Summary
In this paper, we present a calibration-free head gesture recognition system using a motion-sensor-based approach. For data acquisition we conducted a comprehensive study with 10 subjects. We analyzed the resulting head movement data with regard to separability and transferability to new subjects. Ordered means models (OMMs) were used for classification since they provide an easy-to-use, fast, and stable approach to machine learning of time series. In result, we achieved classification rates of 85–95% for nodding, head shaking and tilting head gestures and good transferability. Finally, we show first promising attempts towards online recognition.
