AB1528-HPR FULL BODY HAPTIC BODYSUIT - AN INSTRUMENT TO MEASURE THE RANGE AND SPEED OF MOTION IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS (axSpA) - PRELIMINARY RESULTS - Weber, N.; Lennartz, Rebecca; Knitza, Johannes; Bayat, Sara; Sadeghi, Misha; Ibrahim, Alzhraa; Karatastan, C.; Sudhakar, A.; Ramzezanzadegan, N.; Kahali, S.; Mirzayev, A.; Schuster, Louis; et al. (Annals of the Rheumatic Diseases, 2022)
Movement of the spine is restricted in axial spondyloarthritis (axSpA) [1]. Spine function is usually assessed by the Bath Ankylosing Spondylitis Metrology Index (BASMI), which is based on a limited set of defined motions that are measured semiquantitatively in the spatial dimension but not in the temporal dimension. Sensor-based measurement of spine function in axSpA patients is in its infancy but may provide a deeper and more detailed understanding of the impact of axSpA on the impairment of spine function [2,3]. In theory, unbiased full body assessment of spine motion may open a new dimension in function analysis in axSpA.<br

Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach - Van Hooren, Bas; Lennartz, Rebecca; Cox, Maartje; Hoitz, Fabian; Plasqui, Guy; Meijer, Kenneth (Scandinavian journal of medicine & science in sports, 2024)
Background Prior studies investigated selected discrete sagittal-plane outcomes (e.g., peak knee flexion) in relation to running economy, hereby discarding the potential relevance of running technique parameters during noninvestigated phases of the gait cycle and in other movement planes. Purpose Investigate which components of running technique distinguish groups of runners with better and poorer economy and higher and lower weekly running distance using an artificial neural network (ANN) approach with layer-wise relevance propagation. Methods Forty-one participants (22 males and 19 females) ran at 2.78 m∙s−1 while three-dimensional kinematics and gas exchange data were collected. Two groups were created that differed in running economy or weekly training distance. The three-dimensional kinematic data were used as input to an ANN to predict group allocations. Layer-wise relevance propagation was used to determine the relevance of three-dimensional kinematics for group classification. Results The ANN classified runners in the correct economy or distance group with accuracies of up to 62% and 71%, respectively. Knee, hip, and ankle flexion were most relevant to both classifications. Runners with poorer running economy showed higher knee flexion during swing, more hip flexion during early stance, and more ankle extension after toe-off. Runners with higher running distance showed less trunk rotation during swing. Conclusion The ANN accuracy was moderate when predicting whether runners had better, or poorer running economy, or had a higher or lower weekly training distance based on their running technique. The kinematic components that contributed the most to the classification may nevertheless inform future research and trainin
Keywords: distance running; efficiency; energy cost; gait; machine learning; running kinematics

Neural network and layer-wise relevance propagation reveal how ice hockey protective equipment restricts players’ motion - Lennartz, Rebecca; Khassetarash, Arash; R. Nigg, Sandro; Eskofier, Björn; Nigg, Benno M. (PLoS ONE, 2024)
Understanding the athlete’s movements and the restrictions incurred by protective equipment is crucial for improving the equipment and subsequently, the athlete’s performance. The task of equipment improvement is especially challenging in sports including advanced manoeuvres such as ice hockey and requires a holistic approach guiding the researcher’s attention toward the right variables. The purposes of this study were (a) to quantify the effects of protective equipment in ice hockey on player’s performance and (b) to identify the restrictions incurred by it. Twenty male hockey players performed four different drills with and without protective equipment while their performance was quantified. A neural network accompanied by layer-wise relevance propagation was applied to the 3D kinematic data to identify variables and time points that were most relevant for the neural network to distinguish between the equipment and no equipment condition, and therefore presumable result from restrictions incurred by the protective equipment. The study indicated that wearing the protective equipment, significantly reduced performance. Further, using the 3D kinematics, an artificial neural network could accurately distinguish between the movements performed with and without the equipment. The variables contributing the most to distinguishing between the equipment conditions were related to the upper extremities and movements in the sagittal plane. The presented methodology consisting of artificial neural networks and layer-wise relevance propagation contributed to insights without prior knowledge of how and to which extent joint angles are affected in complex maneuvers in ice hockey in the presence of protective equipment. It was shown that changes to the equipment should support the flexion movements of the knee and hip and should allow players to keep their upper extremities closer to the tors

The Influence of Gamification on Motivation and Cardiac Variables in Soccer - Lennartz, Rebecca; Stöve, Maike; Talar, Karolina; Kumarampulakkal, Nandu; Dorschky, Eva; Witte, Matthias; Eskofier, Björn (Book of Abstracts of the 29th Annual Congress of the European College of Sport Science, 2024)
INTRODUCTION: Motivation, a complex construct that influences behavior and is a critical factor in an athlete’s success is being extensively researched in sports sciences [1,2]. However, motivation is primarily assessed through motivational questionnaires, which results in a lack of objective, continuous measures during a sports activity. Therefore, this study explores the feasibility of measuring short-term motivation through electrocardiogram (ECG). Gamification elements, aligned with self-determination theory (SDT), fulfilling the three basic needs relatedness, competence, and autonomy were used to influence motivation [3]. METHODS: In this study, 42 healthy soccer players (25 males, 17 females; age: (27,8 ± 7,8) years) performed a soccer drill in a circular space surrounded by a 360° screen. The goal for each player was to score as many goals as possible in two different, randomly ordered, counterbalanced scenarios, each lasting 3 minutes - one incorporating gamification elements aligned with SDT and one without. ECG data (Portabiles, Erlangen, Germany) were recorded during the drill. Postscenario assessments included responses to the Intrinsic Motivation Inventory (IMI) and Player Experience of Needs Satisfaction (PENS), both on a 7-point Likert scale, along with the Borg scale. RESULTS: Six and nine participants, respectively, were excluded from the questionnaires and ECG analyses. The results of the questionnaires, perceived exertion (Borg scale), perceived autonomy, competence and relatedness (PENS), and interest/enjoyment (IMI) showed no significant differences between scenarios. Average heart rate (HR) during the basic scenario was (116, 25 ± 21,76) bpm, while in the gamified version, it was (108, 39 ± 19,13) bpm (p = 0.0322). HR variability (HRV) revealed significantly higher RMSSD (+23 %, p = 0.0139) and SDNN (+17 %, p = 0.0172) values in the gamified version compared to the basic scenario. CONCLUSION: In this work, no effect of the gamification elements on motivation could be measured. Some players reported that they solely paid attention to the task and that they ignored additional elements. Nevertheless, the results showed a lower HR and higher HRV in the gamified scenario compared to the basic one, which indicates lower stress levels [5]. This trend is opposed to previous literature [6]. However, due to the complex and individual character of motivation, future analysis should include a detailed investigation of the physiological responses of individuals as well as a long-term study in the presented set-up. Acknowledgment: This work was supported by VDI/VDE-IT. [1] Clancy R et al. Psychol Sport Exerc., 27: 232-242, 2016. [2] Vallerand R et al. Handbook of sport psychology, 59-83, 2007. [3] Deci E et al. Scand. J. Educ. Res., 38(1): 3-14, 1994. [4] Francisco-Aparicio A et al. New trends in interaction, virtual reality and modeling, 113-126, 2013. [5] Aubert A. et al. Sports Med, 33: 889–919, 2003. [6] Schlömmer M. et al. Sustainability, 13(12), 6608. 2021. <br
Keywords: Cardiac Variables; Gamification; Motivation; Performance; Soccer

The Influence of Protective Equipment on Performance in Ice Hockey - Lennartz, Rebecca; Khassetarash, Arash; Spyrou, Evangelos; Hallihan, Aiden; Eskofier, Björn; Nigg, Benno (ISB Program & Abstract Book, 2023)
Understanding an ice hockey player’s movements and the restrictions incurred by the protective equipment is crucial for improving the equipment and subsequently, player’s performance. The optimum design of the protective equiment is specially challenging given the complexity of the movements and manuevers in ice hockey. This complexity arises from the multitude of possible variables that describe player’s motion and therefore complex analysis methods are required to help direct the researcher’s attention toward the right variables. The purpose of this work was to utilize artificial neural network (ANN) and layer-wise relevance propagation (LRP) [1] to understand how complex drills in ice hockey were affected by the presence of protective equipment. Seventeen male ice hockey players (age: 26.4 ± 7.0 years; mass: 86.4 ± 6.5 kg; height:183.8 ± 6.5 cm) skated the long axis of the ice rink as fast as possible. The sprint time over 30 meters was recorded (Brower TCi Timing system) and the movement data was captured by inertial measurements units integrated into the Xsens MVN Awinda System. The sprints were performed twice in either with (Equipment) or without protective equipment (No Equipment) conditions in a randomized order. Individual strides were defined from one ice contact to the subsequent ice contact of the same foot. A total of 24 strides were extracted for each of the participants. The trajectories of 12 joint angles (Figure 1) were extracted and normalized. All trajectories for one movement were concatenated into one single feature vector. A shallow ANN was trained to distinguish whether the sprint stride was performed in Equipment or No Equipment condition. Using LRP, the contribution of each variable to the classification result of the ANN was determined. On average, the participants performed the sprint drill 1.64 % faster in No Equipment compared to Equipment condition (p = 0.0037). The model was trained 17 times while leaving one participant out each time for testing, reaching an average accuracy of 99.3 %. The average relevance scores that were derived from the ANN model are depicted in Figure 1. Each row corresponds to one rotational degree of freedom, while each column depicts one percent of the stride cycle. The histograms at the top and right part of the figure show the vertical, and horizontal summation of the heatmap respectively. The results show that the ANN can distinguish between the two conditions while observing differences in performance. Thus, it appears that protective equipment impairs performance. The presented data indicate that rotations around the medial-lateral axis (in sagittal plane) and movements associated to the shoulder, knee, and hip joint contributed the most to the classification result. The proposed methodology based on ANN and LRP was able to highlight the variables and time points that differ between Equipment and No Equipment conditions. We were able to distinguish the important movements that the protective equipment restricts, and future design can leverage this information. In general, this approach allows researchers to investigate a question from a holistic point of view and therefore make informed decisions in complex, multivariate problems. [1] Bach S et al. PLOS ONE 10(7), 201
Keywords: Explainable AI; Machine Learning; Biomechanics; Performance; Joint Angles