Workers' biomechanical loads and kinematics during multiple-task manual material handling

Coupling analysis of crane accident risks based on Bayesian network and the N-K model

Crane usage is pervasive on construction sites, however, it is associated with a notably high accident rate. The analyzing of crane accident risks is essential for accident prevention, control, and ensuring the safety of lifting operations. Hence, significant emphasis should be placed on understanding the interaction among various risk factors. This paper proposes a quantitative coupling method for human, machine, management, and environmental risk factors in crane accidents, leveraging Bayesian networks (BN) and the N-K model. Firstly, text mining technology and fault tree analysis are employed to analyze the causes of crane accidents and categorize the associate risk factors. Secondly, the types of risk coupling resulting from human, machine, management, and environmental risk factors are defined. Thirdly, the BN model is developed based on the analysis of crane accident risksand its N-K model. Fourthly, the parameters of the risk coupling nodes in the developed BN are determined based on the calculation results of the N-K model. Finally, for the risk coupling types with high coupling values and the first-level node and second-level node, the failure probability is analyzed through posterior probability and sensitivity analysis. The results indicate that factors related to man and management significantly impact crane accidents and warrant enhanced attention. The interplay among multiple risk factors significantly influences the probability of crane accidents, necessitating careful attention.

Multi-phase optimisation model predicts manual lifting motions with less reliance on experiment-based posture data

Optimisation-based predictive models are widely-used to explore the lifting strategies. Existing models incorporated empirical subject-specific posture constraints to improve the prediction accuracy. However, over-reliance on these constraints limits the application of predictive models. This paper proposed a multi-phase optimisation method (MPOM) for two-dimensional sagittally symmetric semi-squat lifting prediction, which decomposes the complete lifting task into three phases-the initial posture, the final posture, and the dynamic lifting phase. The first two phases are predicted with force- and stability-related strategies, and the last phase is predicted with a smoothing-related objective. Box-lifting motions of different box initial heights were collected for validation. The results show that MPOM has better or similar accuracy than the traditional single-phase optimisation (SPOM) of minimum muscular utilisation ratio, and MPOM reduces the reliance on experimental data. MPOM offers the opportunity to improve accuracy at the expense of efforts to determine appropriate weightings in the posture prediction phases. Practitioner summary: Lifting optimisation models are useful to predict and explore the human motion strategies. Existing models rely on empirical subject-specific posture constraints, which limit their applications. A multi-phase model for lifting motion prediction was constructed. This model could accurately predict 2D lifting motions with less reliance on these constraints.

Multisensory Evaluation of Muscle Activity and Human Manipulability during Upper Limb Motor Tasks

In this work, we evaluate the relationship between human manipulability indices obtained from motion sensing cameras and a variety of muscular factors extracted from surface electromyography (sEMG) signals from the upper limb during specific movements that include the shoulder, elbow and wrist joints. The results show specific links between upper limb movements and manipulability, revealing that extreme poses show less manipulability, i.e., when the arms are fully extended or fully flexed. However, there is not a clear correlation between the sEMG signals' average activity and manipulability factors, which suggests that muscular activity is, at least, only indirectly related to human pose singularities. A possible means to infer these correlations, if any, would be the use of advanced deep learning techniques. We also analyze a set of EMG metrics that give insights into how muscular effort is distributed during the exercises. This set of metrics could be used to obtain good indicators for the quantitative evaluation of sequences of movements according to the milestones of a rehabilitation therapy or to plan more ergonomic and bearable movement phases in a working task.

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.

Investigating Glenohumeral Joint Contact Forces and Kinematics in Different Keyboard and Monitor Setups using Opensim

Background

The musculoskeletal complaints of the shoulder are prevalent in people who work with computers for a long time.

Objective

This study aimed to investigate the glenohumeral joint contact forces and kinematics in different keyboards and monitor setups using OpenSim.

Material and Methods

Twelve randomly selected healthy males participated in an experimental study. A 3×3 factorial design was used in which three angles were considered for the monitor and three horizontal distances for the keyboard while performing standard tasks. The workstation was adjusted based on ANSI/HFES-100-2007 standard to maintain a comfortable ergonomic posture for controlling confounding variables. Qualisys motion capture system and OpenSim were used.

Results

The maximum mean range of motion (ROM) of both shoulders' flexion and adduction was observed when the keyboard was 15 cm from the edge of the desk, and the monitor angle was 30°. The maximum mean ROM of both shoulders' internal rotation was recorded for the keyboard at the edge of the desk. Peak forces for most right shoulder complex muscles were obtained in two setups. 3D shoulder joint moments were significantly different among nine setups (P-value<0.05). The peak anteroposterior and mediolateral joint contact forces were recorded for the keyboard at 15 cm and the monitor at zero angles (0.751 and 0.780 N/BW, respectively). The peak vertical joint contact force was observed for the keyboard at 15 cm and the monitor at 15° (0.310 N/BW).

Conclusion

The glenohumeral joint contact forces are minimum for the keyboard at 8 cm and the monitor at zero angles.

A Feasibility Study on the Conversion from Manual to Semi-Automatic Material Handling in an Oil and Gas Service Company

In manufacturing companies, manual material handling (MMH) involves lifting, pushing, pulling, carrying, moving, and lowering objects, which can lead to musculoskeletal disorders (MSDs) among workers, resulting in high labor costs due to excessive overtime incurred for manual product preparation. The aim of this study was to show how ergonomic measures were used to reduce the risk of MSDs and to reduce operating costs in the warehouse department of an oil and gas service company. A preliminary study using the Nordic Body Map survey showed that the workers experienced pain in various parts of the body, indicating the presence of MSDs. The researchers then used methods such as the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and National Institute for Occupational Safety and Health (NIOSH) assessments to verify whether the MMH activities had an acceptable level of risk. The results revealed that certain manual material handling (MMH) activities were assessed as low–very high risk, with RULA scores ranging from 3 to 7 and REBA scores ranging from 4 to 11. An immediate solution was to replace the manual process with a semi-automatic process using a vacuum lifter. A feasibility study was conducted using the net present value (NPV), internal rate of return (IRR), and payback period to justify the economic viability of the solution. The analysis indicated that implementing the vacuum lifter not only mitigated the risk of MSDs but also reduced the operating costs, demonstrating its viability and profitability. Overall, this study suggests that implementing a vacuum lifter as an assistive device in the warehouse would be a beneficial investment for both the workers and the company, improving both well-being and finances.

Why multi-tier surgical instrument table matters? An ergonomic analysis from mento-physical demand perspectives

Using traditional back tables (BT) in operating rooms (OR) can lead to high physical/cognitive demand on nurses due to repetitive manual material handling activities. A multi-tier table (MTT) has been developed to relieve such stressors by providing extra working surfaces to avoid stacking the instrument trays and facilitate access to surgical tools. In this study, sixteen participants performed lifting/lowering and instrument findings tasks on each table, where kinematics, kinetics, subjective, and performance-related measures were recorded. Results indicated that MTT required lesser shoulder flexion (p-value<0.001), ∼14% lower shoulder loads (0.012), task completion time (<0.001), and cognitive/physical workloads (<0.004). Although peak low-back demands were ∼15% higher using MTT, the number of lifts to complete the same task was 60% lower, leading to lower cumulative demand on the low-back musculature. Utilizing MTT in OR could reduce demand and increase nurses' efficiency, leading to reduced risk of WMSDs and the total time of surgery.

Motion envelopes: unfolding longitudinal rotation data from walking stick-figures

This work presents Motion Envelopes (ME), a simple method to estimate the missing longitudinal rotations of minimal stick figures, which is based on the spatial-temporal surface traced by line segments that connect contiguous pairs of joints. We validate ME by analyzing the gait patterns of 6 healthy subjects, comprising a total of 18 gait cycles. A strong correlation between experimental and estimated data was obtained for lower limbs and upper arms, indicating that ME can predict their longitudinal orientation in normal gait, hence, ME can be used to complement the kinematic information of stick figures whenever it is incomplete.

A Review on Ergonomics in Agriculture. Part I: Manual Operations

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.

Effects of Ergomotor Intervention on Improving Occupational Health in Workers with Work-Related Neck-Shoulder Pain

(1) Background: Work-related neck and shoulder pain (WRNSP) are common problems, and past occupational research has focused on ergonomic interventions such as adjusting workstations while physiotherapists have traditionally focused on teaching exercises to improve posture and movement control in the clinical setting. The current study aimed to integrate these two approaches and evaluate the immediate and long-term effects of such interventions on occupational exposure outcomes. (2) Methods: A total of 101 patients diagnosed with WRNSP were randomized into 2 groups: Control (CO) group (n = 50) and ergomotor (EM) group (n = 51). Participants in the control group had 12 weeks of usual care (conventional physiotherapy) while participants in the EM group received an integrated program with tailor-made motor control training and ergonomic advice for 12 weeks. (3) Results: Both groups achieved significant improvement in pain and functional outcomes at post-intervention. The EM group also reported significantly improved scores in terms of perceived exertion in the job-related physical demands (JRPD) and the short form workstyle questionnaires compared to the control group. (4) Conclusions: The results suggest that ergomotor intervention may be more effective in producing favorable occupational health outcomes compared to conventional physiotherapy.