GLIMPSE: Google Glass interface for sensory feedback in myoelectric hand prostheses

Marković, Marko; Karnal, Hemanth; Gramlich, Bernhard; Farina, Dario; Došen, Strahinja

doi:10.1088/1741-2552/aa620a

“…Future research should investigate the interaction between task learning and feedback and explore how each of the feedback variables (e.g., ‘Touch’ , ‘Force’ , ‘DoF-switch’ ) contributes to the overall task performance. The subjective relevance of different feedback variables that were delivered simultaneously during prosthesis grasping was investigated in a recent study [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

“…To avoid this and require that the subjects control the grasping force, we equipped each pin with a switch and a small LED. If the subjects produced an excessive force during grasping, the pin opened too much, closing the switch and activating the LED (see Figure 2D2) [ 53 ]. An activated LED simulated the breaking of the clothespin and forced the subjects to repeat the grasp.…”

Section: Methodsmentioning

confidence: 99%

The clinical relevance of advanced artificial feedback in the control of a multi-functional myoelectric prosthesis

Marković

¹

,

Schweisfurth

²

,

Engels

³

et al. 2018

J NeuroEngineering Rehabil

Self Cite

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BackgroundTo effectively replace the human hand, a prosthesis should seamlessly respond to user intentions but also convey sensory information back to the user. Restoration of sensory feedback is rated highly by the prosthesis users, and feedback is critical for grasping in able-bodied subjects. Nonetheless, the benefits of feedback in prosthetics are still debated. The lack of consensus is likely due to the complex nature of sensory feedback during prosthesis control, so that its effectiveness depends on multiple factors (e.g., task complexity, user learning).MethodsWe evaluated the impact of these factors with a longitudinal assessment in six amputee subjects, using a clinical setup (socket, embedded control) and a range of tasks (box and blocks, block turn, clothespin and cups relocation). To provide feedback, we have proposed a novel vibrotactile stimulation scheme capable of transmitting multiple variables from a multifunction prosthesis. The subjects wore a bracelet with four by two uniformly placed vibro-tactors providing information on contact, prosthesis state (active function), and grasping force. The subjects also completed a questionnaire for the subjective evaluation of the feedback.ResultsThe tests demonstrated that feedback was beneficial only in the complex tasks (block turn, clothespin and cups relocation), and that the training had an important, task-dependent impact. In the clothespin relocation and block turn tasks, training allowed the subjects to establish successful feedforward control, and therefore, the feedback became redundant. In the cups relocation task, however, the subjects needed some training to learn how to properly exploit the feedback. The subjective evaluation of the feedback was consistently positive, regardless of the objective benefits. These results underline the multifaceted nature of closed-loop prosthesis control as, depending on the context, the same feedback interface can have different impact on performance. Finally, even if the closed-loop control does not improve the performance, it could be beneficial as it seems to improve the subjective experience.ConclusionsTherefore, in this study we demonstrate, for the first time, the relevance of an advanced, multi-variable feedback interface for dexterous, multi-functional prosthesis control in a clinically relevant setting.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0371-1) contains supplementary material, which is available to authorized users.

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“…Although some studies ( e.g . 14 , 20 , 21 ) have shown that sensory feedback improves performance, others ( c.f . 14 ) have concluded that sensory feedback had no effect on performance.…”

Section: Introductionmentioning

confidence: 99%

Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control

Shehata

¹

,

Scheme

²

,

Sensinger

³

2018

Sci Rep

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Myoelectric prosthetic devices are commonly used to help upper limb amputees perform activities of daily living, however amputees still lack the sensory feedback required to facilitate reliable and precise control. Augmented feedback may play an important role in affecting both short-term performance, through real-time regulation, and long-term performance, through the development of stronger internal models. In this work, we investigate the potential tradeoff between controllers that enable better short-term performance and those that provide sufficient feedback to develop a strong internal model. We hypothesize that augmented feedback may be used to mitigate this tradeoff, ultimately improving both short and long-term control. We used psychometric measures to assess the internal model developed while using a filtered myoelectric controller with augmented audio feedback, imitating classification-based control but with augmented regression-based feedback. In addition, we evaluated the short-term performance using a multi degree-of-freedom constrained-time target acquisition task. Results obtained from 24 able-bodied subjects show that an augmented feedback control strategy using audio cues enables the development of a stronger internal model than the filtered control with filtered feedback, and significantly better path efficiency than both raw and filtered control strategies. These results suggest that the use of augmented feedback control strategies may improve both short-term and long-term performance.

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“…Vibro-tactile 13 , mechano-tactile 14 , electrotactile [15][16][17] , skin stretch 18 , and auditory 19 are just some of the techniques that have been developed and used to provide prosthesis users with feedback. Although some studies (e.g., 14,20,21 ) have shown that sensory feedback improves performance, others (c.f., 14 ) have concluded that sensory feedback had no effect on performance. This lack of consensus arises, at least in part, because of an unclear understanding of how the incorporation of feedback relates to performance.…”

Section: Introductionmentioning

confidence: 99%

“…Humans use visual feedback along with other sensory and proprioceptive information to develop their internal model, however constant visual attention and the high level of concentration it requires may lead powered prosthesis users to reject their devices 28 . Conveniently, augmented feedback can be used to convey artificial proprioceptive and exteroceptive information 21,29 , which may help to develop strong internal models. Researchers have used audio augmented feedback in both robotic teleoperation 30,31 and Brain Computer Interfaces (BCI) 32 and have concluded that audio augmented feedback improves performance.…”

Section: Introductionmentioning

confidence: 99%

Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control

Shehata

¹

,

Scheme

²

,

Sensinger

³

2018

Preprint

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Myoelectric prosthetic devices are commonly used to help upper limb amputees perform activities of daily living, however amputees still lack the sensory feedback required to facilitate reliable and precise control. Augmented feedback may play an important role in affecting both short-term performance, through real-time regulation, and long-term performance, through the development of stronger internal models. In this work, we investigate the potential tradeoff between controllers that enable better short-term performance and those that provide sufficient feedback to develop a strong internal model. We hypothesize that augmented feedback may be used to mitigate this tradeoff, ultimately improving both short and long-term control. We used psychometric measures to assess the internal model developed while using a filtered myoelectric controller with augmented audio feedback, imitating classification-based control but with augmented regression-based feedback. In addition, we evaluated the short-term performance using a multi degree-of-freedom constrained-time target acquisition task. Results obtained from 24 able-bodied subjects show that an augmented feedback control strategy using audio cues enables the development of a stronger internal model than the filtered control with filtered feedback, and significantly better path efficiency than both raw and filtered control strategies. These results suggest that the use of augmented feedback control strategies may improve both short-term and long-term performance.

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GLIMPSE: Google Glass interface for sensory feedback in myoelectric hand prostheses

Cited by 41 publications

References 46 publications

The clinical relevance of advanced artificial feedback in the control of a multi-functional myoelectric prosthesis

The clinical relevance of advanced artificial feedback in the control of a multi-functional myoelectric prosthesis

Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control

Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control

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