Running on marked tracks is central to athletic training, still athletes with visual impairment depend on external guides limiting independence in training and competition. Advancements in assistive and navigation technologies offer new possibilities for inclusive sports environments. Therefore, there is an urgent need to develop inclusive mobility solutions that empower visually impaired athletes. In this work, we focus on an autonomous lane guidance system designed to assist athletes in independent navigation. To address this investigation, we propose an end-to-end vision-based system built upon our TrackAid Dataset (TrackAid-DT). The system operates under real-world conditions and supports safe athletic running by delivering vibrotactile haptic feedback for real-time guidance, in contrast to prior works that primarily address walking or jogging. TrackAid-DT comprises egocentric, pixel-annotated monocular images acquired from chest-mounted cameras under diverse orientations, speeds, and lighting conditions, and is designed to train segmentation models that enable the proposed guidance system and its deployment on edge devices. We benchmark multiple advanced segmentation models using eight-fold cross validation, task specific pretraining with public KITTI dataset and report segmentation accuracy together with computational efficiency on both training and generalization sets. Among the considered models, pretrained TransUNet achieved the strongest segmentation quality (IoU 0.929 ± 0.064, F1 0.962 ± 0.036), while U-Net offered a more deployable balance (IoU 0.921 ± 0.064, 80.31 ms/frame, 12.45 FPS, and the lowest environmental impact at 16.49 g CO 2 e). We consider the trade-offs and deploy the system on multiple edge devices and validate it through outdoor prototype testing showing its practical effectiveness in real-world running scenarios. The system maintains stable lane detection and delivers timely haptic feedback at speeds of up to 10 km/h, demonstrating feasibility for autonomous vision based guidance in athletic running. To foster further development in this domain all code and data are publicly released (link: https://github.com/vrai-group/TrackAid-DT ).

Patent databases represent a large-scale, heterogeneous form of Big Data that captures technological evolution, diffusion, and commercialization potential of technologies. Traditional systematic mapping approaches such as the Technology Function Matrix (TFM) are widely used to correlate technical solutions with functional needs of society. However, the nature of the data that is utilized in the mapping process lacks indicators of market adoption which portrays the ground level potential of technological innovation. Therefore, such mappings do not fairly represent the technologies that have been widely adopted and cloud the judgement of stakeholders. Addressing this, we propose an Adoption-weighted Technology Function Matrix (ATFM), an analytics framework that integrates functional mapping with adoption-oriented indicators such as forward citations, grant rate, family size, and claims. Using a large dataset of 15,807 patents grants (2015–2025), we systematically showcase the procedure to apply ATFM to a case study of China's smart elderly care sector. The framework reveals not only technology-function linkages but also differential adoption patterns across domains. Our results highlight adoption hotspots in IoT-based sensing, low-latency 5G communication, and AI-driven health management, while identifying gaps in privacy protection and predictive diagnostics. Compared to conventional TFM, ATFM provides a more grounded, scalable and data-driven methodology that enriches patent analytics with adoption insights, offering value for both academic research in large scale dataset and practical decision-making in emerging industries and government stakeholders.

Predictive maintenance is a key aspect for the safety of critical infrastructure such as bridges, dams, and tunnels, where a failure can lead to catastrophic outcomes in terms of human lives and costs. The surge in Artificial Intelligence-driven visual robotic inspection methods necessitates high-quality datasets containing diverse defect classes with several instances on different conditions (e.g., material, illumination). In this context, we introduce a Controllable Object Inpainting Generative Adversarial Network (COIGAN) to synthetically generate realistic images that augment defect datasets. The effectiveness of the model is quantitatively validated by a Fréchet Inception Distance, which measures the similarity between the generated and training samples. To further evaluate the impact of COIGAN-generated images, a segmentation task was conducted, utilizing key performance metrics such as segmentation accuracy, mAP, mIoU, and F1 score, demonstrating that the synthetic images integrate seamlessly and produce results comparable to real defect images. Subsequently, COIGAN generability was successfully used for the segmentation of a defect-free dataset by inpainting defects. The results showcase COIGAN's ability to learn defect patterns and apply them in new contexts, preserving the original features of the base image and allowing the creation of new datasets with a desired multi-class distribution. Specifically, in the context of predictive maintenance, COIGAN enriches datasets, enabling deep learning models to more effectively identify potential infrastructure anomalies. Project page: https://bit.ly/4bzxwqf.

Ship detection using remote sensing and data from tracking devices like Automatic Identification System (AIS) play a critical role in maritime surveillance, supporting security, fisheries management, and efforts to combat illegal activities. However, challenges such as varying ship sizes, complex backgrounds, and intentional deactivation of AIS hinder accurate mapping. This study proposes a novel multimodal framework that integrates Sentinel-1 Synthetic Aperture Radar, Sentinel-2 and higher resolution optical imagery. It features an enhanced deep learning-based ship detection model combined with an AIS matchmaking algorithm to detect and cross-reference potentially suspicious maritime activities. The detection model is based on an enhanced You Only Look Once architecture, optimized for identifying small vessels in cluttered and noisy image backgrounds. The model achieves superior performance, surpassing state-of-the-art accuracy on multiple public datasets while reducing training time by 12% compared to baseline models. To ensure transparency within the pipeline, Eigen-CAM explainability techniques were employed, while C O 2 emissions were minimized during training using CodeCarbon , aligning the process with environmentally sustainable practices. Finally, the effectiveness of the pipeline was validated in a case study, successfully identifying potential ‘dark vessels’ and highlighting their possible involvement in suspicious activities. • Novel method tackles suspicious maritime activities using remote sensing imagery. • Multimodal framework integrates Sentinel-1 SAR, Sentinel-2, and optical imagery. • Enhanced YOLO model detects small ships in cluttered maritime environments. • Custom HS3-S2 dataset improves detection accuracy across multiple metrics. • Eigen-CAM ensures model transparency; CodeCarbon is used to report CO 2 emissions during training.

Data, information, and technology are improving healthcare aspects such as fitness tracking, patient-doctor communication, and access to health records, etc. Diet management approaches based on a functional estimation involve clinical and computer-supported methods that are utilized in the strategy of treatment. Clinical methods such as dietary records, 24-hour recall, and food frequency are common in practice and majorly rely on a calorie-conscious intake, which though with a cautionary proceeding works as expected, but inherently lags a focus on the processing levels of the ingredients such as the quantifiable content of natural or processed food. Calories consumed from natural sources share contrast to processed food. The exploration, hence, needs a hybrid approach that combines the computer-supported systems to build on a processed food proportion estimation methodology. Emerging augmented reality technologies seamlessly merge real-world environments with computer-supported perceptual information have the potential to solve the problem enabling user decision support through a unified schema of nutritional information. This research presents (i) a blueprint for a unified schema that merges clinical and computer-supported methods that underlie the viable standard for a host of multi-point chronic diet management applications, followed by (ii) a work-in-progress outline of the essential function blocks, resources, clinical considerations, and integrations that form a technology stack for the estimation of processed and non-processed food towards a clinical conscious diet intake.