Objekt Deteksjon, simulert våkenhet
Abstract
Medical emergencies and trauma situations are stressful events. Training and repetition in controlled environment is used for health professionals to gain experience and retain the learning for longer. Laerdal Medical creates medical equipment and training equipment meant for health personnel.
SimMan is a high-fidelity patient simulator created by Laerdal Medical to train teams in treating medical emergencies and trauma. To make the simulations more realistic to increase the training effect work has been done to make prototypes that can replicate realistic behaviour.
The project in this thesis builds on a head prototype with LCD monitors as eyes and with a joystick and switch controller attached for changing eye modes. One of those modes were used in this thesis for receiving target angles the eyes should be rotated to look at a detected object. The detected object was found using a neural network trained on detecting faces.
Edge devices like Raspberry Pi with lower computing capability are cheap and flexible for many use cases. The effectiveness object detection network can achieve on these edge devices makes this eye prototype system flexible for further implementation and more advanced functionality.
The modified eye prototype and object detection pipeline developed for this thesis performs well and appear realistic when there is a single target person in the depth camera's field of view. Multiple people visible will make the eyes change who it looks at in a way that does not seem realistic. There are also some angles relative to the prototype where the eye contact looks unfocused. Medical emergencies and trauma situations are stressful events. Training and repetition in controlled environment is used for health professionals to gain experience and retain the learning for longer. Laerdal Medical creates medical equipment and training equipment meant for health personnel.
SimMan is a high-fidelity patient simulator created by Laerdal Medical to train teams in treating medical emergencies and trauma. To make the simulations more realistic to increase the training effect work has been done to make prototypes that can replicate realistic behaviour.
The project in this thesis builds on a head prototype with LCD monitors as eyes and with a joystick and switch controller attached for changing eye modes. One of those modes were used in this thesis for receiving target angles the eyes should be rotated to look at a detected object. The detected object was found using a neural network trained on detecting faces.
Edge devices like Raspberry Pi with lower computing capability are cheap and flexible for many use cases. The effectiveness object detection network can achieve on these edge devices makes this eye prototype system flexible for further implementation and more advanced functionality.
The modified eye prototype and object detection pipeline developed for this thesis performs well and appear realistic when there is a single target person in the depth camera's field of view. Multiple people visible will make the eyes change who it looks at in a way that does not seem realistic. There are also some angles relative to the prototype where the eye contact looks unfocused.