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Supplemental material, sj-docx-1-pil-10.1177_14644207221112362 for A novel 3D printed personalised insole for improvement of flat foot arch compression and recoil – preliminary study by Che-Wei Hu, Canh Toan Nguyen, Dominik Hölbling, Toh Yen Pang, Arnold Baca and Peter Dabnichki in Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications

Supplemental material, sj-docx-1-pil-10.1177_14644207221112362 for A novel 3D printed personalised insole for improvement of flat foot arch compression and recoil – preliminary study by Che-Wei Hu, Canh Toan Nguyen, Dominik Hölbling, Toh Yen Pang, Arnold Baca and Peter Dabnichki in Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications

In sports feedback systems, digital systems perform tasks such as capturing, analysing and representing data. These systems not only aim to provide athletes and coaches with insights into performances but also help athletes learn new tasks and control movements, for example, to prevent injuries. However, designing mobile feedback systems requires a high level of expertise from researchers and practitioners in many areas. As a solution to this problem, we present Direct Mobile Coaching (DMC) as a design paradigm and model for mobile feedback systems. Besides components for feedback provisioning, the model consists of components for data recording, storage and management. For the evaluation of the model, its features are compared against state-of-the-art frameworks. Furthermore, the capabilities are benchmarked using a review of the literature. We conclude that DMC is capable of modelling all 39 identified systems while other identified frameworks (MobileCoach, Garmin Connect IQ SDK, RADAR) could (at best) only model parts of them. The presented design paradigm/model is applicable for a wide range of mobile feedback systems and equips researchers and practitioners with a valuable tool.

Background Musculoskeletal simulations are widely used in the research community. The locations of surface markers are mostly used to scale a generic model to the participant’s anthropometry. Marker-based scaling approaches include errors due to inaccuracies in marker placements. Research question How do scaling errors of the thigh and shank segments influence simulation results? Methods Motion capture data and magnetic resonance images from a child with cerebral palsy and a typically developing child were used to create a subject-specific reference model for each child. These reference models were modified to mimic scaling errors due to inaccurately placed lateral epicondyle markers, which are frequently used to scale the thigh and shank segments. The thigh length was altered in 1 % steps from the original length and the shank length was accordingly adjusted to keep the total leg length constant. Thirty additional models were created, which included models with an altered thigh length of ±15 %. Subsequently, musculoskeletal simulations with OpenSim were performed with all models. Joint kinematics, joint kinetics, muscle forces and joint contact forces (JCF) were compared between the reference and altered models. Results The investigated scaling error influenced joint kinematics and joint kinetics by up to 9.4° (hip flexion angle) and 0.15 Nm/kg (knee flexion moment), respectively. Maximum muscle and JCF differences of 46 % (medial gastrocnemius) and 72 % (hip JCF) bodyweight, respectively, were observed between the reference and altered models. Scaling errors mainly changed the magnitude but not the shape of most analyzed parameters. The influence of scaling errors on simulation results were similar in both participants. Significance Scaling errors of the thigh segment influence simulation results at all joints due to the global optimization approach used in musculoskeletal simulations. Our findings can be used to estimate potential errors due to marker-based scaling approaches in previous and future studies.

Training programme. Image series of hip, quadriceps and neuromuscular training programme showing start and end position of each exercise. (PDF 1055 kb)

Performance analysis and good decision making in sports is important to maximize chances of winning. Over the last years the amount and quality of data which is available for the analysis has increased enormously due to technical developments like, e.g., of sensor technologies or computer vision technology. However, the data-driven analysis of athletes and team performances is very demanding. One reason is the so called semantic gap of sports analytics. This means that the concepts of coaches are seldomly represented in the data for the analysis. Furthermore, sports in general and game sports in particular present a huge challenge due to its dynamic characteristics and the multi-factorial influences on an athlete’s performance like, e.g., the numerous interaction processes during a match. This requires different types of analyses like, e.g., qualitative analyses and thus anecdotal descriptions of performances up to quantitative analyses with which performances can be described through statistics and indicators. Additionally, coaches and analysts have to work under an enormous time pressure and decisions have to be made very quickly. In order to facilitate the demanding task of game sports analysts and coaches we present a generic approach how to conceptualize and design a Data Analytics System (DAS) for an efficient support of the decision making processes in practice. We first introduce a theoretical model and present a way how to bridge the semantic gap of sports analytics. This ensures that DASs will provide relevant information for the decision makers. Moreover, we show that DASs need to combine qualitative and quantitative analyses as well as visualizations. Additionally, we introduce different query types which are required for a holistic retrieval of sports data. We furthermore show a model for the user-centered planning and designing of the User Experience (UX) of a DAS. Having introduced the theoretical basis we present SportSense, a DAS to support decision making in game sports. Its generic architecture allows a fast adaptation to the individual characteristics and requirements of different game sports. SportSense is novel with respect to the fact that it unites raw data, event data, and video data. Furthermore, it supports different query types including an intuitive sketch-based retrieval and seamlessly combines qualitative and quantitative analyses as well as several data visualization options. Moreover, we present the two applications SportSense Football and SportSense Ice Hockey which contain sport-specific concepts and cover (high-level) tactical analyses.