Augmented Human Research最新文献

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%, surpassing existing models such as VGG16 and DenseNet121 by 6–11%. It also demonstrated a high F1-score, along with individual precision, and recall for each class, despite a few misclassified data. The research elucidates the superiority of SimCLR and ResNet-50 compared to alternative models, with the primary objective of enhancing the dependability of non-invasive species monitoring, even in varying lighting and environmental conditions. These enhancements can fortify conservation planning and facilitate superior management. The research elaborates on the various constraints and challenges that may emerge when using the methodologies and algorithms in wildlife studies, including computational expenses and fluctuating environmental circumstances. The research has addressed several constraints related to underrepresented species, environmental variables like lighting and occlusion, and dependence on visual modalities, as well as prospects.

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.

Mixed reality (MR) integrated with human–robot teaming (HRT) has emerged as a promising approach to address persistent challenges in work health and safety (WHS) within manufacturing. To evaluate its potential, we conducted a systematic review of 33 peer-reviewed studies published between 2015 and 2024, identified from databases indexed in Google Scholar (e.g., IEEE, Elsevier, Springer, MDPI). Studies were screened using predefined inclusion and exclusion criteria, and quality was appraised with the Mixed-Methods Appraisal Tool (MMAT). The synthesis highlights three major applications of MR in HRT for WHS: immersive training and ergonomic assessment, real-time hazard monitoring and visualization, and enhanced human–robot communication via intuitive interfaces and natural language processing. Reported benefits include faster skill acquisition, improved situational awareness, and reduced accident risks. However, key barriers remain—particularly cognitive overload, ergonomic discomfort, integration with legacy manufacturing systems, and limited longitudinal evidence. Despite these challenges, the review demonstrates that MR–HRT solutions can significantly strengthen WHS outcomes if designed with ergonomic validation, adaptive feedback mechanisms, and scalable deployment strategies. For manufacturing industries, the findings provide a practical roadmap: prioritize user-centered MR design, invest in real-world pilot implementations, and embed WHS outcomes into technology evaluation. Advancing MR–HRT beyond proof-of-concept will require interdisciplinary collaboration and rigorous validation, enabling safer, smarter, and more resilient manufacturing environments.

Conventional 2D imaging in orthopedic trauma surgery lacks depth and requires attention shifts away from the operative field. Head-mounted augmented reality (AR HMDs) may improve intra-operative visualization by overlaying 3D holograms in the field of view. However, clinical evaluations focusing on surgeon experience remain limited. This study aimed to evaluate the usability and surgeon experience with AR HMD during intra-articular fracture surgery. A prospective single-center case series was conducted with ten orthopedic trauma surgeons who each completed a preclinical simulator session and then used a Microsoft HoloLens 2 to visualize patient-specific 3D models during 20 open reduction and internal fixation procedures. Outcomes: Simulator Sickness Questionnaire (SSQ; primary), Borg CR10 physical exertion, NASA-TLX mental workload, System Usability Scale (SUS), and a feasibility questionnaire. Across 20 procedures, SSQ indicated symptoms ranging from minimal to significant (preclinical mean 12.7, SD 16.2; intra-operative/postoperative mean 22.0, SD 20.7). Physical exertion was very low (Borg CR10 median 1.0, P₂₅–P₇₅ 0–1). Mental demand was medium (NASA-TLX mean 23.0, SD 21.9). Usability was rated good (SUS mean 69.3, SD 14.0). Surgeons judged potential utility highest for complex trauma, revision cases, and osteotomies (feasibility means 73.0, 73.0, and 68.0, respectively). Overall satisfaction averaged 62.0 (SD 27.5), and willingness to reuse was high (median 80, P₂₅–P₇₅ 55–87). Common challenges were gesture control and hologram positioning. Intra-operative AR use was feasible, with low physical exertion, medium mental demand, and good perceived usability, although simulator sickness symptoms were reported. Surgeons expressed willingness to reuse the system and identified greatest value in complex articular reconstructions. Findings reflect a small, single-center prospective case study using one AR HMD model and did not assess patient outcomes. Results support further multicenter evaluations focused on ergonomics, interaction design, workflow integration, and clinical impact.

Healthcare is experiencing rapid advancements due to the integration of virtual reality (VR), a computer-generated simulation that uses technology to generate an artificial environment; augmented reality (AR), a technology that augments the physical environment by superimposing digital content onto the actual world; and artificial intelligence (AI), which enables personalized diagnostics, immersive training, and improved patient care. This comprehensive review identified 1,075 records by conducting a search of Scopus, PubMed, and Science Direct for studies published from 2019 to 2024. A total of 37 research studies were evaluated following a thorough screening process that included the application of eligibility and exclusion criteria and the removal of duplicated studies following PRISMA regulations. The main findings indicate a significant increase in the overall number of publications, with the USA and the UK accounting for 51.3% of all publications because of their robust research machinery. Countries like Korea, Turkey, Australia, and Italy represented an overall 5.4 to 10.8%. The result of this review could be applied predominantly in telemedicine, educational institutions, rehabilitation, and surgical procedures, resulting in enhanced interaction between patients and operational precision. In summary, although VR, AR, and AI improve health-related education, therapy, and care, they need to tackle limitations such as expenditures and limitations in technology to achieve widespread adoption.

Today, archery is used in sports, hunting, recreational shooting, movies, etc. Traditional sights can lose alignment due to vibration and require tools for zeroing, are time-consuming for adjustment, difficult to see in certain backgrounds, and are affected by parallax. To overcome these challenges, a computer vision-based aiming application for smartphones was developed. The features of this application are digital aiming, zeroing, and arrow drop zeroing without any tools, background-based reticle color inversion, sensor-based incline level indicator, zoom, and zeroed distance autosave. These features aim to improve visibility, ease of adjustment, and aiming without any additional cost. Next, the performance of the sight system was tested by an archer firing arrows at a 50 cm target. A total number of 37 shots were fired outside on 3 days, early morning. By using the new sight, a mean absolute error of 10.85 on day 1, 7.18 on day 2, and 6.25 on day 3 was obtained. The study was limited by a small sample size due to difficulty in finding another skilled archer, as archery is not a common sport and has a huge learning curve. The current study identifies the practicality and efficiency of computer vision-based augmentation, like digital aiming, fast zeroing, and better visibility. Additionally, in future, other studies can work on the use of AI, ML, and sensor-based wind direction prediction in a smartphone application.

This study investigates the effects of temperature and humidity on the subjective characteristics of wine through evaluations in two distinct environments: an artificial climate chamber and a conference room. Two wines, wine 1(CROIX DE BEAUCAILLOU 2011) and wine 2(BLAGNY 1ER CRU LA PIECE SOUS LE BOIS 2014), were analyzed. Significant differences in color intensity, aging degree, and body were observed for wine 1 in the artificial climate room, and in flavor intensity in the conference room. For wine 2, significant differences were detected in flavor intensity and aging degree in the artificial climate room. Additionally, the composition changes of the wines concerning temperature were examined, revealing correlations between specific acids and temperature changes. These findings indicate that wine taste can be optimized by adjusting environmental conditions based on wine type and personal preferences, suggesting the potential for climate-controlled environments in enhancing wine and food experiences in restaurants.