Controlled studies of touchscreen input performance for users with upper body motor impairments remain relatively sparse. To address this gap, we present a controlled lab study of mouse vs. touchscreen performance with 32 participants (16 with upper body motor impairments and 16 without). Our study examines: (1) how touch input compares to an indirect pointing device (a mouse); (2) how performance compares across a range of standard interaction techniques; and (3) how these answers differ for users with and without motor impairments. While the touchscreen was faster than the mouse overall, only participants without motor impairments benefited from a lower error rate on the touchscreen. Indeed, participants with motor impairments had a three-fold increase in pointing (tapping) errors on the touchscreen compared to the mouse. Our findings also highlight the high frequency of spurious touches for users with motor impairments and update past accessibility recommendations for minimum touchscreen target sizes to at least 18mm.

Research on the viability of using crowdsourcing for HCI performance experiments has concluded that online results are similar to those achieved in the lab---at least for desktop interactions. However, mobile devices, the most popular form of online access today, may be more problematic due to variability in the user's posture and in movement of the device. To assess this possibility, we conducted two experiments with 30 lab-based and 303 crowdsourced participants using basic mouse and touchscreen tasks. Our findings show that: (1) separately analyzing the crowd and lab data yields different study conclusions-touchscreen input was significantly less error prone than mouse input in the lab but more error prone online; (2) age-matched crowdsourced participants were significantly faster and less accurate than their lab-based counterparts, contrasting past work; (3) variability in mobile device movement and orientation increased as experimenter control decreased--a potential factor affecting the touchscreen error differences. This study cautions against assuming that crowdsourced data for performance experiments will directly reflect lab-based data, particularly for mobile devices.

Abstract Summary:Wave-spec is a pre-processing package for mass spectrometry (MS) data. The package includes several novel algorithms that overcome conventional difficulties with the pre-processing of such data. In this application note, we demonstrate step-by-step use of this package on a real-world MALDI dataset. Availability: The package can be downloaded at http://www.vicc.org/biostatistics/supp.php. A shared mailbox ([email protected]) also is available for questions regarding application of the package. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.