Direct and continuous exposure measurement has posed challenges to human factors engineering (HFE) professionals when conducting risk assessments. However, emerging technologies have utility to automate elements of HFE assessment and strengthen opportunities for direct and continuous exposure measurement. Leading HFE researchers provide perspectives on how advances in technology and computing, including computer vision, machine learning and wearable sensors, can aid in the automation of exposure measurement to inform ergonomic assessment while also bolstering the opportunities for objective, data-driven insight. Drs. SangHyun Lee and Michael Sonne share perspectives on the development and validation of computer vision-based pose estimation approaches. Such pose estimation approaches allow HFE professions to record video data where software can convert video into a kinematic representation of a worker and then calculate corresponding joint angles without the need for any tedious posture matching, or additional post processing approaches. Dr. Cavuoto discusses how wearable technologies can unobtrusively measure kinematics in work, showcasing the potential of direct measurement, data-driven injury risk assessment. Finally, Dr. Gallagher showcases how data collected through automated approaches can be integrated with models to evaluate injury risk through a fatigue-failure injury mechanism pathway. In addition to showcasing how emerging technologies and approaches may enhance exposure and risk assessment in HFE, panelists also highlight anticipated challenges and barriers that need to be addressed to support more ubiquitous integration of such technologies into HFE assessment practice. The future for innovation and advancement in exposure measurement and assessment is bright.

OCCUPATIONAL APPLICATIONSMilitary helicopter pilots around the globe are at high risk of neck pain related to their use of helmet-mounted night vision goggles. Unfortunately, it is difficult to design alternative helmet configurations that reduce the biomechanical exposures on the cervical spine during flight because the time and resource costs associated with assessing these exposures in vivo are prohibitive. Instead, we developed artificial neural networks (ANNs) to predict cervical spine compression and shear given head-trunk kinematics and joint moments in the lower neck, data readily available from digital human models. The ANNs detected differences in cervical spine compression and anteroposterior shear between helmet configuration conditions during flight-relevant head movement, consistent with results from a detailed model based on in vivo electromyographic data. These ANNs may be useful in helping to prevent neck pain related to military helicopter flight by facilitating virtual biomechanical assessment of helmet configurations upstream in the design process.TECHNICAL ABSTRACTBackground: The use of night vision goggles (NVGs) has been linked to a high prevalence of neck pain and injury in military helicopter pilots. Next generation helmet designs aim to mitigate NVG related consequences on cervical spine loading. Currently, in vivo human-participant experiments are required to collect necessary data, such as electromyography (EMG) to estimate joint contact forces in the cervical spine as a result of unique helmet designs. This is costly and inefficient. Digital human models, which provide inverse dynamics, coupled with artificial neural networks (ANNs), can provide a surrogate for musculoskeletal joint modeling to predict joint contact forces.Purpose: We developed ANNs to predict C6-C7 compression and anteroposterior shear during flight-relevant head movements with sufficient sensitivity to differentiate between candidate helmet designs in terms of associated biomechanical exposures.Methods: Motion capture and EMG data were collected from 26 participants who performed flight-relevant reciprocal head movements about pitch and yaw axes while donning one of four helmet configurations. These data were input into an EMG-driven musculoskeletal model of the neck to generate time series of C6-C7 compression and shear. Rotation-specific ANNs were trained to predict the EMG-driven model outputs, given only the head-trunk kinematics and C6-C7 moments as inputs.Results: ANNs for pitch rotations were successful in estimating peak and cumulative compression and shear, with an absolute error that was lower than absolute differences in joint contact forces between relevant helmet conditions. ANNs for yaw rotations were similarly successful in differentiating between C6-C7 compression and cumulative C6-C7 shear, but less so for peak C6-C7 shear.Conclusions: When combined with biomechanical data readily available from digital human modeling software, use of an ANN surrogate for joint musculoskeletal modeling can permit evaluation of joint contact forces associated with novel helmet designs during upstream design. Improved consideration of joint contact forces during a virtual helmet design process will assist in identifying helmet designs that reduce biomechanical exposures of the cervical spine during helicopter flight.

Municipal waste collectors must avoid bag-body contact, requiring waste bags to be held further from the body. Donning sharps-proof clothing would permit bag-body contact, allowing the bag to be closer to the body, reducing biomechanical exposures. To test this hypothesis, 25 participants loaded waste bags into a simulated garbage truck hopper under two lifting (contact allowed, no contact) and bag mass (7 kg and 20 kg) conditions. Bottom-up rigid-link biomechanical modelling results including peak low back compression force, antero-posterior shear force and peak low back flexion angle were not different between the lifting conditions, but cumulative compression was decreased with bag-body contact, although only at the 20 kg mass. Bag mass had significant effects on outcome measures, causing compression to increase to 4663 (±697) N, exceeding recommended thresholds. Sharps-proof clothing and municipally mandated 23 kg maximum allowable bag mass restrictions may not sufficiently reduce biomechanical exposures to prevent MSD.

Are you tired of listening to presentations that go on and on that put you to sleep? Do you think you can do better? As part of the Student/ECR committee, the IEA2021 is hosting a ‘Pecha Kucha’ (PK) competition for students. This will give students a chance to showcase their overall thesis topics. PK is derived from a Japanese word for ‘chit-chat’ and signifying a concise, fast-paced method of delivering a presentation. PK is a creative and imaginative way to explain complex research, within a short time period. The competition will be following the traditional Pecha Kucha format, where participants will be restricted to showing 20 slides, for 20 s each, with a total of six minutes and 40 s to speak on their thesis topic, in the realm of Ergonomics.

Pressure ulcers are commonly developed in bedridden patients due to prolonged pressure on bony prominences. Turn-assist support surfaces have been developed to help reposition patients to redistribute interface pressure. The aim of this study was to determine if turn-assist technologies confer benefits to patients relative to manual turning, and to determine if different turn-assist functionalities influence patient outcomes differently. Interface pressure (contact area, average and peak pressure) and patient turn quality metrics (turn angle and repeatability) were recorded during manual and facilitated turns on two different turn-assist hospital beds at initial patient position, turn-assist (maximal mattress inflation) and final patient position. Manual turns produced the most repeatable turn angles, and closest to the recommended 30° compared to both turn-assist surfaces. Interface pressure differences between surfaces were most prominent in the pelvis region across all three time points. Overall, turn-assist surfaces produced interface pressure outcomes similar to manual turning, but manual turning produced more repeatable and optimal patient turn angles. Different turn-assist surfaces achieved different patient turn angles, so functionalities should be examined before device implementation.

BACKGROUND: Forceful exertions of the arm/shoulder are common during material handling and many other industrial tasks. Determination of how the risk of shoulder injury changes in conjunction with direction of force exertion could provide useful guidance on the design of workplaces and tasks. OBJEC TIVE: This research was conducted to determine how direction of force exertion and muscle recruitment algorithm effect shoulder strain computed by musculoskeletal modeling. METHODS: Musculoskeletal modeling software was used to perform simulations of static force exertions of the right upper limb. A series of 36 force exertions in directions at 30° intervals in the transverse, sagital, and frontal planes were performed using three muscle recruitment optimization algorithms. A previously validated strain index equation was used to calculate risk injury for each force exertion based on the magnitude and direction of the resultant glenohumeral force. RESULTS: Generally, highest strain values were found in the downward, backward, and leftward direction and lowest strain values were found in the upward, forward, and rightward direction, or, in other words, during force exertions opposing forces in those directions. CONCLUSIONS: When designing workplace tasks that involve forceful exertions of the shoulder, pulling and downward pushing exertions should be given preference over pushing and lifting exertions.

Background: Repetitive handling of heavy concrete blocks has been associated with the risk of low back and shoulder injuries among the masons. Several interventions have been proposed to reduce the risk of musculoskeletal disorders among the masons. A new intervention, a lift-assist handle, was tested in this study. Objective: The effectiveness of the lift-assist handle in masonry work was assessed using the shoulder and low back kinematics during block lifting/lowering tasks performed at two heights. Methods: In a laboratory setting, seven male subjects performed with- and without-lift handle assisted block lifting tasks at two different heights. Optical motion capture system and biomechanical modeling software were used to record and model each dynamic trial. Effect of lifting height and use of a lift-assist handle on range of motion of the shoulder and trunk were tested. Results: The use of lift-assist handle significantly reduced trunk motion and increased shoulder motion. Lifting height had a significant effect on shoulder kinematics only. When height was increased from 17to 29 inches, the ranges of motion of shoulder abduction-adduction and internal rotation significantly increased. Additionally, block lifting/lowering task duration decreased by 26% when the lift-assist handle was used. Conclusions: While potential benefits to lower back health were found with the lift-assist handle, increased shoulder motion may increase the risk of shoulder injuries. The findings of this study emphasize the need for an in-depth analysis of assistive devices prior to implementation to ensure that there are no unintended consequences of their use that could negate their benefit.

In this study, a novel conceptual method was tested to study the kinematic mismatch between the body motion of an occupant with respect to a rigid suit. It was hypothesized that differences between body and suit motion would require extra body movement to achieve the desired suit motion. To quantify the mismatch in kinematics, mock upper body suits with an open structure were used in conjunction with a marker-based motion capture system. A 3D motion modeling software was used to determine the range of motion of the suit and body segments of nine participants performing seven basic arm and trunk motions. In general, range of motion of the body segment was found to be higher than the corresponding suit segment range of motion. Differences in range of motion of up to 21.3% were found between corresponding body and suit segments, and significance was found in five of the seven motions. Relevance to industry: Development of a method of determining kinematic misalignment of protective suits will assist evaluation and development of more appropriate protective suits. Better kinematic alignment will not only reduce the risk of injury, but can also improve comfort and benefit performance. (C) 2014 Elsevier B.V. All rights reserved.

Background: Little is known about ingress/egress requirements and forward reach for workstations with horizontal seats. This research explored differences between ingress/egress kinematics and reach due to seat orientation. Methods: 10 participants performed ingress/egress tasks using three seat orientations (horizontal with 90° and 120° seat angles, and vertical with 90° seat angle) and planar reach tasks in three anatomical planes using horizontal and vertical seats with 90° seat angle. An optical motion capture system was used to record kinematic data. Marker data was processed and modeled to estimate peak joint angles and ranges of motion of several body joints. For reach tasks, marker data of the clavicle and finger were used to plot reach capacity. Results: Ingress/egress joint kinematics differed greatly between horizontal and vertical seats, while few differences existed between the horizontal seat orientations. Peak angles and ranges of motion during ingress/egress of the horizontal seats were significantly higher than the vertical seats, often by a factor of 3–4. The direction of motion affected several peak angles and ranges of motion, but to a lesser extent than seat orientation. Reach was unaffected by seat orientation. Conclusion: This study's findings suggest that ingress/egress of horizontal seats is more stressful for the body, especially the shoulders and lower back, than regular upright seats.

Background: Manual material handling incidents are responsible for a large portion of lost work days annually. With the transition to e-commerce, cart pushing and pulling tasks have become more common. Objective: This research explored the effects of surface gradient and load on full body kinematics during cart pushing and pulling tasks. Methods: Ten participants were recruited to complete two sets of tasks. Participants performed cart pushing tasks on three surface gradients and three load masses and downhill cart pulling tasks on two surface gradients and three load masses while being recorded with an optical motion capture system. Full body, three-dimensional joint angles were calculated for each task, and peak angles of the major body joints were analyzed using general linear models to determine the effects of the dependent variables. Results: During the cart pushing tasks, increased load mass and surface gradient both caused a significant increase in the peak joint angles of most body joints with surface gradient having the larger effect. When cart pushing and pulling tasks were compared with 5° and 10° surface gradients and three load masses, cart pushing resulted in significantly higher joint angles. Conclusion: During manual material handling tasks involving a cart, surface gradients and load masses should be minimized.

Research has been done on the maximum reach and ingress/egress of upright seats. However, research on recumbent seats and comparisons between recumbent and upright seats is limited. By using an eight-camera Vicon motion capture system and C-motion Visual 3D modeling software, this research compared the ingress/egress joint kinematics and maximal planar reach of an upright seat with a recumbent seat. Mean range of motion and mean peak angle for each ingress/egress task were determined and the values for the upright seat were compared to the values for the recumbent seat. For each reach task, three extreme points were extracted and compared between the upright and recumbent seat. Seat orientation was found to have a statistically significant effect on the range of motion of several joints during the ingress/egress tasks, as well as one of the extreme points during the reaching tasks.