A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis

Kuiken, Todd A.; Miller, Laura A.; Turner, Kristi; Hargrove, Levi J.

doi:10.1109/jtehm.2016.2616123

Cited by 112 publications

(91 citation statements)

References 17 publications

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“…However, a fair comparison was not possible because the reduced DOFs and the additional training due to daily usage simplified the tasks for the conventional control schemes. Recently, Kuiken et al (27) evaluated a classification-based system in a home study on three prosthetic users. In that study, three participants with amputation used a classificationbased prosthesis with seven functions and a customized classificationbased controller prototype.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

et al. 2018

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Myoelectric hand prostheses are usually controlled with two bipolar electrodes located on the flexor and extensor muscles of the residual limb. With clinically established techniques, only one function can be controlled at a time. This is cumbersome and limits the benefit of additional functions offered by modern prostheses. Extensive research has been conducted on more advanced control techniques, but the clinical impact has been limited, mainly due to the lack of reliability in real-world conditions. We implemented a regression-based control approach that allows for simultaneous and proportional control of two degrees of freedom and evaluated it on five prosthetic end users. In the evaluation of tasks mimicking daily life activities, we included factors that limit reliability, such as tests in different arm positions and on different days. The regression approach was robust over multiple days and only slightly affected by changing in the arm position. Additionally, the regression approach outperformed two clinical control approaches in most conditions.

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Section: Introductionmentioning

confidence: 99%

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

et al. 2018

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“…Compared to conventional EMG control, pattern recognition is more intuitive as it is based on a natural use of the muscle, in line with its function prior to the amputation. It also has the potential to incorporate more control sensors [39], thus enabling the control of more DoFs, offering easier access to multiple grips or independent movements of the fingers [40]. Pattern recognition also has the potential to offer a simultaneous multiple DoF control of the elbow, wrist, and hand [40].…”

Section: Discussionmentioning

confidence: 99%

“…It also has the potential to incorporate more control sensors [39], thus enabling the control of more DoFs, offering easier access to multiple grips or independent movements of the fingers [40]. Pattern recognition also has the potential to offer a simultaneous multiple DoF control of the elbow, wrist, and hand [40]. Some challenges remain for the implementation of pattern recognition on a large commercial scale: accuracy and consistency vary depending on the models used [122] and depend also on the level of amputation; motor control; pain; and environmental factors such as position of the limb, device weight, or weight applied on the limb, etc [123].…”

Section: Discussionmentioning

confidence: 99%

Motorized Biomechatronic Upper and Lower Limb Prostheses—Clinically Relevant Outcomes

Lechler

Frossard

Whelan

et al. 2018

PM&R

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People with major limb amputations are severely impaired when it comes to activity, body structure and function, as well as participation. Demographic statistics predict a dramatic increase of this population and additional challenges with their increasing age and higher levels of amputation. Prosthetic use has been shown to have a positive impact on mobility and depression, thereby affecting the quality of life. Biomechatronic prostheses are at the forefront of prosthetic development. Actively powered designs are now regularly used for upper limb prosthetic fittings, whereas for lower limbs the clinical use of actively powered prostheses has been limited to a very low number of applications. Actively powered prostheses enhance restoration of the lost physical functions of an amputee but are yet to allow intuitive user control. This paper provides a review of the status of biomechatronic developments in upper and lower limb prostheses in the context of the various challenges of amputation and the clinically relevant outcomes. Whereas most of the evidence regarding lower limb prostheses addresses biomechanical issues, the evidence for upper limb prostheses relates to activities of daily living (ADL) and instrumental ADL through diverse outcome measures and tools.

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“…EMG signals allow to infer the neuromuscular activity information sent to perform a specific movement. Multiple studies show the good performance of EMG signals to predict the user's intent [9][10][11][12][13][14][15][16][17]. The control system extracts the most important information from the data and maps it into a feature space.…”

Section: Introductionmentioning

confidence: 99%

Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

et al. 2020

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New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against donning/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to donning/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects donning/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.

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A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis

Cited by 112 publications

References 17 publications

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

Motorized Biomechatronic Upper and Lower Limb Prostheses—Clinically Relevant Outcomes

Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

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