Inertial human motion capture (IHMC) has become a robust tool to estimate human kinematics in the wild such as industrial facilities. In contrast to optical motion capture, where occlusions might take place, the kinematics of a worker can be continuously provided. This is for instance a prerequisite for an ergonomic assessments of the workers. State-of-the-art IHMC solutions require inertial sensors to be tightly attached to body segments. This requires an additional setup time and lowers the practicability and ease of use when it comes to an industrial application. In contrast, sensors integrated into loose clothing such as a working jacket, may yield corrupted kinematics estimates due to the additional motion of loose clothing. In this work we present a study of orientations deviations obtained from kinematics estimates using tightly attached inertial sensors and into a working jacket integrated ones. We performed a quantitative analysis using data from the two hardware setups worn by 19 subjects performing different industry related tasks and measures of their body shapes. Using this data we approximated probability distributions of the deviation angles for each person and body segment. Applying different statistical measures we could gain insights to questions like, how severe orientation deviations are, if there is an influence of body shapes on the distribution and how probability distributions of the deviation angles can indicate physical motion limitations of a sensor attached to a segment.

Inertial motion capture has become an attractive alternative to optical motion capture for human joint angle estimation outside the laboratory. Usually inertial sensors are assumed to be tightly fixed to the body segments, which can be cumbersome regarding setup-time and ease-of-use. However, integrating the sensors directly into loose clothing, usually, results in additional clothing motion relative to the motion of the underlying bones that should be captured. In this work we propose the Difference Mapping distributions approach that corrects the segment orientations of a given inertial motion capture system that assumes tightly coupled sensors. The approach allows to reduce the joint angle errors due to clothing artefacts by at least 77.2% for people with similar morphology performing a similar task as seen in the training data, including an ergonomic assessments scenario at work places with ten participants. Moreover, we show that the uncertainty of the distribution can be used to measure the reliability of the predicted map if e.g. the motion is further away from the training data to allow for an artefact aware inertial motion tracking approach. The experimental data for this study is available online under [1].

In this paper we try to realize a system that enables users to interact with surrounding surfaces using touch interactions. For this purpose, we propose new touch detection technique which utilizes the shadows of a finger, and developed a prototype system with an infrared (IR) camera and two IR lights. Since the shapes of a finger's shadows vary drastically depending on the distance between the surface and finger, our prototype system can detect touch. To improve the accuracy of the estimated touch position, we also introduce multiple regression analysis into the estimation algorithm of the touch position. We conducted two experiments on the accuracy of estimated touch position, and the results shows that the target accuracy was within an error of less than 5 mm.