The ability to accurately capture locomotion is relevant in various use cases, in particular in the sports and health area. With the major goal of providing a measurement system that can deliver different types of relevant information (3D body segment kinematics, spatiotemporal locomotion parameters, and locomotion patterns) in-field and in real-time, we propose a novel probabilistic (single-plane) ground contact estimation method, using four contact points defined through a biomechanical foot model, and integrate this into an existing inertial motion capturing method. The resulting method is quantitatively evaluated on simulated and real IMU data in comparison to an optical motion capture system on walking, running, and jumping sequences. The results show its ability to maintain a good average 3D kinematics estimation error on low- and high-acceleration locomotion, whereas many previous accuracy studies restrict themselves to movements with low to moderate global accelerations, such as upper body activities or slow locomotion. Moreover, a qualitative evaluation of the estimated ground contact probabilities demonstrates the method's ability to also provide consistent information also for deriving spatiotemporal locomotion parameters as well as locomotion patterns (e.g., over-pronation/-supination) simultaneously with the 3D kinematics.

In the context of a smart user assistance system for industrial manipulation tasks it is necessary to capture motions of the upper body and limbs of the worker in order to derive his or her interactions with the task space. While such capturing technology already exists, the novelty of the proposed work results from the strong requirements of the application context: The method should be flexible and use only on-body sensors, work accurately in industrial environments that suffer from severe magnetic disturbances, and enable consistent registration between the user body frame and the task space. Currently available systems cannot provide this. This paper suggests a novel egocentric solution for visual-inertial upper-body motion tracking based on recursive filtering and model-based sensor fusion. Visual detections of the wrists in the images of a chest-mounted camera are used as substitute for the commonly used magnetometer measurements. The on-body sensor network, the motion capturing system, and the required calibration procedure are described and successful operation is shown in a real industrial environment.