Fall detection for robotic endoscope holders in Minimally Invasive Surgery

Classic Minimally Invasive Surgery (MIS) is an ergonomic burden for assistants and surgeons. The former need to adopt uncomfortable positions for hours while holding a camera to track the latter’s gestures inside the patient. This incurs assistant’s muscle fatigue which can lead to tremor or drift of the video feedback. A backdrivable robotic holder can be attached to this device in order to compensate its weight. This allows the user to place the camera at a desired position which the robot will steadily keep once he/she releases it. However, endoscopic cameras present difficult-to-model accessories whose gravity parameters can change during the same surgery. If these changes are not foreseen by the gravity model of the robot this results in a fall of the endoscope each time it is released. Therefore, it is desired to firstly detect if there is a fall in order to be able to correct it. In this article a fall detection method for a comanipulated robotic endoscope holder is proposed. It evaluates smoothness of the robot end effector trajectory to identify whether the user manipulates the instrument or it has been released and poorly compensated. An experiment was carried out with 10 subjects where 240 releases of the endoscope were performed while it was poorly compensated. The algorithm succeeded to detect the falls with sensitivity up to 99.17%.