Xiaoxu Ji

The Revised NIOSH Lifting Equation and the screening practices built around it interpret lumbar injury risk primarily through axial compression, yet anterior–posterior shear is associated with injury at loads below established compressive thresholds. L4 and L5 as are henceforth defined as the 4th and 5th lumbar vertebrae Using a harmonized database of 106 peak-loading lifting postures reconstructed through digital human modeling across five studies, we developed parallel families of linear mixed-effects regression models for L4/L5 compression and shear that shared identical predictor sets and matched random-effects structures, isolating biomechanical differences from differences in statistical specification. Load magnitude and horizontal hand reach acted as universal drivers of both outcomes—positive, highly significant, and stable across model reduction—although compression was several times more sensitive in absolute terms. The two components diverged in their posture sensitivities: knee flexion was significant only for shear, whereas trunk flexion, shoulder flexion, and vertical hand position acted predominantly on compression. Critically, no predictor significant in both families reversed sign, indicating that posture selectively modulates two independently sourced quantities—the moment arm of the supported mass, which governs compression, and the orientation of the lumbar segment, which governs shear—rather than redistributing a single conserved spinal reaction. Between-subject variability consistently exceeded between-task variability and persisted across model reduction, identifying individual lifting strategy as a structural determinant of spinal exposure. Among the 102 observations below the 3.4 kN compression criterion, 11 (10.8%) exceeded the lower 700 N shear reference, although none exceeded the upper 1000 N reference. Together these findings give a quantitative rationale for regarding compression-only assessment as structurally incomplete and support a multidimensional, posture-aware approach to ergonomic risk evaluation.

The NIOSH Lifting Equation is widely used to evaluate manual material handling tasks by identifying lifting risk through load reduction multipliers based on task geometry. However, while it provides a means of classifying risk, it does not estimate spinal forces and therefore cannot quantify the biomechanical load experienced at the lumbar spine. In contrast, biomechanical simulations can estimate spinal compression with high fidelity, but they require motion capture systems and specialized software that are not practical for most workplace assessments. This study aimed to bridge these approaches by developing a family of mixed-effect statistical models that predict L4/L5 spinal compression forces using the geometric parameters of the NIOSH framework combined with posture-specific biomechanical descriptors at peak-loading poses extracted from digital simulations. Data were aggregated from multiple experimental lifting studies in which standardized NIOSH parameters and corresponding spinal compression forces were obtained through validated digital human modeling. Mixed-effects regression was used to establish the relationship between task geometry, joint posture, load weight, and spinal compression. The resulting predictive equation demonstrated strong agreement with simulation-derived forces and effectively captured the contributions of subject, task and body positioning to the spinal compression force across diverse lifting tasks. Importantly, the variance structure of the model’s coefficients allows the contribution of risk to be attributed either to task-related factors or to subject-specific movement behaviors, reinforcing the safety relevance of the framework. Horizontal reach, vertical hand height, and load magnitude emerged as dominant predictors, with trunk posture providing additional explanatory power. The model offers ergonomists a practical, biomechanics-informed tool that extends the descriptive capacity of the NIOSH framework by enabling direct estimation of spinal compression forces without the need for full biomechanical simulations.

Manual lifting is a common occupational activity associated with an increased risk of low back disorders. In this study, 22 participants from UPMC Hamot, organized into 11 pairs, were recruited. A combination of motion capture techniques and an injury assessment tool was used to investigate the relationships among body anthropometrics, three-dimensional trunk and lower-limb kinematics, and lumbar spinal loading. Potential differences in lifting mechanics were observed between male and female participants. Males exhibited greater trunk flexion and higher compressive loading, while females demonstrated greater hip and knee flexion with reduced trunk motion. These findings indicate that spinal loading during lifting is influenced by an interaction between anthropometric characteristics and movement coordination patterns, with variable behavioral trends affecting load distribution across the trunk and lower extremities. The results provide biomechanical insight that may inform the development of bio-ergonomic training techniques aimed at reducing lumbar spine loading and minimizing injury risk in occupational lifting tasks.

Emergency responders face a high risk of musculoskeletal disorders (MSDs), particularly lower back injuries, due to frequent patient-handling tasks performed in awkward and dynamic postures. This aim of study is to utilize dual motion capture systems integrated with a digital human modeling (DHM) ergonomics tool to evaluate the biomechanical effects of two common two-person carrying techniques: facing forward and facing each other. Twenty-two participants lifted a 25 kg mannequin while wearing Xsens motion sensors, and lumbar forces and joint angles were analyzed using Siemens Jack software (v9.0). Peak compressive and anterior–posterior (AP) shear forces, along with trunk, hip, and knee joint angles, were examined. Compressive forces ranged from approximately 948.6 to 2955.6 N, and AP shear forces ranged from 286.0 to 827.0 N. Mean compressive and AP shear forces were higher during the facing-each-other task (1977.3 N and 595.0 N) than during the facing-forward task (1596.0 N and 462.0 N). Males experienced higher spinal loads than females across both tasks. The facing-each-other technique was associated with greater hip flexion, lower knee flexion, and reduced trunk flexion, whereas the facing-forward technique resulted in less hip flexion, greater knee flexion, and greater trunk flexion. Overall, under the conditions of the present study, the facing-forward technique was associated with lower lumbar loading indicators. Integrating motion capture with DHM offers a valuable approach for evaluating realistic rescue tasks and can inform ergonomic training strategies for emergency responders.

Lumbar shear forces are increasingly recognized as critical contributors to lower-back injury risk, yet most ergonomic assessment tools—most notably the Revised NIOSH Lifting Equation (RNLE)—do not directly estimate shear loading. This study develops and evaluates a family of linear mixed-effects regression models that statistically predict L4/L5 lumbar shear force exposure using traditional NIOSH lifting parameters combined with posture descriptors extracted from digital human models. A harmonized dataset of 106 peak-shear lifting postures was compiled from five controlled laboratory studies, with lumbar shear forces obtained from validated biomechanical simulations implemented in the Siemens JACK (Siemens software, Plano, TX, USA) platform. Twelve model formulations were examined, varying in fixed-effect structure and hierarchical random effects, to quantify how load magnitude, hand location, sex, and joint posture relate to simulated task-level anterior–posterior shear exposure at the lumbar spine. Across all models, load magnitude and horizontal reach emerged as the strongest and most stable predictors of shear exposure, reflecting their direct mechanical influence on anterior spinal loading. Hip and knee flexion provided substantial additional explanatory power, highlighting the role of whole-body posture strategy in modulating shear demand. Upper-limb posture and coupling quality exhibited minimal or inconsistent effects once load geometry and lower-body posture were accounted for. Random-effects analyses demonstrated that meaningful variability arises from individual movement strategies and task conditions, underscoring the necessity of mixed-effects modeling for representing hierarchical structure in lifting data. Parsimonious models incorporating subject-level random intercepts produced the most stable and interpretable coefficients while maintaining strong goodness-of-fit. Overall, the findings extend the NIOSH framework by identifying posture-dependent determinants of lumbar shear exposure and by demonstrating that simulated shear loading can be reliably predicted using ergonomically accessible task descriptors. The proposed models are intended as statistical predictors of task-level shear exposure that complement—rather than replace—comprehensive biomechanical simulations. This work provides a quantitative foundation for integrating shear-aware metrics into ergonomic risk assessment practices, supporting posture-informed screening of manual material-handling tasks in field and sensor-based applications.

Background: Healthcare professionals are at significant risk of musculoskeletal injuries due to the physically demanding nature of patient-handling tasks. While various ergonomic interventions have been introduced to mitigate these risks, comprehensive methods for assessing and addressing musculoskeletal hazards remain limited. Purpose: This study presents a novel approach to evaluating musculoskeletal injury risks among healthcare workers, marking the first instance in which two motion tracking systems are used simultaneously. This dual-system setup enables a more comprehensive and dynamic analysis of worker interactions in real time. Healthcare professionals were divided into three groups to perform patient transfer tasks. Three key poses within the task, associated with peak lumbar forces, were identified and analyzed. Results: The resulting compressive forces on the participants’ lower back ranged from 581.0 N to 3589.1 N, and the Anterior–Posterior (A/P) shear forces ranged from 33.1 N to 912.3 N across the three poses. Relative differences in trunk flexion showed strong correlations with compressive and A/P shear forces at each pose, respectively. Discussion and conclusion: Strong associations were found between lumbar loads and participant’s anthropometrics. Recommendations for optimal postures and partner pairings were developed to help reduce the risk of lower back injuries during patient handling.

A single-camera motion analysis system, referred to as Behrend motion analysis system, is developed to study 3D knee kinematics in vertical jump. The system was tested and compared with 3D commercial motion system Xsens Awinda. The waveform of flexion/extension measured by Behrend motion analysis system exhibits a high degree of similarity to the waveform measured by Xsens Awinda system (R2>0.97).

Archery has increasingly captivated attention in its use for rehabilitation and physical education due to its adaptability for various abilities. However, this repetitive sport carries some injury risk in the shoulder, elbow, and back during the draw and release phases. While research often explores factors affecting shooting performance, limited studies have examined the interplay between gender-specific biomechanics and bow-related variables on lumbar stress and shooting mechanics. This study addresses this gap by leveraging the Xsens MVN Awinda motion capture system and JACK Siemens ergonomic software to analyze full-body movements of archers with different experience levels, bow types, and target placements. Thirteen subjects participated in this investigation, each equipped with standard gear. We analyzed their posture throughout the shooting sequence and the forces acting on their lower back. This innovative approach streamlines data collection and eliminates the need for extensive prototyping. Our findings highlight natural biomechanical adaptations between males and females when using bows of varying draw weights. Males generally exhibited greater consistency and stability, while females showed increased variability, particularly with heavier bows. This research establishes a foundation for ergonomic and reproducible archery techniques, enabling individualized training and performance optimization strategies.

The objective of this study was to assess the accuracy of virtual reality (VR) technology in replicating real-life environments for the adoption of appropriate human postures and forces. Despite the widespread implementation of VR in various applications, there is a lack of research evaluating the accuracy of human postures and sensory aspects in the VR environment compared to real-life scenarios. A total of twenty-two student participants were recruited for this study, which involved a common lifting task. Two specific poses were identified as having potentially excessive forces exerted on the lower back. By comparing the angles of seven anatomical joints in both the real environment and the VR environment at each pose, we observed that depth perception may influence posture adoption in the VR setting. Moreover, the presence of a physical load applied to both hands significantly influenced the postures adopted by participants compared to those in the VR environment. These deviations in postures directly led to significant differences in predicted spinal forces exerted on the lower back, which in turn could result in inaccurate assessments of injury risks and the design of injury prevention programs. Therefore, it is crucial to understand the accuracy of VR technology as a substitute for real-life environments.