Semi-Supervised Approach to Detect Human Discontent from Real-Life Behaviour Data

Manifestation of emotions in human behaviour strongly depends on individual and is affected by his/her current activity. In lab studies, activities of test subjects are determined by study protocols, and emotions are carefully documented. In real life, activities of test persons are varied and unknown to researchers, while labels for emotions are scarcer. Usually, labels are obtained for long chunks of sensor data, and many labels are missing. Such datasets are too small for deep learning, whereas traditional approach (first to extract data features and then to train classifiers on them) requires more labels to learn most useful features for each user than the users typically provide. To address this problem, this work proposes a novel stacking-based approach: first to compress lengthy data chunks in the same way for all subjects by training unsupervised HMM, and then to use HMM output as input to supervised models, so that they can learn person-specific decision boundaries. In test results on motion data, obtained from depth cameras, this approach achieved 67-69% accuracies using only 25 labels per person, which is encouraging result for so challenging data.