A neural network approach to markerless measurement of human motion

Most automatic motion analysis system operates by tracking markers across a field of view. The markers are usually attached to the skin at the joints. For some applications such as measuring the motion of elderly and the disabled persons, this approach can be uncomfortable which ultimately can affect the motion itself. The protocol of some markers can also be a problem for example active markers using infra-red LEDs must continuously be in the field of view of the detectors at all times. This will impose restriction in the motion. Therefore a marker free method is an attractive proposition. Such an approach would allow the subject to move freely thus exhibiting a more natural movement. We have developed a PC based system which uses frames from the video or photographic capture of a motion in the frontal and sagital plane. Key frames are selected and co-ordinate positions of the joints are identified. Joint angles are calculated from these positions based on a multiple link model of the human figure. The angles are then used as training data for a fully connected neural network. The neural network generates a model of the captured motion which is used to reproduce closely the actual motion.