Measuring human movement in real time is of primary importance in a number of new applications of interactive systems and human centered robotics. A major difficulty in this field arises from the high joint redundancy of the human kinematics. Conventional approaches address this problem by installing a rather large number of sensors or markers on the subject. On the other hand, well admitted theories in neurosciences claim that joint synchronization in human movements is governed by so-called synergies, that can be viewed as joint patterns corresponding to a given gesture of a given individual. In this paper, we intend to exploit this property in order to reduce the number of sensors to be installed on a human subject when tracking his/her motion. Namely, our suggested method comprizes two phases. In a learning stage, the subject is asked to complete a given gesture a few times, while he/she is equipped with sensors able to measure his/her full posture. An algorithm is thus used in a second phase to reduce the required number of sensors while reconstructing the whole posture of the subject. Experimental evidence is provided for the particular motion of sit-to-stand transfer, in a study that involves healthy subjects.
['Ludovic Saint-Bauzel', 'Viviane Pasqui', 'Guillaume Morel', 'Bruno Gas']
Real-time human posture observation from a small number of joint measurements