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.