A Myoelectric Postural Control Algorithm for Persons With Transradial Amputations: A Consideration of Clinical Readiness

Segil, Jacob L.; Kaliki, Rahul R.; Uellendahl, Jack E.; Weir, Richard F.

doi:10.1109/mra.2019.2949688

Cited by 9 publications

(14 citation statements)

References 29 publications

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“…Our third experiment illustrated that this method can translate to additional control sites and targets. The results of Experiment 3 largely corroborate the findings of previous learning-based myoelectric interfaces that utilized the centre-out task [12]- [15], [17], [19]. These experiments demonstrate that simultaneous proportional control with multiple muscles can be achieved without the use of statistical learning algorithms.…”

Section: Discussionsupporting

confidence: 80%

“…For details of how the interface used in Experiment 1 and 2 was originally envisaged see [49]. We assume the method demonstrated in Experiment 3 could be used similarly to other centre out control schemes [13]- [15], [17], [19].…”

Section: Discussionmentioning

confidence: 99%

“…Learning-based methods, which rely upon closed-loop feedback to adapt muscle behavior, have generated significant interest as a potential alternative approach for myoelectric prosthesis control [12]- [18]. Insight into the clinical readiness of such an approach has recently been provided [19]. A common component linking many of these methods is the presentation of muscle activity as feedback in a non-representational multidimensional space, such as a centre-out task, within which users must learn to control their muscle activity.…”

mentioning

confidence: 99%

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Learning, Generalization, and Scalability of Abstract Myoelectric Control

Dyson

Dupan

Jones

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor learning-based methods offer an alternative paradigm to machine learning-based methods for controlling upper-limb prosthetics. Within this paradigm, the patterns of muscular activity used for control can differ from those which control biological limbs. Practice expedites the learning of these new, functional patterns of muscular activity. We envisage that these methods can result in enhanced control without increasing device complexity. However, key questions about training protocols, generalisation and scalability of motor learning-based methods have remained. In this work, we pursue three objectives: 1) to validate the motor learning-based abstract myoelectric control approach with people with upper-limb difference for the first time; 2) to test whether, after training, participants can generalize their learning to tasks of increased difficulty; and 3) to show that abstract myoelectric control scales with additional input signals, offering a larger control range. In three experiments, 25 limb-intact participants and 8 people with a limb difference (congenital and acquired) experienced a motor learning-based myoelectric controlled interface. We show that participants with upper-limb difference can learn to control the interface and that performance increases with experience. Across experiments, participant performance on easier lower target density tasks generalized to more difficult higher target density tasks. A proof-of-concept study demonstrates that learning-based control scales with additional myoelectric channels. Our results show that human motor learning-based approaches can enhance the number of distinct outputs from the musculature, thereby increasing the functionality of prosthetic hands and providing a viable alternative to machine learning.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Learning, Generalization, and Scalability of Abstract Myoelectric Control

Dyson

Dupan

Jones

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The value of the work presented by the authors extends beyond their immediate use of a new prosthetic hand in rehabilitation. Dyson et al (4) and Segill et al (5) proposed the Abstract/Postural control paradigm. They argued that with practice, arbitrary functional mappings between stump muscle activity and discrete prosthesis functions (i.e.…”

Section: Subject: Prostheticsmentioning

confidence: 99%

A more human prosthetic hand

Nazarpour

2020

Sci. Robot.

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“…This apparent learning has been investigated in single sessions [8], [3], in multi-day studies [9], [4], [10], [6], [7] and in multi-week studies [5], [10]. There is also a growing body of prosthesis control which uses non-biomimetic mappings between muscle activity and prosthesis grasps [11], [12], [13], [14], [15], [16]. Many of these approaches use motor-learning techniques and consequently performance improves over time [13], [15].…”

Section: Introductionmentioning

confidence: 99%

A Network-Enabled Myoelectric Platform for Prototyping Research Outside of the Lab

Dyson

Olsen

Dupan

2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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We present a network-enabled myoelectric platform for performing research outside of the laboratory environment. A low-cost, flexible, modular design based on common Internet of Things connectivity technology allows home-based research to be piloted. An outline of the platform is presented followed by technical results obtained from ten days of home-based tests with three participants. Results show the system enabled collection of close to 12,000 trials during around 28 cumulative hours of use. Home-based testing of multiple participants in parallel offers efficiency gains and provides a intuitive route toward long-term testing of upper-limb prosthetic devices in more naturalistic settings. Clinical relevance-In-home myoelectric training reduces clinician time. Network-enabled systems with back-end dashboards allow clinicians to monitor patients myoelectric ability over time and will provide a new way of accessing information about how upper-limb prosthetics are commonly used.

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A Myoelectric Postural Control Algorithm for Persons With Transradial Amputations: A Consideration of Clinical Readiness

Cited by 9 publications

References 29 publications

Learning, Generalization, and Scalability of Abstract Myoelectric Control

Learning, Generalization, and Scalability of Abstract Myoelectric Control

A more human prosthetic hand

A Network-Enabled Myoelectric Platform for Prototyping Research Outside of the Lab

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