The computer aided analysis of surgical activity and workflow in the operating theatre has gained much interest in the past few years. Many of these works deal with or depend on detection and classification of surgical activity which is represented by multi-dimensional, continuous signal data recorded from the Operating Room (OR). In this work, we propose a complementary approach directed towards intelligent intermediate processing of raw sensor data. We adopt a technique from data mining called motif discovery, which allows the unsupervised discovery of recurrent and semantically important patterns in otherwise unstructured data. Using data recorded by accelerometers placed on the operator, we discover an objective alphabet of surgical motions performed during simulated percutaenous vertebroplasties and autonomously identify the motion pattern for surgical tool changes in laparoscopic cholecystectomy. The results indicate the usability of motif discovery for autonomous pre-processing and mining of unstructured OR sensor data.     «

The computer aided analysis of surgical activity and workflow in the operating theatre has gained much interest in the past few years. Many of these works deal with or depend on detection and classification of surgical activity which is represented by multi-dimensional, continuous signal data recorded from the Operating Room (OR). In this work, we propose a complementary approach directed towards intelligent intermediate processing of raw sensor data. We adopt a technique from data mining called...     »