The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.

Background: Agriculture involves several harmful diseases. Among the non-fatal ones, musculoskeletal disorders (MSDs) are the most prevalent, as they have reached epidemic proportions. The main aim of this investigation is to systematically review the major risk factors regarding MSDs as well as evaluate the existing ergonomic interventions. Methods: The search engines of Google Scholar, PubMed, Scopus, and ScienceDirect were used to identify relevant articles during the last decade. The imposed exclusive criteria assured the accuracy and current progress in this field. Results: It was concluded that MSDs affect both developed and developing countries, thus justifying the existing global concern. Overall, the most commonly studied task was harvesting, followed by load carrying, pruning, planting, and other ordinary manual operations. Repetitive movements in awkward postures, such as stooping and kneeling; individual characteristics; as well as improper tool design were observed to contribute to the pathogenesis of MSDs. Furthermore, low back disorders were reported as the main disorder. Conclusions: The present ergonomic interventions seem to attenuate the MSDs to a great extent. However, international reprioritization of the safety and health measures is required in agriculture along with increase of the awareness of the risk factors related to MSDs.

Background: Musculoskeletal disorders (MSDs) have long been recognized as the most common risks that operation of agricultural machineries poses, thus, undermining the ability to labor and quality of life. The purpose of this investigation was to thoroughly review the recent scholarly literature on ergonomics in agricultural mechanized operations; Methods: Electronic database research over the last ten years was conducted based on specific inclusion criteria. Furthermore, an assessment of the methodological quality and strength of evidence of potential risk factors causing MSDs was performed; Results: The results demonstrated that ergonomics in agriculture is an interdisciplinary topic and concerns both developed and developing countries. The machines with driving seats seem to be associated with painful disorders of the low back, while handheld machines with disorders of the upper extremities. The main roots of these disorders are the whole-body vibration (WBV) and hand-arm transmitted vibration (HATV). However, personal characteristics, awkward postures, mechanical shocks and seat discomfort were also recognized to cause MSDs; Conclusions: The present ergonomic interventions aim mainly at damping of vibrations and improving the comfort of operator. Nevertheless, more collaborative efforts among physicians, ergonomists, engineers and manufacturers are required in terms of both creating new ergonomic technologies and increasing the awareness of workers for the involved risk factors.

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

The anterior cruciate ligament (ACL) constitutes one of the most important stabilizing tissues of the knee joint whose rapture is very prevalent. ACL reconstruction (ACLR) from a graft is a surgery which yields the best outcome. Taking into account the complicated nature of this operation and the high cost of experiments, finite element (FE) simulations can become a valuable tool for evaluating the surgery in a pre-clinical setting. The present study summarizes, for the first time, the current advancement in ACLR in both clinical and computational level. It also emphasizes on the material modeling and properties of the most popular grafts as well as modeling of different surgery techniques. It can be concluded that more effort is needed to be put toward more realistic simulation of the surgery, including also the use of two bundles for graft representation, graft pretension and artificial grafts. Furthermore, muscles and synovial fluid need to be included, while patellofemoral joint is an important bone that is rarely used. More realistic models are also required for soft tissues, as most articles used isotropic linear elastic models and springs. In summary, accurate and realistic FE analysis in conjunction with multidisciplinary collaboration could contribute to ACLR improvement provided that several important aspects are carefully considered.

Understanding the performance indicators that contribute to the final score of a football match is crucial for directing the training process towards specific goals. This paper presents a pipeline for identifying key team-level performance variables in football using explainable ML techniques. The input data includes various team-specific features such as ball possession and pass behaviors, with the target output being the average scoring performance of each team over a season. The pipeline includes data preprocessing, sequential forward feature selection, model training, prediction, and explainability using SHapley Additive exPlanations (SHAP). Results show that 14 variables have the greatest contribution to the outcome of a match, with 12 having a positive effect and 2 having a negative effect. The study also identified the importance of certain performance indicators, such as shots, chances, passing, and ball possession, to the final score. This pipeline provides valuable insights for coaches and sports analysts to understand which aspects of a team’s performance need improvement and enable targeted interventions to improve performance. The use of explainable ML techniques allows for a deeper understanding of the factors contributing to the predicted average team score performance.