Video-based CPR analysis system

In this paper, we propose a system for automatically detecting cardiopulmonary resuscitation (CPR) events and analyzing CPR qualities in surveillance videos. The system is further applied to the training of healthcare providers in the emergency room. Instructors could more efficiently evaluate the CPR quality performed by a medical team with the aid of our system. We extract motion vectors in all image blocks, and take motion information as a clue to classify video sequences into CPR and non-CPR segments based on Support Vector Machine (SVM). We further analyze several indicators of CPR quality and attach CPR information to the video. In order not to infringe the privacy of the patient, we apply a mosaic mechanism to mask the skin color regions of the patient. Our system has been applied to several simulated CPR video sequences and the results show acceptable accuracy in CPR detection and CPR information measurement.