Predicting occupational injury causal factors using text-based analytics : A systematic review

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research

Children’s Physiological and Perceptual Responses to Sports Exergames When Played in Different Positions

Today’s children are prone to becoming involved in exergames, but their positions during play have not been sufficiently investigated to determine whether the positions they adopt result in equal responses. The design of this study involved the collection of physiological and perceptual responses (i.e., heart rate (HR), rating of perceived exertion, and enjoyment score) during exergames in three different sports (bowling, tennis, and boxing) with players in different positions (sitting and standing). The participants played each game for 10 min while their HR was recorded. After the gameplay, each perceptual response was retrieved. The results revealed a significant increase in HR above rest during exergaming overall (p p p p p > 0.5). For all the variables, no statistically significant differences between genders were identified (p > 0.5). This home-based intervention demonstrated that sports exergames are not only enjoyable; overall, they can provide at least moderately intense physical activity, whether played seated or standing

Foot over pronation problem among undergraduate students: a preliminary study

Over pronation is a dysfunctional movement where the foot has turned in excessively from its neutral line and can lead to misalignment of the foot and leg in humans. The purposes of this study are to investigate the ankle biomechanics behavior in individuals among the undergraduate students with over pronation foot and provide guidelines to help correct the foot deformities. 10 subjects with over pronated foot where volunteer but only 7 pass the selection test and divided into two group normal subjects (n=2) and over pronated subjects (n=5). Vicon motion analysis was used to observe and analyze the gait cycle and the ankle range of motion in individuals with over pronation. The study found that the ankle joint during the initial contact was below 5° for all subjects. Subject 2 shows the lowest ankle angle during initial contact while for mid stance phase, subject 3 shows the highest ankle angle which was 24.15° on left foot and 28.30° on right foot. From the ANOVA test, the p value for ankle joint angle was less than 0.05, which indicates that there was significant difference between all the subjects. The ankle angle depended on the muscle movement as the muscles and ligaments tried to stabilize and move the foot by controlling the angle to make sure the foot is in correct position and can move forward. As conclusion, there are significant differences for ankle behavior between normal and over pronated subjects, thus proper guideline for exercise or treatment can help to overcome this problem

Development of 3-Dimensional Model of Femur Bone Considering Cortical and Cancellous Structures

Femur bone is commonly used for various experimental and computer simulation works in multidiscipline research. Various techniques are applied prior to mimic the actual bone properties. In order to perform any research related to human bone, some issues need to take into account such as cost, ethical concern and limited bone sample availability. Experimental test and computer simulation related to femur bone model commonly executed using hollow cylinder, solid rod or beam elements instead of real anatomy. The aims of present study is to provide 3D femur bone model construction considering both cortical and cancellous structures utilising only one software approach. The constructed model could be utilised for various research purposes such as computer simulation, 3D print of bone model and experimental test. Complete femur bone model which include proximal, shaft and distal condyles is successfully constructed and ready to be used for further investigation. Mimics software was the only software used in present study to performed overall tas

Biomechanical analysis of an improvement of prosthetic liner using polyurethane focusing at the anterior-distal part of residual limb : a case study

Most transtibial prosthesis users always experience pain sensation at the distal of the residual limb due to bony prominences and nerve endings. Many initiatives have been taken to resolve this problem, including using softer materials such as silicone or gel liner and designing a distal off load prosthetic socket. Another promising approach is to incorporate polyurethane foam in the manufacturing of prosthetic liner. This study aimed to design a new prosthetic liner using polyurethane at the anterior-distal part of the residual limb as a Pelite replacement and to compare the biomechanical gait analysis between the new modified polyurethane liner and the common Pelite liner. A unilateral transtibial amputee was recruited as the subject. Two Patellar Tendon Bearing transtibial prostheses with different liners were fabricated for the subject, which were Pelite liner and a modified polyurethane foam liner. The modified liner using polyurethane foam consisted of Ethylene vinyl-acetate – Polyurethane – Ethylene vinyl-acetate sandwich placed at the anterior-distal part of the residual limb. The Ethylene vinyl-acetate – Polyurethane – Ethylene vinyl-acetate sandwich function was to improve the walking gait and compensate for the pain sensation experienced by the subject when wearing the Pelite liner. Biomechanical analysis was done using the Vicon Motion Analysis System on the subject when using the two newly fabricated transtibial prostheses and the subject’s original prosthesis with Pelite liner. During the loading response phase, the original liner exerted a slightly higher force than the Pelite and the modified liner. At 30% and 50% of the gait cycle, the original liner exerted low force than the Pelite liner and the modified liner for Ground Reaction Force at the amputated side. However, no significant difference (p>0.05) was found between all prosthetic liners for Ground Reaction Force (Non-Amputated). The biomechanical analysis showed that the modified liner using polyurethane foam improved the prosthesis user gait cycle and the walking gait of the prosthesis user

Contributions of the Cybathlon championship to the literature on functional electrical stimulation cycling among individuals with spinal cord injury: A bibliometric review

Background: Due to its clinically proven safety and health benefits, functional electrical stimulation (FES) cycling has become a popular exercise modality for individuals with spinal cord injury (SCI). Since its inception in 2013, the Cybathlon championship has been a platform for publicizing the potential of FES cycling in rehabilitation and exercise for individuals with SCI. This study aimed to evaluate the contribution of the Cybathlon championship to the literature on FES cycling for individuals with SCI 3 years pre and post the staging of the Cybathlon championship in 2016. Methods: Web of Science, Scopus, ScienceDirect, IEEE Xplore, and Google Scholar databases were searched for relevant studies published between January 2013 and July 2019. The quality of the included studies was objectively evaluated using the Downs and Black checklist. Results: A total of 129 articles on FES cycling were retained for analysis. A total of 51 articles related to Cybathlon were reviewed, and 14 articles were ultimately evaluated for the quality. In 2017, the year following the Cybathlon championship, Web of Science cited 23 published studies on the championship, which was almost 5-fold more than that in 2016 (n = 5). Training was most often reported as a topic of interest in these studies, which mostly (76.7%) highlighted the training parameters of interest to participating teams in their effort to maximize their FES cycling performance during the Cybathlon championship. Conclusion: The present study indicates that the Cybathlon championship in 2016 contributed to the number of literature published in 2017 on FES cycling for individuals with SCI. This finding may contribute to the lessons that can be learned from participation in the Cybathlon and potentially provide additional insights into research in the field of race-based FES cycling

Nanoscale bioactive glass/injectable hydrogel composites for biomedical applications

The advances in nanotechnology have revolutionized the field of biomedical research by overcoming the shortcomings of conventional micron-scale biomaterials. Nanoscale bioactive glasses (NBGs) with higher surface reactivity as compared to the micro-sized bioactive glasses have a faster rate of dissolution and ion release in the physiological environments, and therefore, superior bioactivity and in turn tissue regenerative properties are expected. The incorporation of NBGs into polymeric hydrogels has shown to be a valuable strategy to take benefit from the inherent properties of both materials and to obtain multifunctional nanocomposite (NC) hydrogels suitable for diverse biomedical applications (e.g., tissue repair and regeneration). Such NC hydrogel biomaterials are highlighted in this chapter, in the context of tissue engineering

Harnessing the Multimodal Data Integration and Deep Learning for Occupational Injury Severity Prediction

Most previous studies have neglected the potential of integrating structured data and unstructured workplace injury reports to perform a predictive analysis of occupational injury severity. This study proposes an optimized integrated approach for occupational injury severity prediction using multimodal machine and deep learning techniques. We used 66,405 data points gathered from the US OSHA Severe Injury Reports from January 2015 to July 2021. Structured labeled data are preprocessed and normalized, whereas unstructured injury reports undergo text cleaning using Natural Language Processing techniques and text representation using Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) to convert them into numerical representations. Both modalities, in the form of vector representations, were concatenated and fed as input features for the proposed models. Seven sets of classifiers, namely Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Long Short-Term Memory, and Bidirectional Long Short-Term Memory, were employed to learn the multimodal representations. The algorithm with superior performance was further optimized using the proposed feature importance and hyperparameter optimization techniques. Our findings revealed that the proposed optimized-Bi-LSTM architecture outperformed other classifiers in learning multimodal data to predict the likelihood of hospitalization and amputation with higher accuracies of 0.93 and 0.99, respectively. Consequently, the proposed approach enhances the performance by significantly improving the model processing time. This performance prediction provides a convincing benchmark for the successful execution of multimodal deep learning in occupational injury research. Therefore, the proposed multimodal occupational injury severity prediction model enhances the early screening and identification of at-risk workers with severe occupational injury outcomes, as well as, provides valuable information to improve the workplace safety, health, and well-being of the workers

Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records

Medical device failure and maintenance records are essential information, as some nations lack dedicated systems for capturing this valuable data. In addition to making healthcare more intelligent and individualized, machine learning has the potential to transform the conventional healthcare system. Optimizing AI models in decision-making could mitigate the effects of three research issues: malfunctioning medical devices, high maintenance costs, and the lack of a strategic maintenance framework. This study proposes a data-driven machine-learning model for predicting medical device failure. The proposed predictive model is developed using multimodal data of structured maintenance and unstructured text narrative of maintenance reports to predict the failure of 8,294 critical medical devices. In developing the model, 44 varieties of essential medical devices from 15 healthcare institutions in Malaysia are utilized. A classification problem is addressed by classifying failure into three prediction classes: (i) class 1, unlikely to fail within the first three years, (ii) class 2, likely to fail within three years; and (iii) class 3, likely to fail after three years from the date of commissioning. The topic modelling and synthesis strategy: Latent Dirichlet Allocation is applied to unstructured data in order to uncover concealed patterns in maintenance notes captured during failures. In addition, sensitivity analysis is performed to select only the most significant parameters affecting the failure performance of the medical device. Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. Based on the performance evaluation, the Ensemble Classifier is further optimized and demonstrates improved accuracy of 88.80%, specificity of 94.41%, recall of 88.82%, precision of 88.46%, and F1 Score of 88.84%. The study proves a reduction in intervention from 18 to 8 features and a reduction in training time from 1660.5 to 901.66 seconds for comprehensive model development

Investigation of EMG Parameters for Transtibial Amputees While Treadmill Walking with Different Speeds: A Preliminary Study

Electromyography (EMG) is the process of acquiring electrical signals generated through muscle activity (contraction/relaxation). Surface EMG deliberates the amount of electrical activity in the musculoskeletal system in a non-invasive way. Under specific conditions and during certain motor activities, this signal is substantially associated with muscle strength. These Signals are used as Control Inputs by assistive devices. The study aimed to investigate the EMG parameters of lower limb muscles (rectus femoris and biceps femoris) in healthy individuals and transtibial amputees walking on a treadmill at different speeds (0.55 m/s, 0.83 m/s, and 1.11 m/s). Ten non-amputee and two amputee subjects participated. Findings reveal significant reductions in EMG signals at slower speeds, emphasizing foot stability. The right biceps femoris exhibits the highest signals average, while the right rectus femoris has the lowest for amputees. The male participants’ right biceps femoris muscle showed the greatest signals of average treadmill walking activity at 0,55 m/s (0.0014 V) compared to the amputee individuals’ (0.001 V). At (0,83 m/s), male participants (0.0015 V) outperformed amputee subjects (0.0004 V). At (1,11 m/s), male participants (0.0024 V) outperformed amputee subjects (0.001 V). Male participants consistently outperform amputees across speeds. The study suggests the potential application of findings in rehabilitating transtibial amputees on a treadmill, considering distance and maximum speed with a prosthesis. Overall, slow walking pace impacts EMG signals, providing insights for clinicians developing interventions for amputee rehabilitation