Stress is the second most frequent work-related health problem in Europe, and it is important to assess stress risks relatively early to diminish negative consequences. Automatic detection of everyday stress is a challenging problem, however, as both stress perception and stress manifestation notably vary between individuals. Therefore stress detectors, trained on data of each person separately, usually achieve notably higher accuracies than non-personalised ones. Unfortunately, this accuracy gain requires collecting too large sets of labelled training data to realistically obtain from end users. In addition, the majority of current stress detectors exploit physiological or mobile phone data: the latter approach increases battery consumption compare with normal phone use, and the former requires to wear additional devices and to charge their batteries. Unlike previous work, this work proposes genuinely unobtrusive personalised stress detection system, based on use of environmental sensors and unsupervised training of hidden Markov models (HMM) classifier and hence requiring neither sensor maintenance nor data labelling efforts from end users. In the experiments with real life behavioural data of office workers, collected during 10 months, the proposed system achieved 67% accuracy of classifying each day as stressful vs. normal and 95% accuracy in classifying months, thanks to discovery of novel characteristics of motion trajectories, indicative of stress.