Using biomechanical constraints to improve video-based motion capture

In motion capture applications whose aim is to recover human body postures from various input, the high dimensionality of the problem makes it desirable to reduce the size of the search-space by eliminating a priori impossible configurations. This can be carried out by constraining the posture recovery process in various ways. Most recent work in this area has focused on applying camera viewpoint-related constraints to eliminate erroneous solutions. When camera calibration parameters are available, they provide an extremely efficient tool for disambiguating not only posture estimation, but also 3D reconstruction and data segmentation. Increased robustness is indeed to be gained from enforcing such constraints, which we prove in the context of an optical motion capture framework. Our contribution in this respect resides in having applied such constraints consistently to each main step involved in a motion capture process, namely marker reconstruction and segmentation, followed by posture recovery. These steps are made inter-dependent, where each one constrains the other. A more application-independent approach is to encode constraints directly within the human body model, such as limits on the rotational joints. This being an almost unexplored research subject, our efforts were mainly directed at determining a new method for measuring, representing and applying such joint limits. To the present day, the few existing range of motion boundary representations present severe drawbacks that call for an alternative formulation. The joint limits paradigm we propose not only overcomes these drawbacks, but also allows to capture intra- and inter-joint rotation dependencies, these being essential to realistic joint motion representation. The range of motion boundary is defined by an implicit surface, its analytical expression enabling us to readily establish whether a given joint rotation is valid or not. Furthermore, its continuous and differentiable nature provides us with a means of elegantly incorporating such a constraint within an optimisation process for posture recovery. Applying constrained optimisation to our body model and stereo data extracted from video sequence, we demonstrate the clearly resulting decrease in posture estimation errors. As a bonus, we have integrated our joint limits representation in character animation packages to show how motion can be naturally constrained in this manner

Design and implementation of a mobile sensor system for human posture tracking

De reconstructie van menselijke houding en het traceren van bewegingen kan in vele toepassingen worden gebruikt. Van animatie waar de bewegingen van acteurs kunnen gekoppeld worden aan een digitaal personage, tot revalidatie waar artsen na biomechanische analyse snel accurate diagnoses kunnen stellen. De snelle evolutie in de ontwikkeling van microsensoren en de opkomst van draadloze sensornetwerken hebben ertoe geleid dat draadloze nodes met verschillende sensoren hiervoor kunnen worden gebruikt. Door de informatie van deze sensoren te combineren is het immers mogelijk om absolute oriëntatie te berekenen. Eens deze informatie van elk lichaamsdeel bekend is, kan de volledige houding gereconstrueerd worden. In dit onderzoek werd een inertieel traceringssysteem ontwikkeld waarbij, in tegenstelling tot commerciële oplossingen, geen gyroscopen werden gebruikt. De sensor nodes worden enkel voorzien van accelerometers en magnetometers. Computer software implementeert het traceringssalgoritme en visualiseert de gereconstrueerde menselijke houding. Ingebedde software bepaalt dan weer de werking van de nodes en implementeert een draadloos protocol op maat dat toelaat om de informatie van verschillende nodes te ontvangen. De werking van het volledige systeem werd gevalideerd aan de hand van experimenten waarbij de houding van een persoon werd gevolgd.Human posture reconstruction and motion tracking is of interest for many different applications. From animation where captured motion sequences from actors can be mapped to a digital character in order to obtain a realistic visualization, to revalidation, where biomechanical analysis enables physicians to determine which exercises should be executed for a better and faster recovery. The combination of the increasingly fast evolution in the development of micromachined and the rise of wireless sensor networks as a distributed solution has allowed inertial sensors to become a fast emerging technology for orientation tracking. Sensor nodes equipped with accelerometers, magnetometers and gyroscopes supply three dimensional readings that can be used to determine driftfree absolute orientation. By approximating the human body by a set of rigid structures interconnected by joints, posture reconstruction is made possible when each of the individual bodyparts is equipped with a sensor node. In this work, an inertial tracking system was developed where, contrast to commercial applications, no gyroscopes were included. The sensor nodes were only equipped with accelerometers and magnetometers. Computer software implements the tracking algorithm and visualizes the reconstructed human posture. Embedded software determines the functionality of the nodes and implements a fully custom wireless protocol that allows to receive information from several nodes. The functionality of the entire system was validated by conducting full body tracking experiments