Two-degree-of-freedom powered prosthetic wrist

Kyberd, Peter; Lemaire, Edward D.; Scheme, Erik; MacPhail, Catherine M.; Goudreau, Louis; Bush, Greg; Brookeshaw, Marcus

doi:10.1682/jrrd.2010.07.0137

Cited by 46 publications

(31 citation statements)

References 17 publications

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“…The only restriction imposed for these movements was that each single DoF was activated at a time. For all subjects, except AV4, the selected DoFs were wrist flexion/extension (DoF1) and wrist pronation/supination (DoF2), as these two DoFs are functionally the most important for transradial amputees [12] other than intrinsic hand functions, and there are recent attempts to realize prostheses that have active control of these two DoFs [13]. For AV4 (transhumeral amputation), the two articulated DoFs were elbow flexion/extension and hand open/close, because these were the only functions available after a targeted muscle reinnervation surgery (TMR) [14] following the amputation, and are functionally most important for this user.…”

Section: B Experiments Protocolmentioning

confidence: 99%

Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees

Jiang

Rehbaum

Vujaklija

et al. 2014

IEEE Trans. Neural Syst. Rehabil. Eng.

245

229

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We propose an approach for online simultaneous and proportional myoelectric control of two degrees-of-freedom (DoF) of the wrist, using surface electromyographic signals. The method is based on the nonnegative matrix factorization (NMF) of the wrist muscle activation to extract low-dimensional control signals translated by the user into kinematic variables. This procedure does not need a training set of signals for which the kinematics is known (labeled dataset) and is thus unsupervised (although it requires an initial calibration without labeled signals). The estimated control signals using NMF are used to directly control two DoFs of wrist. The method was tested on seven subjects with upper limb deficiency and on seven able-bodied subjects. The subjects performed online control of a virtual object with two DoFs to achieve goal-oriented tasks. The performance of the two subject groups, measured as the task completion rate, task completion time, and execution efficiency, was not statistically different. The approach was compared, and demonstrated to be superior to the online control by the industrial state-of-the-art approach. These results show that this new approach, which has several advantages over the previous myoelectric prosthetic control systems, has the potential of providing intuitive and dexterous control of artificial limbs for amputees.

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Section: B Experiments Protocolmentioning

confidence: 99%

Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees

Jiang

Rehbaum

Vujaklija

et al. 2014

IEEE Trans. Neural Syst. Rehabil. Eng.

245

229

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show abstract

“…Natural limb control involves simultaneous movement of multiple degrees of freedom (DOF), which provides significant functionality during activities of daily living (such as simultaneous hand closing and wrist rotation to turn a door knob). Recent multifunction prosthetic hands [1-5] and advanced arm system prototypes described in the literature (which include 2 DOF wrist units) [6-8] offer the mechanical means to restore such function. However, an intuitive method of control is desirable to direct the multi-DOF movements provided by these devices.…”

Section: Introductionmentioning

confidence: 99%

Classification of Simultaneous Movements Using Surface EMG Pattern Recognition

Young

Smith

Rouse

et al. 2013

IEEE Trans. Biomed. Eng.

285

169

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Advanced upper-limb prostheses capable of actuating multiple degrees of freedom (DOF) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one degree of freedom at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using non-amputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for non-amputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p<0.05) than a single LDA classifier or a parallel approach. For 3-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.

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“…A two degree of freedom wrist based on a differential, details of the device are in [19]. The two axes control both flexion/extension and pro/supination, thus they must both be driven whenever the wrist is moved.…”

Section: B Second Example -Two Axis Wristmentioning

confidence: 99%

Use of Accelerometers in the Control of Practical Prosthetic Arms

Kyberd

Poulton

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Accelerometers can be used to augment the control of powered prosthetic arms. They can detect the orientation of the joint and limb, and the controller can correct for the amount of torque required to move the limb. They can also be used to create a platform, with a fixed orientation relative to gravity for the object held in the hand. This paper describes three applications for this technology, in a powered wrist and powered arm. By adding sensors to the arm making these data available to the controller, the input from the user can be made simpler. The operator will not need to correct for changes in orientation of their body as they move. Two examples of the correction for orientation against gravity are described and an example of the system designed for use by a patient. The controller for all examples is a distributed set of microcontrollers, one node for each joint, linked with the control area network bus. The clinical arm uses a version of the Southampton adaptive manipulation scheme to control the arm and hand. In this control form, the user gives simpler input commands and leaves the detailed control of the arm to the controller.

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Two-degree-of-freedom powered prosthetic wrist

Cited by 46 publications

References 17 publications

Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees

Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees

Classification of Simultaneous Movements Using Surface EMG Pattern Recognition

Use of Accelerometers in the Control of Practical Prosthetic Arms

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