Social enterprises are organisations, combining profit making with support of their employees and environment. Employee stress is harmful for both society (due to increased risk of mental health disorders and cardiovascular diseases) and for enterprise performance (due to increased risk of presenteeism, employee turnover and early retirement). This work aims at helping enterprises to improve employee wellbeing. To this end, we introduce a concept of IoT-based privacy-aware “team barometer” and present a first study into using inexpensive PIR (passive infrared) motion detection sensors in such barometers. The study was conducted as follows: first, we deployed IoT system in real offices and collected employee data in the course of everyday work during several months. Second, we developed a machine learning method to classify human conditions on the basis of collected PIR data. In the tests, this method recognised employees’ stress with 80% accuracy and dissatisfaction with indoor environmental quality - with 75% accuracy. Third, we integrated stress detection results into a “team barometer” and conducted interviews of line managers. Interview results suggest that the proposed IoT-based team barometer can be beneficial for both employees and enterprises because of its potential to discover and mitigate workplace problems notably faster than with current practice to use periodic surveys.