Biomechanical Risk Assessment Research Articles

Criteria for Assessing Exposure to Biomechanical Risk Factors: A Research-to-Practice Guide—Part 1: General Issues and Manual Material Handling

Musculoskeletal disorders are the most prevalent occupational health problem all over the world and are often related to biomechanical risk factors; to control these risk factors, several assessment methods (mostly observational) have been proposed in the past 40 years. An in-depth knowledge of each method to evaluate biomechanical risk factors is needed to effectively employ them in the field, together with a robust understanding of their effective predictive value and limitations. In Part 1, some general issues relevant to biomechanical risk assessment are discussed, and the method for assessing manual material handling after receiving more robust validation data is reviewed (Revised NIOSH Lifting Equation), together with a discussion about variability of tasks. Similarly, for the assessment of the biomechanical exposure of the upper limb, the TLV for Hand activity (ACGIH®) is presented in Part 2 of this guide, together with criteria to proportion risk assessment to the working duration in part-time jobs.

Digital and Virtual Technologies for Work-Related Biomechanical Risk Assessment: A Scoping Review

Digital and Virtual Technologies for Work-Related Biomechanical Risk Assessment: A Scoping Review

Transoral laser exoscopic surgery of the larynx: state of the art and comparison with traditional transoral laser microsurgery.

To evaluate the efficacy of transoral laser exoscopic surgery (TOLES) in a unicentric series of patients affected by benign and malignant glottic and supraglottic lesions, and compare outcomes with those of transoral laser microsurgery (TOLMS). To demonstrate the non-inferiority of TOLES in terms of operative time, margin status and complication rates, we compared outcomes of 93 patients treated by TOLES between July 2021 and July 2023 with those of a match-paired group of 107 historical patients treated by TOLMS. To perform a multiparametric ergonomic evaluation of TOLES vs TOLMS, we used observational methods for biomechanical overload risk assessment and wearable technologies comparing 15 procedures with TOLES vs a paired match of 13 surgeries performed with TOLMS by the same surgeon. No significant differences were found in terms of surgical duration, positive margins, or complications between TOLES and TOLMS. Ergonomics assessment by inertial measurement units and electromyographic surface electrodes demonstrated a reduced biomechanical overload with TOLES compared to TOLMS. The many advantages of TOLES, such as its superior didactic value, better digital control of light even through small-bored laryngoscopes, improved binocular vision, and increase in surgical performance by 3 or 4-hand techniques, are difficult to be quantified. In contrast, its non-inferiority in terms of oncological results and better ergonomics compared to TOLMS are demonstrated herein.

Assessing Musculoskeletal Disorder Risks in an Automobile Part Manufacturing Factory: A Comparison Study of Biomechanical and Ergonomic Tools

Background: Various tools and methods are available to assess the risk of musculoskeletal injuries in occupational settings, each considering different assessment criteria. While these methods have been previously studied, further comparative investigations are still needed. Objectives: This study aimed to assess the risk of musculoskeletal disorders (MSDs) in workers performing various tasks using different ergonomic and biomechanical risk assessment tools and to compare the outcomes of these tools. Methods: This analytical and cross-sectional study was conducted in a sector of the SAIPA Automobile Part Manufacturing Factory in Iran. A total of 33 workers engaged in lifting, grinding, pushing, and lowering activities participated. Lifting activities involved handling parts weighing 36, 7.9, and 3.4 kg at three workstations. In total, six tasks were examined, and workers' discomfort levels were evaluated using the Cornell Musculoskeletal Discomfort Questionnaires (CMDQ). Fourteen biomechanical and ergonomic risk assessment tools were utilized and compared. Kendall's tau-b correlation coefficient and Cohen's kappa agreement coefficient were applied to determine relationships between different tools and to match the measurement of risk levels, respectively. Additionally, the mean and standard deviation of the data were calculated. Results: There was a high positive correlation between the outcomes of three biomechanical tools [3D static strength prediction programTM (3DSSPP), hand-calculation back compressive force (HCBCF), and JACK], with 3DSSPP and HCBCF being interchangeable for estimating lumbar spine loads. Ergonomic assessment tools showed a suitable correlation in assessing load-carrying tasks. Manual handling assessment charts (MAC) had perfect agreement with National Institute for Occupational Safety and Health (NIOSH), and SNOOK with Washington Industrial Safety and Health Act (WISHA). Among body posture evaluation methods, rapid upper limb assessment (RULA) and rapid entire body assessment (REBA) had the highest correlation. Lifting fatigue failure tool (LiFFT) correlated well with manual handling methods, and survival chance correlated better with body posture evaluation methods. Lowering activities were identified as the most hazardous. Conclusions: Ease of use and accuracy are key considerations when selecting biomechanical risk assessment tools. Experienced assessors can effectively utilize JACK, 3DSSPP, and HCBCF. The MAC and NIOSH, as well as RULA and REBA tools, are interchangeable. Selecting appropriate tools requires consideration of their limitations and advantages.

Adaptive Lifting Index (aLI) for Real-Time Instrumental Biomechanical Risk Assessment: Concepts, Mathematics, and First Experimental Results.

When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment.

SimMACT, a Software Demonstrator to Improve Maximum Actuation Joint Torques Simulation for Ergonomics Assessment.

The maximum actuation joint torques that operators can perform at the workplace are essential parameters for biomechanical risk assessment. However, workstation designers generally only have at their disposal the imprecise and sparse estimates of these quantities provided with digital manikin digital human model (DHM) software. For instance, such tools consider only static postures and ignore important specificities of the human musculoskeletal system such as interjoints couplings. To alleviate the weaknesses of existing approaches implemented in digital human modeling tools relying on torque databases, this paper describes a methodology based on a class of polytopes called zonotopes and musculoskeletal simulation to assess maximum actuation torques. It has two main advantages, the ability to estimate maximum joint torques for any posture and taking into account musculoskeletal specificities unlike existing digital human modeling tools. As a case study, it also compares simulated maximum actuation torques to those recorded during an experiment described in the literature, focusing on an isometric task of the upper limb. This simulation has led to similar or smaller errors than DHM software tools. Hence, this methodology may help in interpreting interjoint couplings, choosing appropriate mathematical models or design experimental protocols. It may also be implemented in DHM software to provide designers with more comprehensive and more reliable data.

Preliminary results of a biomechanical risk assessment of an actual industrial use-case executed with and without a dual-arm CoBot

Preliminary results of a biomechanical risk assessment of an actual industrial use-case executed with and without a dual-arm CoBot

A method for the risk assessment of biomechanical overload in hospital physiotherapists.

In physiotherapists, biomechanical overload risk assessment (RA) is particularly complex due to the tasks' variability. The present study aims to propose a new methodology, named Whole Body RA Biomechanical Overload (WB-RAMBO), to assess the risk in the activities performed by physiotherapists. Each type of intervention was broken down into elementary operations. The risk factors (force, repetitiveness, and incongruous postures) were recorded and evaluated for each of these. For each task, the risk level was obtained by integrating the results of multiple ergonomic methods among those proposed by the international literature. To verify and validate the obtained results, we reviewed the medical records of health surveillance carried out on physiotherapists. From the ergonomic point of view, RA shows a situation of acceptability. The observed slight dysergonomies are diluted in the work shift and allow an optimal functional recovery of the musculoskeletal system. This method proposes a RA for each operation performed. A work plan subjected to such a peculiar RA can be redesigned and adapted to the company's and the hypersusceptible worker's organizational needs.

Wearables for Biomechanical Performance Optimization and Risk Assessment in Industrial and Sports Applications.

Wearable technologies are emerging as a useful tool with many different applications. While these devices are worn on the human body and can capture numerous data types, this literature review focuses specifically on wearable use for performance enhancement and risk assessment in industrial- and sports-related biomechanical applications. Wearable devices such as exoskeletons, inertial measurement units (IMUs), force sensors, and surface electromyography (EMG) were identified as key technologies that can be used to aid health and safety professionals, ergonomists, and human factors practitioners improve user performance and monitor risk. IMU-based solutions were the most used wearable types in both sectors. Industry largely used biomechanical wearables to assess tasks and risks wholistically, which sports often considered the individual components of movement and performance. Availability, cost, and adoption remain common limitation issues across both sports and industrial applications.

Biomechanical overload risk assessment in Industry 4.0

Biomechanical overload risk assessment in Industry 4.0

The quest for a unified theory on biomechanical palm risk assessment through theoretical analysis and observation

Several methodologies related to the biomechanical risk assessment and the uprooting and breaking potential of palms are reviewed and evaluated in this study. Also a simple mathematical model was designed, to simulate the results of critical wind speed predictions for a tall coconut palm by using classic beam theory and Brazier buckling. First, the review presents arguments that assess the applicability of some influential claims and tree and palm risk assessment methods that have been amply marketed in the last 20 years. Then, the analysis goes beyond the classical procedures and theories that have influenced the arboricultural industry and related press so far. And afterwards, rationale behind several postulated ideas are presented, that are hoped to be fruitful in the path towards a new biomechanical theory for the biomechanical risk assessment of palms. The postulated model envisages the palm stem as a viscoelastic and hollow cylinder that is not only prone to buckling, ovalization and kinking, but also fatigue, shear, splitting and crack propagation. This envisaging was also the main reason why simple Brazier buckling formulation was experimentally applied to simulate the breaking risk of a cocostem. This study also enables a better understanding of the wide range of factors that may influence the mechanical behaviour of trees and palms under (wind) loading.

Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network.

The purpose of this study was to develop an approach to predict hand posture (pinch versus grip) and grasp force using forearm surface electromyography (sEMG) and artificial neural networks (ANNs) during tasks that varied repetition rate and duty cycle. Prior studies have used electromyography with machine learning models to predict grip force but relatively few studies have assessed whether both hand posture and force can be predicted, particularly at varying levels of duty cycle and repetition rate. Fourteen individuals participated in this experiment. sEMG data for five forearm muscles and force output data were collected. Calibration data (25, 50, 75, 100% of maximum voluntary contraction (MVC)) were used to train ANN models to predict hand posture (pinch versus grip) and force magnitude while performing tasks that varied load, repetition rate, and duty cycle. Across all participants, overall hand posture prediction accuracy was 79% (0.79 ± .08), whereas overall hand force prediction accuracy was 73% (0.73 ± .09). Accuracy ranged between 0.65 and 0.93 based on varying repetition rate and duty cycle. Hand posture and force prediction were possible using sEMG and ANNs, though there were important differences in the accuracy of predictions based on task characteristics including duty cycle and repetition rate. The results of this study could be applied to the development of a dosimeter used for distal upper extremity biomechanical exposure measurement, risk assessment, job (re)design, and return to work programs.

Technology-Enhanced Learning of Human Trauma Biomechanics in an Interprofessional Student Context

Phenomenon: This study aimed to investigate how students can develop their understanding of trauma biomechanics by means of technology-enhanced learning—an interactive visualization tool developed to enhance understanding of the biomechanics underlying an injury via dynamic imaging sequences. Approach: Students were invited to explore the content as a learning resource during an interprofessional clinical placement on an orthopedic ward. Thirty volunteer medical, nursing, and physiotherapy/occupational therapy students participated in 10 interprofessional groups of three participants. They were video recorded while interacting with learning software that was divided into five sections: Work Up, General Information, Biomechanical Case Study, Biomechanical Risk Assessment, and Treatment. Investigators probed students’ learning experiences via four focus group discussions. A sociomaterial perspective was adopted, directing the analytical focus to how students’ made use of talk, gestures, bodies, and material objects to understand the visualized phenomena. Findings: When connecting the visualization to a patient case, certain features of the technology stood out as important for promoting engagement and understanding trauma mechanisms. Decreased tempo, showing the directions and dynamics of trauma biomechanics in slow-motion, and color coding of the strain on the affected structures were especially important for evoking the emotional responses. The visualization tool also supported students’ explorations of causal relationships between external forces and their biomedical effects. These features emphasize the sociomaterial relation between the design of the technology and the student activities. Insights: Dynamic visualization of biomechanical events has the potential to improve the understanding of injury mechanisms and specifically to identify anatomical structures at high risk of injury. Dynamic visualizations for educational purposes seem to promote possibilities for learners to contextualize visual representations relative to one’s own body. Educational methods and practice need explicit attention and development in order to use the full potential of the visualization technology for learning for the health care professions.

Biomechanical Risk Assessment of Non-Contact Anterior Cruciate Ligament Injury in Taekwondo Athletes

Non-contact anterior cruciate ligament (ACL) injury can occur in many sports. It is interrelated with gender, anatomy, biomechanics, and neuromuscular control. Taekwondo athletes have a higher incidence of ACL injury than athletes from other sports. Objective: This study aimed to determine the biomechanical gender differences and mechanism of taekwondo athletes with ACL injury. Methods: A total of 28 taekwondo athletes (aged 14–19 years) were randomly selected and grouped by gender. Feet high floor, one foot high floor, and single leg squat were analyzed by a Vicon motion analysis system and Kistler 3D force platform for action. The knee joint angle and ground force were evaluated. Results: Results demonstrated biomechanical differences in knee joint between male and female athletes. Conclusion: ACL injury in taekwondo female athletes indicated the biomechanical mechanism of the knee joint, and it can be prevented by neuromuscular control training.

Predictors of Abdominal Aortic Aneurysm Risks.

Computational biomechanics via finite element analysis (FEA) has long promised a means of assessing patient-specific abdominal aortic aneurysm (AAA) rupture risk with greater efficacy than current clinically used size-based criteria. The pursuit stems from the notion that AAA rupture occurs when wall stress exceeds wall strength. Quantification of peak (maximum) wall stress (PWS) has been at the cornerstone of this research, with numerous studies having demonstrated that PWS better differentiates ruptured AAAs from non-ruptured AAAs. In contrast to wall stress models, which have become progressively more sophisticated, there has been relatively little progress in estimating patient-specific wall strength. This is because wall strength cannot be inferred non-invasively, and measurements from excised patient tissues show a large spectrum of wall strength values. In this review, we highlight studies that investigated the relationship between biomechanics and AAA rupture risk. We conclude that combining wall stress and wall strength approximations should provide better estimations of AAA rupture risk. However, before personalized biomechanical AAA risk assessment can become a reality, better methods for estimating patient-specific wall properties or surrogate markers of aortic wall degradation are needed. Artificial intelligence methods can be key in stratifying patients, leading to personalized AAA risk assessment.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship betweenWLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed.The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other liftingconditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this,we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtainedin three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within thelifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity centerfrequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LIand that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used formapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation duringthese tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associatedwith work activities can be integrated with methods already used for biomechanical risk assessment.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship between WLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed. The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other lifting conditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this, we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtained in three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within the lifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity center frequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LI and that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used for mapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation during these tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associate with work activities can be integrated with methods already used for biomechanical risk assessment.

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges-A Systematic Review.

Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use.

Lifting activity assessment using surface electromyographic features and neural networks

The surface electromyographic (sEMG) data of 12 trunk muscles of 10 workers during the execution of lifting tasks using three lifting indices (LI) were recorded. The aims of this work were to: 1) identify the most sensitive trunk muscles with respect to changes in lifting conditions based on the selected sEMG features and 2) test whether machine-learning techniques (artificial neural networks) used for mapping time and frequency sEMG features on LI levels can improve the biomechanical risk assessment. The results show that the erector spinae longissimus is the trunk muscle for which every sEMG feature can significantly discriminate each pair of LI. Furthermore, only when using multi-domain features (time and frequency) a more complex artificial neural network can lead to an improved biomechanical risk classification related to lifting tasks.

Observational methods for biomechanical risk assessment in workers: a systematic review

Abstract Introduction: Among the methods of measurement of the biomechanical risk factors available in the literature, the observational methods have greater applicability in occupational practice. Objective: To identify observational methods used in Brazilian workers to identify and to evaluate their translation/cross-cultural adaptation procedures and measuring property tests. Methods: Three search strategies were used in MEDLINE, EMBASE, CINAHL, LILACS and SCIELO. After a review of titles and abstracts, potential articles were read in full for inclusion and subsequent extraction of data related to translation, cross-cultural adaptation and measurement properties of the observational methods. Results: 5349 potential studies were found and 29 were eligible for inclusion. The methods used in Brazilian workers were: AET, NIOSH, OCRA, OWAS, QEC, RARME, REBA and RULA. All procedures regarding the translation and cross-cultural adaptation were positive for the QEC and REBA. The translation, synthesis of the translations and review committee procedures were doubtful for the OCRA method. The QEC measuring properties showed negative reliability, doubtful internal consistency, and positive agreement and construct validity. The REBA showed negative reliability and agreement. The RARME presented positive reliability and negative construct validity. Conclusion: For most observational methods used in Brazilian workers, the translation and cross-cultural adaptation procedures were not performed and their measurement properties were not performed, highlighting the need to perform these procedures before using them.