Exploring speed–accuracy tradeoff in reaching movements: a neurocomputational model

Parziale, Antonio; Senatore, Rosa; Marcelli, Angelo

doi:10.1007/s00521-019-04690-z

Cited by 14 publications

(8 citation statements)

References 109 publications

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“…As we all know, “static” is the biggest attribute of an image. In an image, the discriminative features of one area point are very likely to be applicable to other areas [ 18 , 19 ]. Therefore, it appears to calculate the mean or maximum value of the features in a certain area of the image and use it to represent the features of the area.…”

Section: Neural Network Model Based On Volleyball Arm Recognitionmentioning

confidence: 99%

Recognition of Volleyball Player’s Arm Motion Trajectory and Muscle Injury Mechanism Analysis Based upon Neural Network Model

Zhao

2022

Journal of Healthcare Engineering

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At present, in sports training for volleyball, it still mainly depends on the personal experience of the coach. Training costs are high, and the quality is difficult to maintain stable. Even with the introduction of training assistance software, it is often necessary to manually enter complex data, and the research samples are mostly single individuals. Serving is one of the basic and important technical movements of volleyball, and its standardization is of great significance to the stable performance of the scene. This article proposes an analysis of the volleyball player’s arm trajectory based on the background of human posture recognition and analysis, based on the neural network model. The changes in the angles of the shoulders, elbows, and wrist when serving the ball reflect the different trajectories of the arm. Experiments show that the height of the throwing arm from the ground accounts for 98% of the height. The horizontal angle of the throwing arm at the moment the ball leaves the hand is positively correlated with the throwing time and height, and the reasonable trajectory has an impact on the stability of the throwing ball. The closer the trajectory of the tossing arm is to the vertical, the more stable the tossing is.

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Section: Neural Network Model Based On Volleyball Arm Recognitionmentioning

confidence: 99%

Recognition of Volleyball Player’s Arm Motion Trajectory and Muscle Injury Mechanism Analysis Based upon Neural Network Model

Zhao

2022

Journal of Healthcare Engineering

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“…In terms of control strategies, the subjects employed relatively more intermittent feedback control at the slow target speeds whereas feedforward control was dominant at the fast target speeds. This tradeoff relationship has been interpreted between speed and accuracy by establishing computational models [56,57]. Applying those mathematical models in our results is anticipated to better explain the speed-dependent characteristics of the human motor control mechanism.…”

Section: Clinical Application and Future Workmentioning

confidence: 87%

Analysis of Control Characteristics between Dominant and Non-Dominant Hands by Transient Responses of Circular Tracking Movements in 3D Virtual Reality Space

Park

Choi

et al. 2020

Sensors

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Human movement is a controlled result of the sensory-motor system, and the motor control mechanism has been studied through diverse movements. The present study examined control characteristics of dominant and non-dominant hands by analyzing the transient responses of circular tracking movements in 3D virtual reality space. A visual target rotated in a circular trajectory at four different speeds, and 29 participants tracked the target with their hands. The position of each subject’s hand was measured, and the following three parameters were investigated: normalized initial peak velocity (IPV2), initial peak time (IPT2), and time delay (TD2). The IPV2 of both hands decreased as target speed increased. The results of IPT2 revealed that the dominant hand reached its peak velocity 0.0423 s earlier than the non-dominant hand, regardless of target speed. The TD2 of the hands diminished by 0.0218 s on average as target speed increased, but the dominant hand statistically revealed a 0.0417-s shorter TD2 than the non-dominant hand. Velocity-control performances from the IPV2 and IPT2 suggested that an identical internal model controls movement in both hands, whereas the dominant hand is likely more experienced than the non-dominant hand in reacting to neural commands, resulting in better reactivity in the movement task.

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“…In addition, we plan to use the data gathered by HaReS to train machine learning methods that can automatically predict whether the user is improving or not by reviewing their history, or to state whether a user is prone to suffer any hand motor or cognitive disease such as Parkinson's. We plan to also involve computational models [58][59][60] to produce EMG signals that would enable to provide an automatic analysis to assist therapists to understand the sEMG signals provided by HaReS.…”

Section: Discussionmentioning

confidence: 99%

A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot

2021

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In this work, we introduce HaReS, a hand rehabilitation system. Our proposal integrates a series of exercises, jointly developed with a foundation for those with motor and cognitive injuries, that are aimed at improving the skills of patients and the adherence to the rehabilitation plan. Our system takes advantage of a low-cost hand-tracking device to provide a quantitative analysis of the performance of the patient. It also integrates a low-cost surface electromyography (sEMG) sensor in order to provide insight about which muscles are being activated while completing the exercises. It is also modular and can be deployed on a social robot. We tested our proposal in two different facilities for rehabilitation with high success. The therapists and patients felt more motivation while using HaReS, which improved the adherence to the rehabilitation plan. In addition, the therapists were able to provide services to more patients than when they used their traditional methodology.

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Exploring speed–accuracy tradeoff in reaching movements: a neurocomputational model

Cited by 14 publications

References 109 publications

Recognition of Volleyball Player’s Arm Motion Trajectory and Muscle Injury Mechanism Analysis Based upon Neural Network Model

Recognition of Volleyball Player’s Arm Motion Trajectory and Muscle Injury Mechanism Analysis Based upon Neural Network Model

Analysis of Control Characteristics between Dominant and Non-Dominant Hands by Transient Responses of Circular Tracking Movements in 3D Virtual Reality Space

A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot

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