Abstract. The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classification. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4% accuracy.
Lukowicz et al. REF presented a technique to automatically track the progress of maintenance or assembly tasks using body-worn 3-axis accelerometers, microphones, and computers based on frequency-matching sound classification technique that combined the intensity analysis of signals from microphones at different parts of body and correlation analysis of surrounding sounds and user activity.
Recognizing workshop activity using body worn microphones and accelerometers
In Pervasive Computing
Computer Science