Person: Dennerlein, Jack
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Dennerlein
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Jack
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Dennerlein, Jack
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Publication Tablet Keyboard Configuration Affects Performance, Discomfort and Task Difficulty for Thumb Typing in a Two-Handed Grip(Public Library of Science, 2013) Trudeau, Matthieu B.; Catalano, Paul; Jindrich, Devin L.; Dennerlein, JackWhen holding a tablet computer with two hands, the touch keyboard configuration imposes postural constraints on the user because of the need to simultaneously hold the device and type with the thumbs. Designers have provided users with several possible keyboard configurations (device orientation, keyboard layout and location). However, potential differences in performance, usability and postures among these configurations have not been explored. We hypothesize that (1) the narrower standard keyboard layout in the portrait orientation leads to lower self-reported discomfort and less reach than the landscape orientation; (2) a split keyboard layout results in better overall outcomes compared to the standard layout; and (3) the conventional bottom keyboard location leads to the best outcomes overall compared to other locations. A repeated measures laboratory experiment of 12 tablet owners measured typing speed, discomfort, task difficulty, and thumb/wrist joint postures using an active marker system during typing tasks for different combinations of device orientation (portrait and landscape), keyboard layout (standard and split), and keyboard location (bottom, middle, top). The narrower standard keyboard with the device in the portrait orientation was associated with less discomfort (least squares mean (and S.E.) 2.9±0.6) than the landscape orientation (4.5±0.7). Additionally, the split keyboard decreased the amount of reaching required by the thumb in the landscape orientation as defined by a reduced range of motion and less MCP extension, which may have led to reduced discomfort (2.7±0.6) compared to the standard layout (4.5±0.7). However, typing speed was greater for the standard layout (127±5 char./min.) compared to the split layout (113±4 char./min.) regardless of device orientation and keyboard location. Usage guidelines and designers can incorporate these findings to optimize keyboard design parameters and form factors that promote user performance and usability for thumb interaction.Publication A Data-Driven Design Evaluation Tool for Handheld Device Soft Keyboards(Public Library of Science, 2014) Trudeau, Matthieu B.; Sunderland, Elynor; Jindrich, Devin L.; Dennerlein, JackThumb interaction is a primary technique used to operate small handheld devices such as smartphones. Despite the different techniques involved in operating a handheld device compared to a personal computer, the keyboard layouts for both devices are similar. A handheld device keyboard that considers the physical capabilities of the thumb may improve user experience. We developed and applied a design evaluation tool for different geometries of the QWERTY keyboard using a performance evaluation model. The model utilizes previously collected data on thumb motor performance and posture for different tap locations and thumb movement directions. We calculated a performance index (PITOT, 0 is worst and 2 is best) for 663 designs consisting in different combinations of three variables: the keyboard's radius of curvature (R) (mm), orientation (O) (°), and vertical location on the screen (L). The current standard keyboard performed poorly (PITOT = 0.28) compared to other designs considered. Keyboard location (L) contributed to the greatest variability in performance out of the three design variables, suggesting that designers should modify this variable first. Performance was greatest for designs in the middle keyboard location. In addition, having a slightly upward curve (R = −20 mm) and orientated perpendicular to the thumb's long axis (O = −20°) improved performance to PITOT = 1.97. Poorest performances were associated with placement of the keyboard's spacebar in the bottom right corner of the screen (e.g., the worst was for R = 20 mm, O = 40°, L = Bottom (PITOT = 0.09)). While this evaluation tool can be used in the design process as an ergonomic reference to promote user motor performance, other design variables such as visual access and usability still remain unexplored.Publication Prediction of trapezius muscle activity and shoulder, head, neck, and torso postures during computer use: results of a field study(BioMed Central, 2014) Bruno Garza, Jennifer L; Eijckelhof, Belinda HW; Huysmans, Maaike A; Johnson, Peter W; van Dieen, Jaap H; Catalano, Paul; Katz, Jeffrey; van der Beek, Allard J; Dennerlein, JackBackground: Due to difficulties in performing direct measurements as an exposure assessment technique, evidence supporting an association between physical exposures such as neck and shoulder muscle activities and postures and musculoskeletal disorders during computer use is limited. Alternative exposure assessment techniques are needed. Methods: We predicted the median and range of amplitude (90th-10th percentiles) of trapezius muscle activity and the median and range of motion (90th-10th percentiles) of shoulder, head, neck, and torso postures based on two sets of parameters: the distribution of keyboard/mouse/idle activities only (“task-based” predictions), and a comprehensive set of task, questionnaire, workstation, and anthropometric parameters (“expanded model” predictions). We compared the task-based and expanded model predictions based on R2 values, root mean squared (RMS) errors, and relative RMS errors calculated compared to direct measurements. Results: The expanded model predictions of the median and range of amplitude of trapezius muscle activity had consistently better R2 values (range 0.40-0.55 compared to 0.00-0.06), RMS errors (range 2-3%MVC compared to 3-4%MVC), and relative RMS errors (range 10-14%MVC compared to 16-19%MVC) than the task-based predictions. The expanded model predictions of the median and range of amplitude of postures also had consistently better R2 values (range 0.22-0.58 compared to 0.00-0.35), RMS errors (range 2–14 degrees compared to 3–22 degrees), and relative RMS errors (range 9–21 degrees compared to 13–42 degrees) than the task-based predictions. Conclusions: The variation in physical exposures across users performing the same task is large, especially in comparison to the variation across tasks. Thus, expanded model predictions of physical exposures during computer use should be used rather than task-based predictions to improve exposure assessment for future epidemiological studies. Clinically, this finding also indicates that computer users will have differences in their physical exposures even when performing the same tasks.