Ten questions concerning the impact of environmental stress on office workers

Awada, Mohamad; Becerik-Gerber, Burçin; Liu, Ruying; Seyedrezaei, Mirmahdi; Lu, Zheng; Xenakis, Matheos; Lucas, Gale M.; Roll, Shawn C.; Narayanan, Shrikanth

doi:10.1016/j.buildenv.2022.109964

Cited by 24 publications

(2 citation statements)

References 134 publications

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“…For instance, integrating a smart workstation with productivity prediction features into office systems enables real-time monitoring and response to workers' emotional and physiological states. Through intelligent lighting, temperature control, and ambient music, the office environment can be optimized to promote positive mood states and high levels of eustress, thereby enhancing productivity [50]. Also, the proposed model allows for targeted interventions and personalized approaches to productivity enhancement.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, wearable devices allow for unobtrusive data collection without altering participants' natural work routines, mitigating concerns about participant awareness and potential bias [50]. Lastly, the discreet nature of wearable devices somewhat addresses privacy concerns associated with camera-based systems or tracking applications, ensuring participant comfort and the representation of natural work behaviors.…”

Section: Comparison Between Different Modalitiesmentioning

confidence: 99%

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Predicting Office Workers’ Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

Awada,

Becerik-Gerber,

Lucas

et al. 2023

Sensors

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This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. 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’s R2 of 0.48 and MAE of 16.62. The extended model’s 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.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison Between Different Modalitiesmentioning

confidence: 99%