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Objective: We evaluated the effect of work surface angle and input hardware on upper-limb posture when using a hybrid computer workstation.Background: Offices use sit-stand and/or tablet workstations to increase worker mobility. These workstations may have negative effects on upper-limb joints by increasing time spent in non-neutral postures, but a hybrid standing workstation may improve working postures.Method: Fourteen participants completed office tasks in four workstation configurations: a horizontal or sloped 15° working surface with computer or tablet hardware. Three-dimensional right upper-limb postures were recorded during three tasks: reading, form filling, and writing e-mails. Amplitude probability distribution functions determined the median and range of upper-limb postures.Results: The sloped-surface tablet workstation decreased wrist ulnar deviation by 5° when compared to the horizontal-surface computer when reading. When using computer input devices (keyboard and mouse), the shoulder, elbow, and wrist were closest to neutral joint postures when working on a horizontal work surface. The elbow was 23° and 15° more extended, whereas the wrist was 6° less ulnar deviated, when reading compared to typing forms or e-mails.Conclusion: We recommend that the horizontal-surface computer configuration be used for typing and the sloped-surface tablet configuration be used for intermittent reading tasks in this hybrid workstation.Application: Offices with mobile employees could use this workstation for alternating their upper-extremity postures; however, other aspects of the device need further investigation. [ABSTRACT FROM AUTHOR]

This study sought to compare and validate baseball pitching mechanics, including joint angles and spatiotemporal parameters, from a single camera markerless motion capture solution with a 3D optical marker-based system. Ten healthy pitchers threw 2–3 maximum effort fastballs while concurrently using marker-based optical capture and pitchAITM (markerless) motion capture. Time-series measures were compared using R-squared (r2), and root mean square error (RMSE). Discrete kinematic measures at foot plant, maximal shoulder external rotation, and ball release, plus four spatiotemporal parameters were evaluated using descriptive statistics, Bland-Altman analyses, Pearson’s correlation coefficients, p-values, r2, and RMSE. For time-series angles, r2 ranged from 0.69 (glove arm shoulder external rotation) to 0.98 (trunk and pelvis rotation), and RMSE ranged from 4.37° (trunk lateral tilt) to 20.78° (glove arm shoulder external rotation). Bias for individual joint angle and spatiotemporal parameters ranged from −11.31 (glove arm shoulder horizontal abduction; MER) to 12.01 (ball visible). RMSE was 3.62 m/s for arm speed, 5.75% height for stride length and 21.75 ms for the ball visible metric. pitchAITM can be recommended as a markerless alternative to marker-based motion capture for quantifying pitching kinematics. A database of pitchAITM ranges should be established for comparison between systems. [ABSTRACT FROM AUTHOR]

This study sought to compare and validate baseball pitching mechanics, including joint angles and spatiotemporal parameters, from a single camera markerless motion capture solution with a 3D optical marker-based system. Ten healthy pitchers threw 2-3 maximum effort fastballs while concurrently using marker-based optical capture and pitchAI TM (markerless) motion capture. Time-series measures were compared using R -squared (r 2 ), and root mean square error (RMSE). Discrete kinematic measures at foot plant, maximal shoulder external rotation, and ball release, plus four spatiotemporal parameters were evaluated using descriptive statistics, Bland-Altman analyses, Pearson's correlation coefficients, p-values, r 2 , and RMSE. For time-series angles, r 2 ranged from 0.69 (glove arm shoulder external rotation) to 0.98 (trunk and pelvis rotation), and RMSE ranged from 4.37° (trunk lateral tilt) to 20.78° (glove arm shoulder external rotation). Bias for individual joint angle and spatiotemporal parameters ranged from -11.31 (glove arm shoulder horizontal abduction; MER) to 12.01 (ball visible). RMSE was 3.62 m/s for arm speed, 5.75% height for stride length and 21.75 ms for the ball visible metric. pitchAI TM can be recommended as a markerless alternative to marker-based motion capture for quantifying pitching kinematics. A database of pitchAI TM ranges should be established for comparison between systems.