A Nonlinear Model for Mouse Pointing Task Movement Time Analysis Based on Both System and Human Effects

Almanji, Amur; Payne, Alex; Amor, Robert; Davies, T. Claire

doi:10.1109/tnsre.2014.2377692

Cited by 7 publications

(14 citation statements)

References 33 publications

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“…Recent advances in this direction are mainly focused on their extension, e.g. a model for analyzing rapid point-and-click motions taking into account human effects has been recently proposed in [12].…”

Section: Modeling Pointing Motionsmentioning

confidence: 99%

Modeling pointing tasks in mouse-based human-computer interactions

Aranovskiy

Ushirobira

Efimov

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Pointing is a basic gesture performed by any user during human-computer interaction. It consists in covering a distance to select a target via the cursor in a graphical user interface (e.g. a computer mouse movement to select a menu element). In this work, a dynamic model is proposed to describe the cursor motion during the pointing task. The model design is based on experimental data for pointing with a mouse. The obtained model has switched dynamics, which corresponds well to the state of the art accepted in the humancomputer interaction community. The conditions of the model stability are established. The presented model can be further used for the improvement of user performance during pointing tasks.

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Section: Modeling Pointing Motionsmentioning

confidence: 99%

Modeling pointing tasks in mouse-based human-computer interactions

Aranovskiy

Ushirobira

Efimov

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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show abstract

“…The first study showed that for individuals with CP, it is more important to focus on the development of user interfaces and algorithms to increase speed because they already appear to have sophisticated accuracy [13]. The second study examined both system (width and amplitude) and human effects (errors, slips off the target, etc) that contribute to the speed and accuracy of cursor movement of youths with CP and determined that the most significant human effect is the curvature index [14]. The curvature index is the ratio of the total distance traveled to the straight-line distance between the start and end points.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Examining Dynamic Control-Display Gain Adjustments to Assist Mouse-Based Pointing for Youths with Cerebral Palsy

Manji¹,

Davies²,

Amor³

2015

VWHCI

Self Cite

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Youths with cerebral palsy encounter difficulties in target acquisition using mouse-based "point-and-click" tasks. The performance of "point-and-click computer tasks" by youths with cerebral palsy still needs improvement to allow easy target acquisition. The purpose of this study was to evaluate an algorithm intended to enhance the performance of point-andclick computer tasks for youths with cerebral palsy using a standard mouse. The curvature index-based algorithm for dynamic adjustment of control-display gain showed experimentally better performance during primary submovement, but worse performance during secondary correction sub-movements (higher number of sub-movements and longer movement time) for both typically developed youths and youths with cerebral palsy. It also showed better performance of average speed and maximum speed compared with Windows default settings. Furthermore, the average movement time, error rate, and overshoot rate for both typically developed youths and youths with cerebral palsy are higher using the curvature index-based algorithm.

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“…Despite the above approach, the issue of how to model the less-affected arm persists. Several statistical models have captured quantifiable reaching characteristics [ 24 – 26 ]; however, to the best of our knowledge, there is no appropriate model for evaluating the normal RM of stroke. Fitts’ law, which is the most well-known reaching model [ 24 ], has been applied to quantify the reaching performance of stroke survivors.…”

Section: Introductionmentioning

confidence: 99%

Novel evaluation of upper-limb motor performance after stroke based on normal reaching movement model

Moon

Kim

Hwang

et al. 2023

J NeuroEngineering Rehabil

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Background Upper-limb rehabilitation robots provide repetitive reaching movement training to post-stroke patients. Beyond a pre-determined set of movements, a robot-aided training protocol requires optimization to account for the individuals’ unique motor characteristics. Therefore, an objective evaluation method should consider the pre-stroke motor performance of the affected arm to compare one’s performance relative to normalcy. However, no study has attempted to evaluate performance based on an individual’s normal performance. Herein, we present a novel method for evaluating upper limb motor performance after a stroke based on a normal reaching movement model. Methods To represent the normal reaching performance of individuals, we opted for three candidate models: (1) Fitts’ law for the speed-accuracy relationship, (2) the Almanji model for the mouse-pointing task of cerebral palsy, and (3) our proposed model. We first obtained the kinematic data of healthy (n = 12) and post-stroke (n = 7) subjects with a robot to validate the model and evaluation method and conducted a pilot study with a group of post-stroke patients (n = 12) in a clinical setting. Using the models obtained from the reaching performance of the less-affected arm, we predicted the patients’ normal reaching performance to set the standard for evaluating the affected arm. Results We verified that the proposed normal reaching model identifies the reaching of all healthy (n = 12) and less-affected arm (n = 19; 16 of them showed an R2 > 0.7) but did not identify erroneous reaching of the affected arm. Furthermore, our evaluation method intuitively and visually demonstrated the unique motor characteristics of the affected arms. Conclusions The proposed method can be used to evaluate an individual’s reaching characteristics based on an individuals normal reaching model. It has the potential to provide individualized training by prioritizing a set of reaching movements.

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A Nonlinear Model for Mouse Pointing Task Movement Time Analysis Based on Both System and Human Effects

Cited by 7 publications

References 33 publications

Modeling pointing tasks in mouse-based human-computer interactions

Modeling pointing tasks in mouse-based human-computer interactions

Examining Dynamic Control-Display Gain Adjustments to Assist Mouse-Based Pointing for Youths with Cerebral Palsy

Novel evaluation of upper-limb motor performance after stroke based on normal reaching movement model

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