Informing the Design of Novel Input Methods with Muscle Coactivation Clustering

Bachynskyi, Myroslav; Palmas, Gregorio; Oulasvirta, Antti; Weinkauf, Tino

doi:10.1145/2687921

Cited by 39 publications

(23 citation statements)

References 58 publications

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“…However, as do control-theoretical models [30,50], neuromechanics also subscribes to modeling of essential features of the plant, the body or pointer in its environment, as well as their properties such as sampling rate, latency, and gain. As in biomechanics [6], the plant model in neuromechanics captures essential anatomical and physiological factors, the bones and tissues of the fingertip in our case, as well as the mechanical properties of the button they contact. Combining these assumptions, one can study how the motor system achieves control over episodes with a button.…”

Section: Theoretical Assumptions and Scopementioning

confidence: 99%

Neuromechanics of a Button Press

Oulasvirta

Kim

Lee

2018

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

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To press a button, a finger must push down and pull up with the right force and timing. How the motor system succeeds in button-pressing, in spite of neural noise and lacking direct access to the mechanism of the button, is poorly understood. This paper investigates a unifying account based on neuromechanics. Mechanics is used to model muscles controlling the finger that contacts the button. Neurocognitive principles are used to model how the motor system learns appropriate muscle activations over repeated strokes though relying on degraded sensory feedback. Neuromechanical simulations yield a rich set of predictions for kinematics, dynamics, and user performance and may aid in understanding and improving input devices. We present a computational implementation and evaluate predictions for common button types.

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Section: Theoretical Assumptions and Scopementioning

confidence: 99%

Neuromechanics of a Button Press

Oulasvirta

Kim

Lee

2018

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

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“…Fitts' law model representing the data of all participants. they are only slightly below the throughput of uninstrumented mid-air pointing (average T P = 5.48 bits/s [1]).…”

Section: Resultsmentioning

confidence: 76%

LeviCursor

Bachynskyi

Paneva

Müller

2018

Proceedings of the 2018 ACM International Conference on Interactive Surfaces and Spaces

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Figure 1. LeviCursor enables dexterous interactive control of levitated particles. Users can control particle motion using an optical marker attached to a fingernail. Because we use the optimization-based approach to ultrasonic levitation, particle motion is smooth in any direction. We achieve round-trip latencies of 15 ms, sub-millimeter accuracy, and stability in levitation. Please see the accompanying video for demonstration. ABSTRACTWe present LeviCursor, a method for interactively moving a physical, levitating particle in 3D with high agility. The levitating object can move continuously and smoothly in any direction. We optimize the transducer phases for each possible levitation point independently. Using precomputation, our system can determine the optimal transducer phases within a few microseconds and achieves round-trip latencies of 15 ms. Due to our interpolation scheme, the levitated object can be controlled almost instantaneously with sub-millimeter accuracy. We present a particle stabilization mechanism which ensures the levitating particle is always in the main levitation trap. Lastly, we conduct the first Fitts' law-type pointing study with a real 3D cursor, where participants control the movement of the levitated cursor between two physical targets. The results of the user study demonstrate that using LeviCursor, users reach performance comparable to that of a mouse pointer.

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“…The parameters of Chen et al's model [13] describe cognitive characteristics of a user, such as the duration of a saccade when searching through the menu. In contrast to Chen et al [13], where parameters were largely set to values in the literature 4 , the inference problem that we study here is to estimate parameter values based on limited behavioral data: click times for menu items. Across the studies, we condition the parameter values of this model, and it's variants, to this type of data in different settings.…”

Section: Case: Model Of Menu Selectionmentioning

confidence: 99%

“…This is because the models are usually defined as simulators, and thus the inference is very difficult to perform using direct analytical means 2 . Such process models in HCI have been created, for example, based on cognitive science [2,9,11,16,26,41], control theory [23], biomechanics [4], game theory [10], foraging [38,37], economic choice [3], and computational rationality [13]. In the absence of principled inference methods for such models, some approaches Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Inferring Cognitive Models from Data using Approximate Bayesian Computation

Kangasrääsiö

Athukorala

Howes

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.

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Informing the Design of Novel Input Methods with Muscle Coactivation Clustering

Cited by 39 publications

References 58 publications

Neuromechanics of a Button Press

Neuromechanics of a Button Press

LeviCursor

Inferring Cognitive Models from Data using Approximate Bayesian Computation

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