Effects of Mouse, Trackpad and 3d Motion and Gesture Control on Performance, Posture, and Comfort

Kar, Gourab; Vu, Amy; Nehme, Begoña Juliá; Hedge, Alan

doi:10.1177/1541931215591068

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…On the other hand, we see that a closer distance to the table might have led to uncomfortable positions for the users, which prolonged the time in usr , thus leading to an overall worse scoring of the physical ergonomics in usr . This is similar to what was discovered by ( Kar et al, 2015 ), where faster movements led to lower operator risk. However, this held true in just one scenario, and the overall analysis yielded that operators in usr were, on average, farther away, as shown in Section 3.3 .…”

Section: Discussionsupporting

confidence: 91%

Influence of task decision autonomy on physical ergonomics and robot performances in an industrial human–robot collaboration scenario

Pantano

Yang²,

Blumberg³

et al. 2022

Front. Robot. AI

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Adoption of human–robot collaboration is hindered by barriers in collaborative task design. A new approach for solving these problems is to empower operators in the design of their tasks. However, how this approach may affect user welfare or performance in industrial scenarios has not yet been studied. Therefore, in this research, the results of an experiment designed to identify the influences of the operator’s self-designed task on physical ergonomics and task performance are presented. At first, a collaborative framework able to accept operator task definition via parts’ locations and monitor the operator’s posture is presented. Second, the framework is used to tailor a collaborative experience favoring decision autonomy using the SHOP4CF architecture. Finally, the framework is used to investigate how this personalization influences collaboration through a user study with untrained personnel on physical ergonomics. The results from this study are twofold. On one hand, a high degree of decision autonomy was felt by the operators when they were allowed to allocate the parts. On the other hand, high decision autonomy was not found to vary task efficiency nor the MSD risk level. Therefore, this study emphasizes that allowing operators to choose the position of the parts may help task acceptance and does not vary operators’ physical ergonomics or task efficiency. Unfortunately, the test was limited to 16 participants and the measured risk level was medium. Therefore, this study also stresses that operators should be allowed to choose their own work parameters, but some guidelines should be followed to further reduce MSD risk levels.

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Section: Discussionsupporting

confidence: 91%

Influence of task decision autonomy on physical ergonomics and robot performances in an industrial human–robot collaboration scenario

Pantano

Yang²,

Blumberg³

et al. 2022

Front. Robot. AI

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show abstract

“…This view is consistent with prior work showing that trackpads are empirically and subjectively less effective and less desired than a mouse or touchscreen input for controlling digital interfaces in the workplace (Ulrich, et al, 2015). Similarly, there is ample evidence that trackpads are less efficient and desirable than a mouse for standard computing tasks (Kar, et al, 2015; Maleckar, et al, 2016; Shanis & Hedge, 2003). Future work will be necessary to determine whether slowed reaction times in our VMR task with a trackpad is related to cognitive demands or motor control efficiency.…”

Section: Discussionmentioning

confidence: 99%

Comparing mouse versus trackpad input in a web-based app for assessing motor learning

Watral,

Morley,

Pastel

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Current laboratory approaches to measuring motor learning are not accessible to all populations, limiting research about developmental processes and medical conditions that impact motor control. We recently created a web-based application remote assessment of visuomotor adaptation, a gold-standard approach to studying motor learning. Previously, we validated this application in younger and older adults. However, preliminary analyses suggested that the input device (mouse or trackpad) may have impacted performance. The current study directly evaluated performance differences in younger adults using the application with a mouse compared to a trackpad. Results showed no statistically significant differences in learning curves or movement times between groups, but reaction time was significantly faster in mouse users. While the input device had very little impact on motor learning, slower reaction time when using a trackpad may be related to increased cognitive demands or reduced movement efficiency compared to using a mouse for this task.

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“…However, while the use of Fitts tasks to assess performance between two touchpads is a common practice, there does not seem to be a widely used standard regarding the number of trials and participants. Specifically, a preliminary review of the literature has revealed that research assessing performance of pointing devices through Fitts tasks range from 10 to 36 participants (Avera, Harper, Russi-Vigoya & Stoll, 2016;Rawdin, Vanderheiden & Lin, 1990;Sawin & Ark, 1999;Sutter & Ziefle, 2005) and from 20 to 640 trials (Bohan, Slocum, Shaikh & Chaparro, 2003;David, Peres & Harper, 2013;Kar, Vu, Nehme & Hedge, 2015). Often, industry practitioners in particular are interested in individual device assessments as well as finding differences between two touchpads.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Optimal Number of Trials and Participants for Touchpad Testing Using Fitts-based Tasks

Avera

Kelling

Harper

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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With touchpads being the primary input device for many laptop users, the importance of ensuring that laptops have the most functional touchpad has continued to grow. Human Factors practitioners are often tasked with ensuring that touchpads are accurate and efficient input devices. Tasks based on Fitts law to assess point and click performance are commonly used for testing touchpads, however there does not seem to be a widely used standard regarding the number of trials and participants. Testing with more trials and participants than necessary can be time consuming and costly for industry practitioners. This research explored varied numbers of participants and Fitts task trials with the goal of determining the optimal practices to maximize efficiency. Preliminary results indicate that when testing performance with a multidirectional Fitts test, twelve participants is acceptable for device comparison with at least 48 trials. For a sample size of 24 participants, there is evidence to support reducing the number of trials to 24. Implications for examining other testing scenarios for optimal sample size and trial number with the prescribed methodology are discussed.

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Effects of Mouse, Trackpad and 3d Motion and Gesture Control on Performance, Posture, and Comfort

Cited by 9 publications

References 15 publications

Influence of task decision autonomy on physical ergonomics and robot performances in an industrial human–robot collaboration scenario

Influence of task decision autonomy on physical ergonomics and robot performances in an industrial human–robot collaboration scenario

Comparing mouse versus trackpad input in a web-based app for assessing motor learning

Exploring the Optimal Number of Trials and Participants for Touchpad Testing Using Fitts-based Tasks

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