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

Abstract

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.

Different Methods to Detect Stress in Knowledge Workers: A Pilot Study for Long-term Monitoring

“…Increases in mouse usage events were also associated with longer working hours and greater perceived productivity at work. However, as discussed in [7], mouse usage activity is not a very comprehensive indicator of productivity at work, because not all knowledge workersʼ tasks involve mouse usage.…”

“…Around 50% of all lost working days may have links to work stress [6]. On the other hand, eustress, characterized by excitement about work, can promote productivity [7,8]. Knowledge work has shifted more and more from the office to work at home.…”

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

“…A recent study targeting the prediction of office worker productivity emphasized the significance of incorporating psychological states into such models to enhance their predictive accuracy. This research specifically spotlighted mood and eustress as pivotal indicators of productivity [ 17 ]. Recognizing the impact of these four stress appraisal conditions—boredom, eustress, the coexistence of eustress and distress, and pure distress—on mood and performance enables employers to craft strategies that foster positive work environments.…”