Offenburg University of Applied Sciences
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Comparison of an adaptive ankle brace to conventional taping for rehabilitation of acute ankle injury in young subelite soccer players: a pilot study
Get PDFContext: Ankle sprains are a common injury in sports, for which use of external ankle support during rehabilitation has been suggested to improve clinical outcomes.
Design: Cohort study.
Methods: Thirteen soccer players experiencing acute lateral ankle sprain injury were provided a novel adaptive ankle brace or conventional ankle taping (control) as external ankle support throughout the injury rehabilitation process. All other clinical procedures were identical, and rehabilitation was supervised by the same team staff member. Time from injury to clearance to return to sport was tracked. Player experience with the ankle brace also was queried via electronic surveys.
Results: The median time to return to sport was less for the Brace group (52.5 d) compared to the Control group (79.5 d), but the distributions of the 2 groups were not found to differ significantly (P = .109). Player surveys indicated they felt the brace to be comfortable or very comfortable, with better freedom of movement than other braces and the same freedom of movement as wearing no brace. All players reported wearing the brace to be the same or better experience as ankle taping.
Discussion: These preliminary results indicate that the adaptive ankle brace is at least as effective as ankle taping for providing external support during the rehabilitation phase following acute lateral ankle sprain and suggest it may be a more effective ankle support solution in terms of patient compliance than conventional bracing or taping
A biomechanical report of an acute lateral ankle sprain during a handball-specific cutting movement
No full textBiomechanical measurements of accidental ankle sprain injuries are rare but make important contributions to a more detailed understanding of the injury mechanism. In this case study, we present the kinematics and kinetics of a lateral ankle sprain of a female athlete performing handball-specific fake-and-cut manoeuvres. Three-dimensional kinematics and kinetics were recorded and six previously performed trials were used as reference. Plantarflexion, inversion, and internal rotation angles were substantially larger than the reference trials and peaked between 190 and 200 ms after initial ground contact. We observed a highly increased inversion and internal rotation moment. However, compared to the non-injury trials the data also revealed a reduction in the second dorsiflexion moment peak. Ground reaction forces were lower throughout the injury trial. Other parameters at initial ground contact including ankle and hip position, step length, and the traction coefficient indicate that a preparatory maladjustment occurred. This study adds valuable contributions to the understanding of lateral ankle sprains by building upon previously published reports and considering the shoe-surface interaction as an important factor for injury
Exploration of Neural Network Architectures for Inertia Parameter Identification of a Robotic Arm
No full textWe propose a machine-learning-based approach for identifying inertia parameters of robotic systems. We evaluate the method in simulation and compare it against classical methods. Specifically, we implement parameter identification based on numerical optimization and test it using ground truth data. For a case study, we set up a physical simulation of a four-degree-of-freedom robot arm, formulating the problem with Newton-Euler equations as opposed to the conventional Lagrangian formulation at the joint level. Additionally, we derive a test methodology for assessing various Artificial Neural Network architectures
Distributed Authentication using Self Sovereign Identities
No full textTraditional authentication involves sharing a considerable amount of personal and identifying information. Usually, a single central authority controls the data of all their users. This creates a single point of failure and users typically have to relinquish control over their data. Therefore it is important to explore alternate authentication mechanisms to uphold data sovereignty. Data sovereignty describes forms of independence, control, and autonomy over digital data. Enforcing data sovereignty also requires independence from central authorities. This paper explores alternate decentralized authentication methods. It leverages Verifiable Credentials (VCs) which allow verification without needing to contact the issuer and self-sovereign identities in the form of Decentralized IDentifiers (DIDs). This paper aims to leverage the decentralized authentication supported by VCs and DIDs and provide two use cases that might explain how they could be used
Impact of forefoot cushioning stiffness on block start performance in sprinting
No full textThis study aimed to identify the impact of different forefoot cushioning properties in “advanced spiked footwear” on sprinting performance during the block start. Kinetic parameters were collected for twenty-three competitive sprinters during a block sprint start in two advanced spike conditions with only a difference in forefoot cushioning stiffness. An instrumented start block was used to measure the ground reaction forces applied in the front and rear leg. The stiffer shoe condition showed significantly better performance for most parameters, suggesting a softer midsole in forefoot cushioning is not related to better block start performance. This study has demonstrated that differences in midsole materials can alter sprinting block performance and should be considered when analysing advanced spikes features, especially across different shoe brands and their cushioning technologies
Towards flexible demultiple with deep learning
No full textDeep learning (DL) methods have demonstrated promising advancements in seismic demultiple, addressing issues of traditional workflows. However, a key challenge is the limited flexibility of DL solutions in the demultiple process. Once a DL model has been trained, it produces one demultiple solution for a given input data. However, interpreting seismic events as multiples or primaries is often subjective. Moreover, multiple discrimination in Common Depth Point (CDP) domain relies on the accurate Normal Moveout (NMO) velocity estimation. To address this, we propose a supervised DL training method for demultiple based on moveout discrimination in the CDP domain. Our novel approach generates several multiple models based on moveout discriminations for a given input CDP gather, enhancing flexibility without additional computational costs. We validate the generalization ability of the Convolutional Neural Network (CNN) trained with the proposed methodology on synthetically generated data and on field datasets
Ambiguous Annotations: When is a Pedestrian not a Pedestrian?
No full textDatasets labelled by human annotators are widely used in the training and testing of machine learning models. In recent years, researchers are increasingly paying attention to label quality and correctness. However, it is not always possible to objectively determine, whether an assigned label is correct or not. The present work investigates this ambiguity in the annotation of autonomous driving datasets as an important dimension of data quality. Our experiments show that excluding highly ambiguous data from the training improves model performance of a state-of-the-art pedestrian detector in terms of LAMR, precision and F1-score, thereby saving training time and annotation costs. Furthermore, we demonstrates that, in order to safely remove ambiguous instances and ensure the retained representativeness of the training data, an understanding of the properties of the dataset and class under investigation is crucial
Enhancing Independence through Intelligent Robotics: An AI Driven Assistive Robotics Interface
Get PDFApplying methods in artificial intelligence to the field of assistive robotics has the potential to increase the independence of people with disabilities. The usage of AI to realize a shared control in this context is controversial, due to the high complexity of everyday tasks and the needed safety requirements. This paper presents the development of a user interface for AI-driven assistive robotic arms (ARA) that aims to assist people with physical disabilities in performing daily activities. This interface allows the user to select object manipulation tasks based on the objects recognized in a live video stream. Further, we compare several state-of-the-art, real-time object detection models to facilitate automatic robotic control. The results demonstrate the feasibility of the model and its potential integration into the overall robotic system
Power System Modeling Tools for Sustainable Development: A Review
No full textThe green growth paradigm aims to harmonize economic growth with environmental sustainability. Electricity is essential for economic development, and if its associated carbon emissions are sufficiently low, it is a key enabler of green growth and sustainable development. Zambia, a developing country, had only 32.5% of households with access to electricity in 2022. This paper provides a comprehensive overview of power system modeling tools applicable in Zambia and evaluates the ongoing and completed power system modeling initiatives in the Zambian energy space. The study discusses the key features, applicability, and relevance of various modeling tools, including PyPSA-Earth, OSeMOSYS, MAED, MESSAGE, and WASP. Findings indicate that while many tools are available, the selection and adaptation of these tools are crucial for addressing the specific challenges in Zambia's power system. This paper aims to support the strategic planning necessary to achieve a sustainable low-carbon energy transition in Zambia
