Unsupervised Classifier Self-Calibration through Repeated Context Occurences: Is there Robustness against Sensor Displacement to Gain?

Achieving a robust recognition of physical activities or gestures despite variability in sensor placement is highly important for the real-world deployment of wearable context-aware systems.It provides robustness against unintentional displacement of sensors, such as when doing intense physical activities or wearing sensors over extended periods of time.Here we focus on the problem of context recognition when sensors are displaced on body segments. We present an online unsupervised classifier self-calibration algorithm.Upon re-occurring context occurrences, the self-calibration algorithm adjusts the decision boundaries through online learning to better reflect the classes statistics, effectively allowing to track and adjust when classes drift in the feature space.We characterize the theoretical behavior of the system on a synthetic two-class problem dataset.We then analyze the real-world applicability of the method on a 5-class HCI related dataset, and a 6-class fitness scenario dataset.Our results show that the calibration increases the classification accuracy for displaced sensor positions by 33.3% in the HCI scenario and by 13.4% in the fitness scenario.