Neuromechanics of a Button Press

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Oulasvirta

2018

Self Cite

The activation point of a button is defined as the depth at which it invokes a make signal. Regular buttons are activated during the downward stroke, which occurs within the first 20 ms of a press. The remaining portion, which can be as long as 80 ms, has not been examined for button activation for reason of mechanical limitations. The paper presents a technique and empirical evidence for an activation technique called Impact Activation, where the button is activated at its maximal impact point. We argue that this technique is advantageous particularly in rapid, repetitive button pressing, which is common in gaming and music applications. We report on a study of rapid button pressing, wherein users' timing accuracy improved significantly with use of Impact Activation. The technique can be implemented for modern push-buttons and capacitive sensors that generate a continuous signal.

mentioning

confidence: 93%

Impact Activation Improves Rapid Button Pressing

Kim

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Oulasvirta

2018

Self Cite

“…The player must anticipate and plan the input so as to acquire the target successfully. A series of models for predicting user error rates in such anticipated input tasks has recently been published [24,[27][28][29][35][36][37]. The moving-target selection model extended in this paper [28] is the latest of these.…”

Section: Effects Of Latency On Anticipated-input Tasksmentioning

confidence: 99%

Geometrically Compensating Effect of End-to-End Latency in Moving-Target Selection Games

Kim

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

2019

Self Cite

Effects of unintended latency on gamer performance have been reported. End-to-end latency can be corrected by postinput manipulation of activation times, but this gives the player unnatural gameplay experience. For moving-target selection games such as Flappy Bird, the paper presents a predictive model of latency on error rate and a novel compensation method for the latency effects by adjusting the game's geometry design-e.g., by modifying the size of the selection region. Without manipulation of the game clock, this can keep the user's error rate constant even if the endto-end latency of the system changes. The approach extends the current model of moving-target selection with two additional assumptions about the effects of latency: (1) latency reduces players' cue-viewing time and (2) pushes the mean of the input distribution backward. The model and method proposed have been validated through precise experiments.

Virtual Reality & Intelligent Hardware

“…However, target selection is a much more microscopic movement, where the environment and users' slight psychological fluctuations may lead to large changes of trajectory. Oulasvirta et al [17] attempted to use neuromechanics to model the process in which users press a button, which also provided a solution for more elaborate interactions. One case of dynamic models that had been applied in trajectory prediction is known as social force model (SFM).…”

Section: Dynamic Modelsmentioning

confidence: 99%

Trajectory prediction model for crossing-based target selection

Zhang

Huang

Tian

et al. 2019

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some cases. Most of the research in target selection fields are focused on the analysis of the interaction results. Additionally, as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques, an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole process. Methods In this paper, a trajectory prediction model for crossingbased target selection tasks is proposed by taking the reference of a dynamic model theory. ResultsSimulation results demonstrate that our model performed well with regard to the prediction of trajectories, endpoints and hitting time for target-selection motion, and the average error of trajectories, endpoints and hitting time values were found to be 17.28%, 2.73mm and 11.50%, respectively.