Musculoskeletal disorders (MSDs) are among the leading causes of workplace injuries and reduced productivity. Conventional ergonomic assessments often rely on subjective observation, limiting accuracy and prevention. There is a clear gap in adopting computational biomechanics tools that can provide objective, predictive, and scalable solutions for workplace ergonomics. This review synthesizes literature on three key approaches—digital human modeling (DHM), finite element analysis (FEA), and AI-driven simulations—evaluating their theoretical foundations, practical applications, and limitations in posture assessment and workplace safety. DHM supports proactive ergonomic design through virtual worker–task interaction; FEA enables detailed strain and tissue-level analysis for injury risk prediction; AI offers scalability for real-time monitoring and early risk detection. However, widespread implementation is constrained by high computational demands (FEA), limited standardization (DHM), and data dependency (AI). To advance adoption, this paper proposes a roadmap emphasizing cost-effective simulation platforms, collaborative validation frameworks, and integration of AI with industry-ready DHM and FEA tools. These strategies can accelerate translation from research to practice, improving ergonomic design and reducing MSD prevalence in workplaces.
Traditional technologies provide significant integration opportunities for wildlife conservation and re-identification; nonetheless, we necessitate more sophisticated solutions to address extensive areas and substantial animal populations. The research introduced a scalable framework for wildlife re-identification that integrates SimCLR, a self-supervised representation learning method, with supervised fine-tuning utilizing a ResNet-50 backbone on the WildlifeReID-10k dataset, which comprises 37 animal classes. The paper addresses class imbalance by employing weighted losses and data augmentation techniques to maintain fine-grained identity clues in wildlife photos. The suggested model attained an accuracy of 89.81
Augmented human (AH) technologies and transhumanism (TH) try to enhance or modify humans and give them new capabilities using bioengineering, information technology, new sensors, artificial intelligence (AI), implants, etc. The concepts of augmented human and transhuman are sometimes used as synonyms and they have some overlap with each other, but they differ significantly. This paper provides a framework to differentiate between AH and TH and helps to understand their goals and methods. We give an overview and conceptual distinctions of AH and TH and bring more clarity on terminology for the developers and practitioners working in these areas. We also discuss the realism of transhuman ideas, their philosophical premises, their intertwined ethical and societal issues, and some aspects of AI, as it is a crucial element in some AH and TH goals and scenarios.
The advancement of vehicle safety through advanced driver assistance systems (ADAS) depends on the efficient combination of machine learning (ML) and computer vision (CV), but the quick development of algorithms makes it difficult to evaluate and use them in settings with limited resources. A thorough analysis of more than 80 important and current experiments is presented in this work, along with a structured taxonomy that groups ML and CV techniques based on how they are used in important ADAS tasks such driver monitoring, environmental perception, and vehicle control. Our synthesis highlights the feasibility of hybrid models that strike a balance between performance and efficiency. While deep learning models achieve state-of-the-art accuracy in perception tasks, their high computational demand frequently renders them impractical for real-time embedded systems. This analysis offers a strategic roadmap for future research targeted at creating more durable, dependable, and commercially scalable ADAS technology by highlighting important research needs in multi-sensor fusion, domain adaptation for unfavorable situations, and safety validation.
The application of machine learning and computer vision in athletics facilitates analytical tools for the triple jump and long jump, allowing for the monitoring of speed, posture, and the phases of take-off and landing. Prior research has focused on sports biomechanics; however, this work integrates computer vision and machine learning techniques for triple and long jump events. This study examines how sophisticated technology may transform conventional analytical methods concerning precision and efficiency in evaluating athletes' techniques. The investigation indicates that neural networks, RNNs, and CNNs surpassed traditional methods. This study examines the issues associated with advanced algorithms, including accuracy degradation with varying jumps, expenses related to video capture systems, and the ethical implications of contemporary technology. This work investigates computer vision and machine learning to improve athlete performance via comprehensive feedback, encompassing data acquisition through wearables and computer vision systems. This research facilitates the creation of prediction models for performance analysis and addresses dataset restrictions using machine learning approaches, including transfer learning. It examines AI-driven feedback mechanisms to enhance training efficiency. The research demonstrated that approach velocity directly affects leap distance. The categorization and forecasting of leaps using these techniques help coaches assess skills and adjust training regimens.
