Control of prehension for the transradial prosthesis: Natural-like image recognition system

Klisic, Djordje; Kostić, Miloš; Došen, Strahinja; Popović, Dejan B.

doi:10.2298/jac0901027k

Cited by 14 publications

(6 citation statements)

References 3 publications

(2 reference statements)

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“…There have been few studies that focused on utilising vision for hand prostheses. For example, in the work presented in [6,7,9], a dexterous hand was provided with vision and an autonomous controller. After triggering the hand and controlling its orientation by the user, a cognitive vision system (CVS) captured an image of the object.…”

Section: Introductionmentioning

confidence: 99%

An exploratory study on the use of convolutional neural networks for object grasp classification

Ghazaei

Alameer

Degenaar

et al. 2015

2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

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The loss of hand profoundly affects an individual's quality of life. Prosthetic hands can provide a route to functional rehabilitation by allowing the amputees to undertake their daily activities. However, the performance of current artificial hands falls well short of the dexterity that natural hands offer. The aim of this study is to test whether an intelligent vision system could be used to enhance the grip functionality of prosthetic hands. To this end, a convolutional neural network (CNN) deep learning architecture was implemented to classify the objects in the COIL100 database in four basic grasp groups: tripod, pinch, palmar and palmar with wrist rotation. Our preliminary, yet promising, results suggest that the additional machine vision system can provide prosthetic hands with the ability to detect object and propose the user an appropriate grasp.

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Section: Introductionmentioning

confidence: 99%

An exploratory study on the use of convolutional neural networks for object grasp classification

Ghazaei

Alameer

Degenaar

et al. 2015

2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

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“…The other application that is envisioned relates to the inclusion of cognitive vision in the control loop for transradial prosthesis [ 41 , 42 ]. In this case, the camera is integrated into artificial hand; therefore, the camera moves and generates the altered visual input, which the controller needs to adapt to.…”

Section: Discussionmentioning

confidence: 99%

Learning Arm/Hand Coordination with an Altered Visual Input

Nielsen

Došen

Popović

et al. 2010

Computational Intelligence and Neuroscience

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The focus of this study was to test a novel tool for the analysis of motor coordination with an altered visual input. The altered visual input was created using special glasses that presented the view as recorded by a video camera placed at various positions around the subject. The camera was positioned at a frontal (F), lateral (L), or top (T) position with respect to the subject. We studied the differences between the arm-end (wrist) trajectories while grasping an object between altered vision (F, L, and T conditions) and normal vision (N) in ten subjects. The outcome measures from the analysis were the trajectory errors, the movement parameters, and the time of execution. We found substantial trajectory errors and an increased execution time at the baseline of the study. We also found that trajectory errors decreased in all conditions after three days of practice with the altered vision in the F condition only for 20 minutes per day, suggesting that recalibration of the visual systems occurred relatively quickly. These results indicate that this recalibration occurs via movement training in an altered condition. The results also suggest that recalibration is more difficult to achieve for altered vision in the F and L conditions compared to the T condition. This study has direct implications on the design of new rehabilitation systems.

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“…Many current prosthetics, including myoelectric prosthetic upper and lower limbs, employ EMG-based methods for sensing, detection, and feedback processing of motor control signals. Over the recent years, there has been a growing body of evidence establishing the utilization of tri-axial MEMs (Micro Electro-Mechanical System) sensor accelerometers as an alternative or composite sensorimotor feedback platform for use in rehabilitation [4–7]. Here, we suggest a novel design for accelerometers for the implementation of this model, which could allow multiple degrees of freedom (DOF) sensors built into prosthetic and orthotic wearable.…”

Section: Introductionmentioning

confidence: 99%

Development and design specifications for an accelerometer-based biomechanical, prosthetic sensorimotor platform

Johnson

Mardon

2020

Preprint

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Following stroke, injury, or exposure to physically limiting conditions, limbs can become physiologically compromised. In particular, motor and fine-dexterity tasks involving the arm, particularly in locomotion, grasp and release, can be influenced becoming either delayed and having to deal with greater force demands. Current prosthetic systems use electromyography (EMG)-based techniques for creating functional sensorimotor platforms. However, several limitations in practical use and signal detection have been identified in these systems. Accelerometer-based sensorimotor systems have been suggested to overcome these limitations but only proof-of-concept has been demonstrated. Here, we explore design specifications for accelerometers being developed for prosthetic integration. We have developed optimizations for the current model, evaluated system properties to enhance sensitivity and reduce signal noise, and performed a pilot test using simulation to test this model. The data suggest these novel design parameters can enhance signal detection, when compared to conventional accelerometers. Future avenues should focus on validation of this design prototype in a full prosthetic system.

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Control of prehension for the transradial prosthesis: Natural-like image recognition system

Cited by 14 publications

References 3 publications

An exploratory study on the use of convolutional neural networks for object grasp classification

An exploratory study on the use of convolutional neural networks for object grasp classification

Learning Arm/Hand Coordination with an Altered Visual Input

Development and design specifications for an accelerometer-based biomechanical, prosthetic sensorimotor platform

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