Design and Implementation of a Gesture-Aided E-Learning Platform.

In gesture-aided learning (GAL), learners perform specific body gestures while rehearsing the associated learning content. Although this form of embodiment has been shown to benefit learning outcomes, it has not yet been incorporated into e-learning. This work presents a generic system design for an online GAL platform. It is comprised of five modules for planning, administering, and monitoring remote GAL lessons. To validate the proposed design, a reference implementation for word learning was demonstrated in a field test. 19 participants independently took a predefined online GAL lesson and rated their experience on the System Usability Scale and a supplemental questionnaire. To monitor the correct gesture execution, the reference implementation recorded the participants' webcam feeds and uploaded them to the instructor for review. The results from the field test show that the reference implementation is capable of delivering an e-learning experience with GAL elements. Designers of e-learning platforms may use the proposed design to include GAL in their applications. Beyond its original purpose in education, the platform is also useful to collect and annotate gesture data.

Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units.

In alpine skiing, four commonly used turning styles are snowplow, snowplow-steering, drifting and carving. They differ significantly in speed, directional control and difficulty to execute. While they are visually distinguishable, data-driven classification is underexplored. The aim of this work is to classify alpine skiing styles based on a global navigation satellite system (GNSS) and inertial measurement units (IMU). Data of 2000 turns of 20 advanced or expert skiers were collected with two IMU sensors on the upper cuff of each ski boot and a mobile phone with GNSS. After feature extraction and feature selection, turn style classification was applied separately for parallel (drifted or carved) and non-parallel (snowplow or snowplow-steering) turns. The most important features for style classification were identified via recursive feature elimination. Three different classification methods were then tested and compared: Decision trees, random forests and gradient boosted decision trees. Classification accuracies were lowest for the decision tree and similar for the random forests and gradient boosted classification trees, which both achieved accuracies of more than 93% in the parallel classification task and 88% in the non-parallel case. While the accuracy might be improved by considering slope and weather conditions, these first results suggest that IMU data can classify alpine skiing styles reasonably well.

Towards an IMU Evaluation Framework for Human Body Tracking.

Existing full-body tracking systems, which use Inertial Measurement Units (IMUs) as sensing unit, require expert knowledge for setup and data collection. Thus, the daily application for human body tracking is difficult. In particular, in the field of active and assisted living (AAL), tracking human movements would enable novel insights not only into the quantity but also into the quality of human movement, for example by monitoring functional training. While the current market offers a wide range of products with vastly different properties, literature lacks guidelines for choosing IMUs for body tracking applications. Therefore, this paper introduces developments towards an IMU evaluation framework for human body tracking which compares IMUs against five requirement areas that consider device features and data quality. The data quality is assessed by conducting a static and a dynamic error analysis. In a first application to four IMUs of different component consumption, the IMU evaluation framework convinced as promising tool for IMU selection.