Posture estimation for a canine machine interface based training system

Dogs and humans have worked in partnership throughout history thanks to dogs´ unique capability of detecting signals in human voices or gestures and learning from human inputs. Traditional canine training methods rely solely on subjective visual observations made by trainers. We propose a canine body-area-network (cBAN) to incorporate context-aware sensing with objective detection algorithms to augment the sensitivity and specificity of human trainer´s awareness of the dogs they are training. As an initial effort, we developed a Bluetooth-based wireless infrastructure and tested inertial measurement units as cBAN sensor nodes to electronically assess the posture of the dogs. As a result, we were able to optimize the sensor locations and distinguish different postures using the distinct patterns in the measured angles.