Predicting preferred motorcycle riding postures to support human factors/ergonomic trade-off analyses within a multi-objective optimisation-based digital human model.

Digital human models (DHM) can predict how users might interact with new vehicle geometry during early-stage design, an important precursor to conducting trade-off analyses. However, predicting human postures requires assumptions about which performance criteria best predict realistic postures. Focusing on the design of motorcycles, we do not know what performance criteria drive preferred riding postures. Addressing this gap, we aimed to identify which performance criteria and corresponding weightings best predicted preferred motorcycle riding postures when using a DHM. To address our aim, we surveyed the literature to find experimental data specifying joint angles that correspond to preferred riding postures. We then deployed a response surface methodology to determine which performance criteria and weightings optimally predicted the preferred riding postures when using a DHM. Weighting the minimisation of the discomfort performance criteria (an aggregate of joint range of motion, displacement from neutral and joint torque) best predicted preferred motorcycle riding postures.

This study describes how we learned what performance criteria and weightings were necessary to best predict riding postures for a cruiser-like motorcycle when using a digital human model. We learned to prioritise the minimise discomfort performance criteria to predict riding postures that best match experimental data.

Determining whether biomechanical variables that describe common 'safe lifting' cues are associated with low back loads.

Lift technique training programs have been implemented to help reduce injury risk, but the underlying content validity of cues used within these programs is not clear. The objective of this study was to determine whether biomechanical variables, that commonly used lifting cues aim to elicit, are associated with resultant low back extensor moment exposures. A sample of 72 participants were recruited to perform 10 repetitions of a floor-to-waist height barbell lift while whole-body kinematics and ground reaction forces were collected. Kinematic, kinetic, and energetic variables representative of characteristics commonly targeted by lifting cues were calculated as predictor variables, while peak and cumulative low back moments were calculated as dependent measures. Multiple regression revealed that 56.6-59.2% of variance in low back moments was explained by predictor variables. From these regression models, generating motion with the legs (both greater hip and knee work), minimizing the horizontal distance of the body to the load, maintaining a stable body position, and minimizing lift time were associated with lower magnitudes of low back moments. These data support that using cues targeting these identified variables may be more effective at reducing peak low back moment exposures via lift training.

A response surface methodology to determine the optimal objective function weightings within a multi-objective optimization digital human model used to predict postures.

Multi-objective optimization digital human models permit users to predict postures that follow performance criteria, such as minimizing torques. Currently, it is unknown how to weight different objective functions to best predict postures. Objective one was to describe a response surface method to determine optimal objective function weightings to predict lift postures. Objective two was to evaluate the sensitivity of different error calculation methods. Our response surface approach has utility for determining optimal objective function weightings when using a digital human model to evaluate human-system interactions in early design stages. The approach was not dependent on variations in error calculation methods.

Exploring Optimal Objective Function Weightings to Predict Lifting Postures Under Unfatigued and Fatigued States.

OBJECTIVE: To explore whether the optimal objective function weightings change when using a digital human model (DHM) to predict origin and destination lifting postures under unfatigued and fatigued states. BACKGROUND: The ability to predict human postures can depend on state-based influences (e.g., fatigue). Altering objective function weightings within a predictive DHM could improve the ability to predict tasks specific lifting postures under unique fatigue states. METHOD: A multi-objective optimization-based DHM was used to predict origin and destination lifting postures for ten anthropometrically scaled avatars by using different objective functions weighting combinations. Predicted and measured postures were compared to determine the root mean squared error. A response surface methodology was used to identify the optimal objective function weightings, which was found by generating the posture that minimized error between measured and predicted lifting postures. The resultant weightings were compared to determine if the optimal objective function weightings changed for different lifting postures or fatigue states. RESULTS: Discomfort and total joint torque weightings were affected by posture (origin/destination) and fatigue state (unfatigued/fatigued); however, post-hoc differences between fatigue states and lifting postures were not sufficiently large to be detected. Weighting the discomfort objective function alone tended to predict postures that generalized well to both postures and fatigue states. CONCLUSION: Lift postures were optimal predicted using the minimization of discomfort objective function regardless of fatigue state. APPLICATION: Weighting the discomfort objective can predict unfatigued postures, but more research is needed to understand the optimal objective function weightings to predict postures during a fatigued state.

Multi-task exposure assessment to infer musculoskeletal disorder risk: A scoping review of injury causation theories and tools available to assess exposures.

Exposure assessment is critical for understanding musculoskeletal disorder (MSD) risk. Previous reviews summarized the tools available for single-task exposure assessment, however no reviews summarize tools available to assess the accumulation or aggregation of exposure associated with the performance of multiple tasks (i.e., multi-task assessment). We address this gap by using a scoping review methodology to: 1) summarize the theories explaining how multi-task exposures may lead to MSDs, and 2) summarize the models and tools available to assess multi-task exposures, stratified based on prevailing theories. Using a systematic search strategy, 3230 articles were identified, of which 34 were retained for data extraction. Of the retained articles, 13 described MSD causation theories, 12 described mathematical models (not yet accessible as tools), six described readily accessible tools, and three described both theories and a model or tool. We summarized the state-of-the-art in multi-task exposure assessment and highlight the need for more tools that assess muscle fatigue and inform on recovery.

The influence of sex and strength capacity on normalized low-back exposure during lifting: Insights from a joint power analysis.

OBJECTIVE: Investigate the influence of sex, strength capacity, and relative load mass on low-back exposure and lower extremity joint power generation in backboard lifting. BACKGROUND: Sex and strength have been shown to influence lifting strategy, but without load mass being scaled to strength it is unknown which factor influences low-back exposures, and whether there are interactions with load mass. METHODS: Motion capture and force plate data from 28 participants were collected during backboard lifting at load masses scaled to strength capacity. Differences in normalized peak low-back moment, peak knee-to-hip power magnitude ratio and timing were tested as a function of sex, strength, and load mass. RESULTS: Stronger participants had lower normalized peak low-back moments (average 32% change from low-capacity across all load masses), with no significant sex effect (p = 0.582). As load mass increased, normalized peak low-back moment, peak knee-to-hip power magnitude and synchronicity decreased. CONCLUSION: Training to both increase strength capacity and hip-joint power generation may be a strategy to reduce low-back exposure in backboard lifting.

Improving human-in-the-loop simulation to optimize soldier-systems integration.

OBJECTIVE: To identify requirements for human-in-the-loop simulation capabilities and improve their utility in predicting and optimizing soldier-systems integration. BACKGROUND: Technological development rates within the military are rapidly increasing. Emergent technologies often exclude in-depth consideration of human-system interactions until the physical prototyping phase. Human-in-the-loop simulation tools can allow for earlier consideration of humans in the development process; however, use remains limited. METHOD: Semi-structured interviews were conducted with key informants to yield perspectives on current human-in-the-loop simulation capabilities and utility specific to the military. An inductive approach to thematic analysis was used to extract critical themes from transcribed interview data. A scoping review was completed to supplement the data obtained from interviews and summarize knowledge regarding requirements for human-in-the-loop simulation and analysis capabilities targeted to the military. RESULTS: Interviews were conducted with five experts representing the sectors of Vehicle/Equipment Design, Simulation, and Army Research. A total of 2274 sources were identified, and 64 papers were retained for the scoping review. Thematic analysis of the combined data sources yielded six important themes to consider with respect to requirements for future human-in-the-loop simulation capabilities targeting soldier-systems integration. CONCLUSION: This study has identified eight key requirements to support the use of human-in-the-loop simulation tools to predict and optimize soldier-systems integration and performance. APPLICATION: Addressing key requirements will improve the ability of current human-in-the-loop simulation tools to accommodate the military's need for human consideration early in the design process.