Predicting Job Performance Using Mobile Sensing

Shayan Mirjafari, Hessam Bagherinezhad, Subigya Nepal, Gonzalo J. Martinez, Koustuv Saha, Mikio Obuchi, Pino G. Audia, Nitesh V. Chawla, Anind K. Dey, Aaron Striegel, Andrew T. Campbell

Research output: Contribution to journalArticlepeer-review


We hypothesize that behavioral patterns of people are reflected in how they interact with their mobile devices and that continuous sensor data passively collected from their phones and wearables can infer their job performance. Specifically, we study day-today job performance (improvement, no change, decline) of N=298 information workers using mobile sensing data and offer data-driven insights into what data patterns may lead to a high-performing day. Through analyzing workers' mobile sensing data, we predict their performance on a handful of job performance questionnaires with an F-1 score of 75%. In addition, through numerical analysis of the model, we get insights into how individuals must change their behavior so that the model predicts improvements in their job performance. For instance, one worker may benefit if they put their phone down and reduce their screen time, while another worker may benefit from getting more sleep.

Original languageEnglish (US)
Pages (from-to)43-51
Number of pages9
JournalIEEE Pervasive Computing
Issue number4
StatePublished - 2021
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computational Theory and Mathematics


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