MARCUS: a two degree of freedom hand prosthesis with hierarchical grip control

Kyberd, Peter; Holland, Owen; Chappell, P.H.; Smith, Simon; Tregidgo, R.; Bagwell, P.J.; Snaith, M.

doi:10.1109/86.372895

Cited by 112 publications

(85 citation statements)

References 8 publications

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“…2) State Machine: For a state machine (see Table I) with two or more states, the user can for example use input from other sensor modalities like force and slip sensors [39], [55]- [58] to switch between states. A special case is a sequential control system where the user can use co-contractions of antagonistic muscles [20] or a mechanical switch [59] to scroll through a sequence of available states.…”

Section: Intent Interpretationmentioning

confidence: 99%

See 1 more Smart Citation

Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review

Fougner

Stavdahl

Kyberd

et al. 2012

IEEE Trans. Neural Syst. Rehabil. Eng.

441

292

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Abstract-The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject.Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy.In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses.The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.

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Section: Intent Interpretationmentioning

confidence: 99%

“…Force-and slip-sensors were used by Todd [124], Moore [125], Nightingale [55], Chappell [56] and Kyberd [39], [57], [58] in the Southampton hand. This system was a state machine (see Section II-D2) in which two of the available states had proportional control and the mechanical inputs triggered state changes.…”

Section: ) Emg Parameter Estimatesmentioning

confidence: 99%

Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review

Fougner

Stavdahl

Kyberd

et al. 2012

IEEE Trans. Neural Syst. Rehabil. Eng.

441

292

View full text Add to dashboard Cite

show abstract

“…State machines, also referred to as event-driven finite-state schemes or binary control schemes, consist of many predefined states each with a unique function (i.e., posture or motion) that can be selected sequentially using a trigger command [19][20][21][22]. The trigger command is a physiologically inappropriate method to indicate intent; however, state machines are proven to be a clinically robust interface [23].…”

Section: Introductionmentioning

confidence: 99%

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

Segil

Weir

2015

J Rehabil Res Dev

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Abstract-The myoelectric controller (MEC) remains a technological bottleneck in the development of multifunctional prosthetic hands. Current MECs require physiologically inappropriate commands to indicate intent and lack effectiveness in a clinical setting. Postural control schemes use surface electromyography signals to drive a cursor in a continuous twodimensional domain that is then transformed into a hand posture. Here, we present a novel algorithm for a postural controller and test the efficacy of the system during two experiments with 11 total subjects. In the first experiment, we found that performance increased when a velocity cursor-control technique versus a position cursor-control technique was used. Also, performance did not change when using 3, 4, or 12 surface electrodes. In the second experiment, subjects commanded a six degree-of-freedom virtual hand into seven functional postures without training, with completion rates of 82 +/-4%, movement times of 3.5 +/-0.2 s, and path efficiencies of 45 +/ -3%. Subjects retained the ability to use the postural controller at a high level across days after a single 1 h training session. Our results substantiate the novel algorithm for a postural controller as a robust and advantageous design for a MEC of multifunction prosthetic hands.

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“…This level of dexterity and grip span is not achievable with any commercial prosthesis due to their restrictive single axis format. The device is capable of handling both rigid and compliant objects due to the intelligent SAMS control system that utilises sensor information from the hand indicating grip pressure and object slip [10,11]. Consequently active grip is only maintained when an object begins to slip from the grasp, or is requested by the user.…”

Section: Prosthesis Assembly and Evaluationmentioning

confidence: 99%

“…The Southampton Hand philosophy centres on transferring the low level reflexes of prehensile control from the user to the prosthesis [8,9]. The mechanics of these devices historically suffered from high backlash and poor reliability, however the efficacy of the hierarchical control scheme remains true to date, and has seen application in the MARCUS [10] and LO/SH hands [11].…”

Section: Introductionmentioning

confidence: 99%