Sensing Personality to Predict Job Performance


The proliferation of sensors allows for continuous capturing an individual’s physical, social, and environmental contexts. We apply machine learning to sensor-collected data to analyze and predict personality, a factor known to influence job performance. Based on our work in Tesserae project, an ongoing study of 757 workers in multi-companies, we present the initial results for passively assessing the worker personality and performance. Our work opens the way of how pervasive technologies track performance in work environment.


Recommended citation:

  title={Sensing personality to predict job performance},
  author={Lin, Suwen and Mattingly, Stephen M},
  booktitle={Proceedings of the Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, CHI EA},