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Today’s challenges for scientific publications require operating at a time when trust in science depends upon effective vetting of data, identifying questionable practices, and scrutinizing research. The Editor-in-Chief has an invaluable opportunity to influence the direction and reputation of our field but also has the responsibility to confront contemporary trends that threaten the publication of quality research. The editor is responsible for maintaining strict scientific standards for the journal through the exercise of good judgment and steadfast commitment to upholding the highest ethical principles. Opportunities exist to create and implement new initiatives for improving the peer review process and elevating the journal’s stature. The journal must address the challenges as well as effectively communicate with the public, who seek a reliable source of information.

Objective A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting. Background Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments. Methods Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames. Results The mean absolute difference between predicted and motion capture measured trunk angles was 14.7°, and there was a significant linear relationship between predicted and measured trunk angles ( R2 = .80, p < .001). The training error for the kinematics model was 2.3°. Conclusion Using simple computer vision-extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision. Application This computer vision method may be implemented on handheld devices such as smartphones to facilitate automatic lifting risk assessments in the workplace.

Objective: The effect of camera viewpoint was studied when performing visually obstructed psychomotor targeting tasks. Background: Previous research in laparoscopy and robotic teleoperation found that complex perceptual-motor adaptations associated with misaligned viewpoints corresponded to degraded performance in manipulation. Because optimal camera positioning is often unavailable in restricted environments, alternative viewpoints that might mitigate performance effects are not obvious. Methods: A virtual keyboard-controlled targeting task was remotely distributed to workers of Amazon Mechanical Turk. The experiment was performed by 192 subjects for a static viewpoint with independent parameters of target direction, Fitts' law index of difficulty, viewpoint azimuthal angle (AA), and viewpoint polar angle (PA). A dynamic viewpoint experiment was also performed by 112 subjects in which the viewpoint AA changed after every trial. Results: AA and target direction had significant effects on performance for the static viewpoint experiment. Movement time and travel distance increased while AA increased until there was a discrete improvement in performance for 180{\deg}. Increasing AA from 225{\deg} to 315{\deg} linearly decreased movement time and distance. There were significant main effects of current AA and magnitude of transition for the dynamic viewpoint experiment. Orthogonal direction and no-change viewpoint transitions least affected performance. Conclusions: Viewpoint selection should aim to minimize associated rotations within the manipulation plane when performing targeting tasks whether implementing a static or dynamic viewing solution. Because PA rotations had negligible performance effects, PA adjustments may extend the space of viable viewpoints. Applications: These results can inform viewpoint-selection for visual feedback during psychomotor tasks., Comment: Accepted for publication in Human Factors

Objective Trade-offs between productivity, physical workload (PWL), and mental workload (MWL) were studied when integrating collaborative robots (cobots) into existing manual work by optimizing the allocation of tasks. Background As cobots become more widely introduced in the workplace and their capabilities greatly improved, there is a need to consider how they can best help their human partners. Methods A theoretical data-driven analysis was conducted using the O*NET Content Model to evaluate 16 selected jobs for associated work context, skills, and constraints. Associated work activities were ranked by potential for substitution by a cobot. PWL and MWL were estimated using variables from the O*Net database that represent variables for the Strain Index and NASA-TLX. An algorithm was developed to optimize work activity assignment to cobots and human workers according to their most suited abilities. Results Human workload for some jobs decreased while workload for some jobs increased after cobots were reassigned tasks, and residual human capacity was used to perform job activities designated the most important to increase productivity. The human workload for other jobs remained unchanged. Conclusions The changes in human workload from the introduction of cobots may not always be beneficial for the human worker unless trade-offs are considered. Application: The framework of this study may be applied to existing jobs to identify the relationship between productivity and worker tolerances that integrate cobots into specific tasks.

Objective: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an “elastic” rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. Background: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle. Methods: Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30°. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures. Results: The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing. Conclusions: The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes. Applications: It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners.

Objective: This study was a proof of concept for virtual exertions, a novel method that involves the use of body tracking and electromyography for grasping and moving projections of objects in virtual reality (VR). The user views objects in his or her hands during rehearsed co-contractions of the same agonist-antagonist muscles normally used for the desired activities to suggest exerting forces. Background: Unlike physical objects, virtual objects are images and lack mass. There is currently no practical physically demanding way to interact with virtual objects to simulate strenuous activities. Method: Eleven participants grasped and lifted similar physical and virtual objects of various weights in an immersive 3-D Cave Automatic Virtual Environment. Muscle activity, localized muscle fatigue, ratings of perceived exertions, and NASA Task Load Index were measured. Additionally, the relationship between levels of immersion (2-D vs. 3-D) was studied. Results: Although the overall magnitude of biceps activity and workload were greater in VR, muscle activity trends and fatigue patterns for varying weights within VR and physical conditions were the same. Perceived exertions for varying weights were not significantly different between VR and physical conditions. Conclusions: Perceived exertion levels and muscle activity patterns corresponded to the assigned virtual loads, which supported the hypothesis that the method evoked the perception of physical exertions and showed that the method was promising. Application: Ultimately this approach may offer opportunities for research and training individuals to perform strenuous activities under potentially safer conditions that mimic situations while seeing their own body and hands relative to the scene.

Objective: A laboratory study investigated the relationship between power hand tool and task-related factors affecting threaded fastener torque accuracy and associated handle reaction force. Background: We previously developed a biodynamic model to predict handle reaction forces. We hypothesized that torque accuracy was related to the same factors that affect operator capacity to react against impulsive tool forces, as predicted by the model. Method: The independent variables included tool (pistol grip on a vertical surface, right angle on a horizontal surface), fastener torque rate (hard, soft), horizontal distance (30 cm and 60 cm), and vertical distance (80 cm, 110 cm, and 140 cm). Ten participants (five male and five female) fastened 12 similar bolts for each experimental condition. Results: Average torque error (audited − target torque) was affected by fastener torque rate and operator position. Torque error decreased 33% for soft torque rates, whereas handle forces greatly increased (170%). Torque error also decreased for the far horizontal distance 7% to 14%, when vertical distance was in the middle or high, but handle force decreased slightly 3% to 5%. Conclusion: The evidence suggests that although both tool and task factors affect fastening accuracy, they each influence handle reaction forces differently. We conclude that these differences are attributed to different parameters each factor influences affecting the dynamics of threaded faster tool operation. Fastener torque rate affects the tool dynamics, whereas posture affects the spring-mass-damping biodynamic properties of the human operator. Application: The prediction of handle reaction force using an operator biodynamic model may be useful for codifying complex and unobvious relationships between tool and task factors for minimizing torque error while controlling handle force.

Grip force was measured along two orthogonal axes and vector summed. Sixtyone participants recruited from a manufacturing facility (29 men and 32 women) grasped instrumented cylinders (2.54, 3.81, 5.08, 6.35, and 7.62 cm diameter) using a maximal voluntary power grip. Two orthogonal force measurements relative to the third metacarpal were resolved into a magnitude and corresponding angle. On average, magnitude increased 34.8 N as handle diameter increased from 2.54 cm to 3.81 cm, and then monotonically declined 103.8 N as the handle diameter increased to 7.62 cm. The average direction monotonically decreased from 59.2° to 37.7° as handle diameter decreased from the largest to the smallest. When the diameter was smallest, the greatest force component, Fx (168.6 N), was in the direction where the fingertips opposed the palm. Conversely, when the diameter was largest, the smallest component, Fx(77.7 N), was in the same direction. These values are averaged for the left and right hand. The angle for the largest diameter increased with increasing hand size. These relationships should be useful for the design of handles that require gripping in specific directions, such as for hand tools and controls. Actual or potential applications of this research include the design of handles that require gripping in specific directions, such as for hand tools and controls, that reduce effort, and that prevent fatigue and overexertion.

Key switch design parameters, including make force, make travel, and over travel, were investigated for minimizing operator-exerted force while maximizing key-tapping speed. A mechanical apparatus was designed, constructed, and used for independently controlling key switch parameters and for directly measuring finger exertions during repetitive key tapping using strain gauge load cells. The task for the 25 participants involved using the index finger of the dominant hand to repeatedly depress a single key as rapidly as possible. Participants received visual and auditory feedback upon a successful keystroke. Peak force exerted decreased 24% and key-tapping rate increased 2% when over travel was distended from 0.0 to 3.0 mm. Although peak force exerted was not significantly affected by make point travel, key-tapping rate increased 2% when make point travel was reduced from 4.0 to 1.0 mm. These results indicate that key switch mechanisms that provide adequate over travel might enable operators to exert less force during repetitive key tapping without inhibiting performance

An anthropometric study was performed on 103 women 19 to 50 years of age. Each subject measured 19 dimensions of her own body and then performed similar measurements on a random partner using a conventional tape measure with 1/16 inch (1.6-mm) precision. The experimenter measured all subjects using a laboratory-grade anthropometer and tape measure with 1.0-mm precision as a standard. Subjects' measurements of different body dimensions had significantly different average errors, ranging from -4.54 cm to +6.15 cm. Hip circumference, which is often utilized as a key dimension for garment sizing, was significantly undermeasured (M = -4.54 cm). Measurements having the least error included waist, bust, and neck circumferences and shoulder and waist heights. Subjects' measurements of their own bodies had greater absolute error (M = 4.10 cm, SD = 6.06 cm) than those of their partners (M == 3.34 cm, SD = 4.86 cm). Although subjects used a tape measure having 1.6-mm precision, 97% reported measurements using only 1/4-inch (6.4-mm) precision or less. Subjects overestimated their own stature by an average error of 0.68 cm (SD = 4.02 cm) and an absolute error of 2.26 cm (SD = 3.39 cm).

Finger and palmar forces were measured during actual pneumatic nutrunner operation using a strain gauge dynamometer. Eighteen student subjects were assigned to one of three categories based on hand length. Two triggers and four handle spans were presented randomly. Handle span affected maximal and submaximal grip force. Asspan increased from 4 cm to 7 cm, average peak finger force increased 24%, peak palmar force increased 22%, and average finger and palmar tool-holding forces increased 20%. When an extended trigger was used, average peak finger force decreased 9%, peak palmar force decreased 8%, finger tool-holding force decreased 65%, and palmar tool-holding force decreased 48%. Hand size affected grip strength (MVC),grip force, and exertion level (force/MVC). Holding exertion level was maximum for large-handed subjects using a 4-cm handle and for small-handed subjects using a 7-cm handle. Subjective handle span preference increased as hand size increased. A similar experiment was performed using 11 factory workers.

Repetitive trauma disorders of the upper extremity are a major cause of lost work in many hand-intensive industries. Reported risk factors include repetitive and forceful exertions, certain postures, mechanical stress, low temperatures, gloves, and vibration. Risk factors can be identified with job analysis procedures based on traditional work-methods analysis. Risk factors can be controlled through reallocation of work, balancing of tools, selection of alternative tool designs, work relocation, selection of suitable hand protection, and elimination of hand exposure to low temperatures and vibration. Drawing-board manikins are used with computer-aided design systems to estimate the best work location for a given task.