Motion Capture Technology in Sports Scenarios: A Survey

Sensors, Journal Year: 2024, Volume and Issue: 24(9), P. 2947 - 2947

Published: May 6, 2024

Motion capture technology plays a crucial role in optimizing athletes’ skills, techniques, and strategies by providing detailed feedback on motion data. This article presents comprehensive survey aimed at guiding researchers selecting the most suitable for sports science investigations. By comparing analyzing characters applications of different technologies scenarios, it is observed that cinematography remains gold standard biomechanical analysis continues to dominate research applications. Wearable sensor-based has gained significant traction specialized areas such as winter sports, owing its reliable system performance. Computer vision-based made advancements recognition accuracy reliability, enabling application various from single-person technique multi-person tactical analysis. Moreover, emerging field multimodal technology, which harmonizes data sources with integration artificial intelligence, proven be robust method complex scenarios. A review literature past 10 years underscores increasing significance notable shift laboratory practical training fields. Future developments this should prioritize technological cater addressing challenges occlusion, outdoor capture, real-time feedback.

Language: Английский

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Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

Medicine Plus, Journal Year: 2024, Volume and Issue: 1(2), P. 100030 - 100030

Published: May 17, 2024

With the rapid development of artificial intelligence, large language models (LLMs) have shown promising capabilities in mimicking human-level comprehension and reasoning. This has sparked significant interest applying LLMs to enhance various aspects healthcare, ranging from medical education clinical decision support. However, medicine involves multifaceted data modalities nuanced reasoning skills, presenting challenges for integrating LLMs. review introduces fundamental applications general-purpose specialized LLMs, demonstrating their utilities knowledge retrieval, research support, workflow automation, diagnostic assistance. Recognizing inherent multimodality medicine, emphasizes multimodal discusses ability process diverse types like imaging electronic health records augment accuracy. To address LLMs' limitations regarding personalization complex reasoning, further explores emerging LLM-powered autonomous agents healthcare. Moreover, it summarizes evaluation methodologies assessing reliability safety contexts. transformative potential medicine; however, there is a pivotal need continuous optimizations ethical oversight before these can be effectively integrated into practice.

Language: Английский

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Current Research and Future Strategies for the Management of Vision-Threatening Diabetic Retinopathy

Asia-Pacific Journal of Ophthalmology, Journal Year: 2024, Volume and Issue: 13(5), P. 100109 - 100109

Published: Sept. 1, 2024

Diabetic retinopathy (DR) is a major ocular complication of diabetes and the leading cause blindness visual impairment, particularly among adults working-age adults. Although medical economic burden DR significant its global prevalence expected to increase, in low- middle-income countries, large portion vision loss caused by remains preventable through early detection timely intervention. This perspective reviewed latest developments research innovation three areas, first novel biomarkers (including advanced imaging modalities, serum biomarkers, artificial intelligence technology) predict incidence progression DR, second, screening referable vision-threatening (VTDR), finally, therapeutic strategies for VTDR, including diabetic macular oedema (DME), with goal reducing blindness.

Language: Английский

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An effective video captioning based on language description using a novel Graylag Deep Kookaburra Reinforcement Learning

EURASIP Journal on Image and Video Processing, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 9, 2025

Video captioning exhibits a complex challenge, particularly due to the increased subject intensity within videos compared image caption generation. The presence of redundant visual information in video data adds complexity for captioners, making it difficult simplify various content and eliminate irrelevant elements. Additionally, this redundancy often results misalignment with equivalent semantics ground truth, further complicating process. In response these challenges, research introduces Graylag Deep Kookaburra Reinforcement Learning (GDKRL) framework, which enhances through multi-stage First, object detection is performed using single-shot multi-box detector generalized intersection over union accurate tracking similarity calculation. Next, gazelle autoencoder extracts fuses features from frames, integrating temporal into unified representation. residual convolved dual sparse graph attention network then generates detailed contextually rich language descriptions by applying mechanisms convolutions. Finally, hybrid graylag kookaburra optimization refine process, producing comprehensive precise textual content. Extensive experiments on MSVD yielded 81.79 BLEU-4, 51.2 METEOR, 133.3 CIDEr 81.7 ROUGE-L; VATEX achieved 62.29 110.2 78.45 MSR-VTT dataset obtained 44.52 33.35 63.9 68.9 ROUGE-L, demonstrating that proposed technique significantly outperforms previous approaches highlights its effectiveness.

Language: Английский

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D3-Dehaze: a divide-and-conquer framework for enhanced single image dehazing

The Visual Computer, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Language: Английский

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Semi-supervised learning based on distance measurement and augmentations comparison for SAR image recognition

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 26

Published: Jan. 14, 2025

Language: Английский

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Enhanced YOLOv8 framework for precision vehicle detection in high-resolution remote sensing images

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(3)

Published: Jan. 17, 2025

Language: Английский

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Local dual-branch attention feature learning framework from UAVs for visual defect detection

The Visual Computer, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

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Context-aware target texture perturbation attack for concealed object detection

The Visual Computer, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Language: Английский

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Multi-Stage Learning for Intuitive Visualization of Microcystic Macular Edema in OCT Images

Journal of Medical and Biological Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Abstract Purpose Detecting and monitoring Microcystic Macular Edema (MME) in Optical Coherence Tomography (OCT) images is vital for early diagnosis of Diabetic (DME), a leading cause blindness developed countries. However, detecting MME remains challenging due to its fuzzy boundaries diffuse nature. In this work, we propose novel fully-automatic methodology based on multi-stage regional learning successfully detect visualize OCT images. Methods Our work divided into two main stages: the first stage coarsely identifies general DME accumulations innermost retinal layers. On other hand, second precisely detects within reduced search space. These detections are then used generate intuitive confidence maps. Results approach achieves mean 0.9618 ± 0.0518 per pixel, demonstrating consistent strong detections. This robust facilitates MME, independent clinicians’ subjectivity, has potential significantly impact quality life patients. Conclusion represents significant advancement automatic analysis complex pathologies. Source code available at: https://github.com/PlacidoFranciscoLizancosVidal/Microcysts_paper_code .

Language: Английский

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