Section: New Results

Motion Sensing

Participants : Franck Multon, Pierre Plantard.

Recording human activity is a key point of many applications and fundamental works. Numerous sensors and

systems have been proposed to measure positions, angles or accelerations of the user’s body parts. Whatever

the system is, one of the main is to be able to automatically recognize and analyze the user’s performance

according to poor and noisy signals. Hence, recognizing and measuring human performance are important scientific challenges especially when using low-cost and noisy motion capture systems. MimeTIC has addressed the above problems in two main application domains.

Firstly, in ergonomics, we explored the use of low-cost mottion capture systems, a Microsoft Kinect, to measure the 3D pose of a subject in natural environments, such as on a workstation, with many occlusions and inappropriate sensor placements. Predicting the potential accuracy of the measurement for such complex 3D poses and sensor placements is challenging with classical experimental setups. To tackle this problem, we propose [16] a new evaluation method based on a virtual mannequin. Thanks to this evaluation method, more than 500,000 configurations have been automatically tested, which is almost impossible to evaluate with classical protocols. The results show that the kinematic information obtained by the Kinect system is generally accurate enough to fill-in ergonomic assessment grids. However inaccuracy strongly increases for some specific poses and sensor positions. Using this evaluation method enabled us to report configurations that could lead to these high inaccuracies. Results obtained with the virtual mannequin are in accordance with those obtained with a real subject for a limited set of poses and sensor configuration. This knowledge can help to anticipate potential problems using a Kinect in given scenarios, and to propose methods to tackle these expected problems.

Secondly, in clinical gait analysis, we proposed a method to overcome the main limitations imposed by the low accuracy of the Kinect measurements in real medical exams. Indeed, inaccuracies in the 3D depth images leads to badly reconstructed poses and inaccurate gait event detection. In the latter case, confusion between the foot and the ground leads to inaccuracies in the foot-strike and toe-off event detection, which are essential information to get in a clinical exam. To tackle this problem we assumed that heel strike events could be indirectly estimated by searching for the extreme values of the distance between the knee joints along the walking longitudinal axis [5] . As Kinect sensor may not accurately locate the knee joint, we used anthropometrical data to select a body point located at a constant height where the knee should be in the reference posture. Compared to previous works using a Kinect, heel strike events and gait cycles are more accurately estimated, which could improve global clinical gait analysis frameworks with such a sensor. Once these events are correctly detected, it is possible to define indexes that enables the clinician to have a rapid state of the quality of the gait. We proposed [4] a new method to asses gait asymmetry based on depth images, to decrease the impact of errors in the Kinect joint tracking system. It is based on the longitudinal spatial difference between lower-limb movements during the gait cycle. The movement of artifically impaired gaits was recorded using both a Kinect placed in front of the subject and a motion capture system. The proposed longitudinal index distinguished asymmetrical gait (p < 0.001), while other symmetry indices based on spatiotemporal gait parameters failed using such Kinect skeleton measurements. This gait asymmetry index measured with a Kinect is low cost, easy to use and is a promising development for clinical gait analysis.