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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.

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.

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).

A deterministic mechanical model based on physical tool parameters was used for estimating static and dynamic hand forces from kinematic measurements. We investigated the effects of target torque (25, 40, and 55 Nm) and threaded fastener joint hardness (35-, 150-, 300-, 500-, and 900-ms torque buildup time) on hand force. Estimated hand force was affected by target torque and joint hardness. Peak and average dynamic hand force was least for the hard joint (35-ms buildup) and greatest for the medium hardness joint (150-ms buildup). Tool inertia played the major role in reducing hand reaction force. Estimated hand force decreased when the inertial force component increased. Inertial force decreased by 366% when buildup time increased from 35 to 300 ms. Static modeling overestimated hand force; the error ranged from 10% for a soft joint to 40% for a hard joint. Results from direct hand force measurements using a strain gauge dynamometer showed that the dynamic model overestimated peak hand force by 9%. However, average hand force and force impulse were not significantly overestimated.

Objective: A multiaxis dynamometer was used to quantify grip force vector angles and longitudinal centers of pressure (COPs) while varying handle size and effort used. Background: Authors of many studies have examined maximum grip strength using scalar instruments; a few have measured two-axis forces limited to one or more finger contact. This novel dynamometer uses two instrumented beams that are grasped by the distal fingers and proximal palm to compute two orthogonal components of force and the longitudinal COP through which the force acts. Method: Sixteen healthy, right-handed participants grasped the multiaxis dynamometer with plastic handles ranging in diameter from 3.81 to 7.62 cm. They were required to scale their effort to 25%, 50%, 75%, and 100% of maximum. Results: Grip force vector angles were affected by both handle diameter and effort level, with angles increasing an average of 8.1° from the least to greatest effort. Longitudinal COP, averaged among the two beams, shifted 1.75 cm radially as handle diameter increased from 3.81 cm to 7.62 cm. Average COP along the beam in contact with the distal finger segments shifted 0.75 cm ulnarly as effort level increased from 25% to 100% of maximum. Conclusion: Grip force characteristics changed with handle diameter and effort level. Overall grip force magnitude comprised both force components measured. Application: Understanding grip characteristics should be important for handle and grip design and for evaluating hand function.

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.

This study uses a previously developed single-degree-of-freedom mechanical model to predict the power hand tool operator handle kinematic response to impulsive reaction forces (Lin, 2001). The model considers the human operator as a lumped parameter passive mechanical system, consisting of stiffness, mass moment of inertia, and viscous damping elements. Six power nutrunners were operated by 9 volunteers (3 men, 6 women) in the laboratory, and corresponding handle kinematics were compared against model predictions. A full-factorial experiment considered torque buildup time and work location. Normalized forearm flexor EMG was measured to quantify muscle exertions and used to proportionally adjust the stiffness parameter. The measured handle displacement for actual tool operation strongly correlated to the model predictions ( R = .98) for all handle configurations. The overall model prediction error was 3% for predicting tool handle responses to impulsive reaction forces for various tool and workstation parameters. This model should make it possible for designers to identify conditions that minimize the torque reaction experienced by power hand tool operators.

The discrete movement task employed in this study consisted of moving a cursor from the center of a computer display screen to circular targets located 24.4 and 110.9 mm in eight radial directions. The target diameters were 2.7, 8.1, and 24.2 mm. Performance measures included movement time, cursor path distance, and root-mean-square cursor deviation. Ten subjects with no movement disabilities were studied using a conventional mouse and a lightweight ultrasonic headcontrolled computer input pointing device. Average movement time was 306 ms greater (63%) for the head-controlled pointer than for the mouse. The effect of direction on movement time for the mouse was relatively small compared with the head-controlled pointer, which was lowest at 90 and 270 deg, corresponding to head extension and head flexion, respectively. Average path distance and root mean square displacement was lowest at off-diagonal directions (0, 90, 180, and 270 deg). This methodology was also shown to be useful for evaluating performance using an alternative head-controlled input device for two subjects having cerebral palsy, and measured subtle performance improvements after providing a disabled subject with lateral torso support.

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.

We compared two gain measures: One, defined as angle/angle (A/A) gain, is the ratio between the angle subtended by displacement of the cursor and the corresponding angle of head extension/flexion or rotation. The alternative measure, defined as displacement/angle (D/A) gain, is the ratio between the linear displacement of the cursor on the screen and the corresponding angle of head extension/ flexion or rotation. A discrete target acquisition task using circular targets was used to compare control-display gain measures. Operator performance was evaluated for three viewing distances and three gain settings of each measure. Average movement time and root mean squared (RMS) cursor deviation from a straight line path increased as viewing distance increased for AlA gain settings 0.75 and 1.0. That no significant distance effect was observed for any of the D/A gain settings indicated that it might be more suitable to fix D/A gain rather than AlA gain for head-controlled computer input devices. Minimum movement time occurred for D/A gain settings of 0.5 cm/° and 0.67 cm/° One explanation for the observed insensitivity of performance to changes in viewing distance with a fixed D/A gain may be that both angular head movement and the accompanying kinesthetic feedback do not change for a specified cursor displacement as viewing distance is changed.

Objective: A new method is described for automatically quantifying repetitive hand activity with the use of digital video processing. Background: The hand activity level (HAL) is widely used for evaluating repetitive hand work. Conventional methods involving either a trained observer on- or off-site or manual off-site video analysis are often considered inaccurate, cumbersome, or impractical for routine work assessment. Method: A cross-correlation-based template-matching algorithm was programmed to track the motion trajectory of a selected region of interest across successive video frames for a single camera to measure repetition frequency, duty cycle, and HAL. A simple, paced, load transfer task was used to simulate a repetitive industrial activity. A total of 12 participants were videoed performing the task for varying HAL conditions. The automatically predicted HAL was compared with the manually measured HAL with the use of frame-by-frame video analysis. Results: Predicted frequency, duty cycle, and HAL were in concert with the manually measured HAL conditions. The linear regression slopes of the automatically predicted values with respect to the manually measured values were 0.98 ( R2 = .79), 1.27 ( R2 = .63), and 1.06 ( R2 = .77) for frequency, duty cycle, and HAL, respectively. Conclusion: A proof-of-concept for automatic video-based direct exposure assessment was demonstrated. Application: The video assessment method for repetitive motion is promising for automatic, unobtrusive, and objective exposure assessment, which may offer broad availability with the use of a camera-enabled mobile device for helping evaluate, prevent, and control exposure to repetitive motions related to upper-extremity injuries in the workplace.

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.

Objective: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations. Background: Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries. Method: We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video. Results: Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants. Conclusion: Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants. Application: Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching.