 16.62. The extended model\u2019s feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers.<\/jats:p>","DOI":"10.3390\/s23218694","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T06:19:58Z","timestamp":1698214798000},"page":"8694","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Office Workers\u2019 Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1302-7988","authenticated-orcid":false,"given":"Mohamad","family":"Awada","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089, USA"}]},{"given":"Burcin","family":"Becerik-Gerber","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089, USA"}]},{"given":"Gale","family":"Lucas","sequence":"additional","affiliation":[{"name":"USC Institute for Creative Technologies, University of Southern California, Los Angeles, CA 90089, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4202-396X","authenticated-orcid":false,"given":"Shawn C.","family":"Roll","sequence":"additional","affiliation":[{"name":"Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"ref_1","unstructured":"U.S. Bureau of Labor Statistics (2023, August 20). 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