Pointing with the ankle: the speed-accuracy trade-off

Michmizos, Konstantinos P.; Krebs, Hermano Igo

doi:10.1007/s00221-013-3773-0

Cited by 21 publications

(17 citation statements)

References 52 publications

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“…We then assessed their explicit learning by examining how the game parameters adapted to their performance and their implicit learning by analyzing how the distribution of their RT changed with therapy. Our evaluation was supported by our recent studies on the ankle sensorimotor control of young healthy subjects [25, 26, 29, 30]. …”

Section: Discussionsupporting

confidence: 63%

“…Specifically, our recent finding that the performance in visually evoked, visually guided ankle pointing movements is described by a linear function, as predicted by Fitts’ law, supported the idea that the SAT could be incorporated into an adaptive therapeutic intervention for the ankle [25]. In that sense, the SGs trained the child's ankle while challenging his/her ability to move fast and accurately: Depending on one's ability to aim, the targets became smaller or larger; depending on one's ability to move fast or slow, the speed of the game also changed.…”

Section: Prototype Designmentioning

confidence: 82%

“…To address this limitation, we recently established the existence of SAT in goal-directed ankle pointing movements in DP and IE, in straight analogy to a linear model, widely used to quantify the UE motor system for more than half a century (Fitts’ law) [25]. This enabled the design of the control architecture on the basis of a robust adaptive approach that tracks the performance in speed and accuracy as indicators of each patient's abilities to move and point: A PM2 and PM3 decrease indicated a faster and more accurate movement (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…To overcome this pitfall, we recently studied the sensorimotor control of ankle pointing movements at 3 modeling levels. In our first, macroscopic study, we demonstrated the adequacy of Fitts’ law to describe the mean time of major ankle pointing movements and to support the use of linear models to predict the ankle average performance in dorsal-plantar (DP) and inversion-eversion (IE) directions in healthy subjects [25]; this study verified that the central nervous system commands and controls the speed and accuracy of ankle movement in both DP and IE movements in the same way as in UE. In our second, mesoscopic study, we found a remarkable similarity between the models that described the speed profiles of unimpaired ankle pointing movements and the ones previously found for the upper extremities both during arm reaching and wrist pointing movements.…”

Section: Introductionmentioning

confidence: 99%

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Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation

Michmizos

Rossi

Castelli³

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

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This paper presents the pediAnklebot, an impedance-controlled low-friction, backdriveable robotic device developed at the Massachusetts Institute of Technology that trains the ankle of neurologically impaired children of ages 6-10 years old. The design attempts to overcome the known limitations of the lower extremity robotics and the unknown difficulties of what constitutes an appropriate therapeutic interaction with children. The robot's pilot clinical evaluation is on-going and it incorporates our recent findings on the ankle sensorimotor control in neurologically intact subjects, namely the speed-accuracy tradeoff, the deviation from an ideally smooth ankle trajectory, and the reaction time. We used these concepts to develop the kinematic and kinetic performance metrics that guided the ankle therapy in a similar fashion that we have done for our upper extremity devices. Here we report on the use of the device in at least 9 training sessions for 3 neurologically impaired children. Results demonstrated a statistically significant improvement in the performance metrics assessing explicit and implicit motor learning. Based on these initial results, we are confident that the device will become an effective tool that harnesses plasticity to guide habilitation during childhood.

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Section: Discussionsupporting

confidence: 63%

Section: Prototype Designmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation

Michmizos

Rossi

Castelli³

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Tasks where Fitts’ law has been shown to be a good descriptor of movement times include using a stylus to tap a target region of a predefined size (Fitts 1954), transferring pins to holes of different sizes (Fitts 1954), grasping objects (Bootsma et al 1994), using a joystick or mouse to move a cursor on a monitor (Jagacinski, Hartzell, and Ward 1978; Epps 1986; Kantowitz and Elvers 1988), and making pointing movements with a variety of joints (Michmizos and Krebs 2014; Langolf, Chaffin, and Foulke 1976; Corcos, Gottlieb, and Agarwal 1988; Leisman 1989). Fitts’ law expresses two properties of the able-bodied motor system that hold generally (though not always strictly) across a wide range of tasks:…”

Section: Introductionmentioning

confidence: 99%

Signal-independent noise in intracortical brain–computer interfaces causes movement time properties inconsistent with Fitts’ law

et al. 2017

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Objective Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts’ law: MT = a + b log2(D/R ) (where MT is movement time, D is target distance, R is target radius, and a,b are parameters). Fitts’ law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio D/R) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to D/R). Approach Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts’ law. Main Results We found that movement times were better described by the equation MT = a + bD + cR−2, which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the D/R ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user’s motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. Significance The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts’ law-like relationship to iBCI movements may require nonlinear decoding strategies.

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