TY - JOUR A1 - Heinke, Lars N. A1 - Knicker, Axel J. A1 - Albracht, Kirsten T1 - Increased shoulder muscle stretch reflex elicitability in supine subject posture JF - Isokinetics and Exercise Science N2 - BACKGROUND: Muscle stretch reflexes are widely used to examine neural muscle function. The knowledge of reflex response in muscles crossing the shoulder is limited. OBJECTIVE: To quantify reflex modulation according to various subject postures and different procedures of muscle pre-activation steering. METHODS: Thirteen healthy male participants performed two sets of external shoulder rotation stretches in various positions and with different procedures of muscle pre-activation steering on an isokinetic dynamometer over a range of two different pre-activation levels. All stretches were applied with a dynamometer acceleration of 104∘/s2 and a velocity of 150∘/s. Electromyographical response was measured via sEMG. RESULTS: Consistent reflexive response was observed in all tested muscles in all experimental conditions. The reflex elicitation rate revealed a significant muscle main effect (F (5,288) = 2.358, ρ= 0.040; η2= 0.039; f= 0.637) and a significant test condition main effect (F (1,288) = 5.884, ρ= 0.016; η2= 0.020; f= 0.143). Reflex latency revealed a significant muscle pre-activation level main effect (F (1,274) = 5.008, ρ= 0.026; η2= 0.018; f= 0.469). CONCLUSION: Muscular reflexive response was more consistent in the primary internal rotators of the shoulder. Supine posture in combination with visual feedback of muscle pre-activation level enhanced the reflex elicitation rate. Y1 - 2020 U6 - http://dx.doi.org/10.3233/IES-192219 SN - 1878-5913 VL - 28 IS - 2 SP - 139 EP - 146 PB - IOS Press CY - Amsterdam ER - TY - CHAP A1 - Engemann, Heiko A1 - Du, Shengzhi A1 - Kallweit, Stephan A1 - Ning, Chuanfang A1 - Anwar, Saqib T1 - AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation T2 - Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020 N2 - We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level. Y1 - 2020 SN - 978-1-64368-137-5 U6 - http://dx.doi.org/10.3233/FAIA200770 N1 - Frontiers in Artificial Intelligence and Applications. Vol 332 SP - 89 EP - 97 PB - IOS Press CY - Amsterdam ER - TY - CHAP A1 - Alhaskir, Mohamed A1 - Tschesche, Matteo A1 - Linke, Florian A1 - Schriewer, Elisabeth A1 - Weber, Yvonne A1 - Wolking, Stefan A1 - Röhrig, Rainer A1 - Koch, Henner A1 - Kutafina, Ekaterina ED - Röhrig, Rainer ED - Grabe, Niels ED - Haag, Martin ED - Hübner, Ursula ED - Sax, Ulrich ED - Schmidt, Carsten Oliver ED - Sedlmayr, Martin ED - Zapf, Antonia T1 - ECG matching: an approach to synchronize ECG datasets for data quality comparisons T2 - German Medical Data Sciences 2023 – Science. Close to People. N2 - Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data. KW - Electrocardiography KW - Wearable electronic device KW - Sensors comparison KW - Time-series synchronization Y1 - 2023 SN - 978-1-64368-428-4 (Print) SN - 978-1-64368-429-1 (Online) U6 - http://dx.doi.org/10.3233/SHTI230718 N1 - Proceedings of the 68th. Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) 2023 in Heilbron, Germany Part of the series: Studies in Health Technology and Informatics VL - 307 SP - 225 EP - 232 PB - IOS Press ER -