Control of Hand Prostheses Using Peripheral Information

Micera, Silvestro; Carpaneto, J.; Raspopović, Staniša

doi:10.1109/rbme.2010.2085429

Cited by 334 publications

(261 citation statements)

References 143 publications

Supporting

Mentioning

242

Contrasting

Unclassified

Order By: Relevance

“…Although great advances have been made in these fields, there remain many medic al and technical challenges to overcome before they are rea dy for clinical consideration. An excellent overview of this work is provided in Velliste et al [81], Micera et al [82], and Baker [83].…”

Section: Future Prospectsmentioning

confidence: 99%

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Scheme

Englehart²

2011

JRRD

806

613

View full text Add to dashboard Cite

Abstract-Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable op tion. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.

show abstract

Section: Future Prospectsmentioning

confidence: 99%

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Scheme

Englehart²

2011

JRRD

806

613

View full text Add to dashboard Cite

show abstract

“…The first example of this technique is probably [18], dating back to 45 years ago. The advancements since then have been impressive: according to two recent surveys [19], [20], up to twelve hand postures have been classified with accuracy rates between 80%, 90% and more [13], [21]. Analyses on up to twelve intact subjects [13] and up to six amputees (both trans-radials and trans-humerals) are reported.…”

Section: A Related Workmentioning

confidence: 99%

Building the Ninapro database: A resource for the biorobotics community

Atzori

Gijsberts

Heynen

et al. 2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

209

152

View full text Add to dashboard Cite

Abstract-This paper is about (self-powered) advanced hand prosthetics and their control via surface electromyography (sEMG). We hereby introduce to the biorobotics community the first version of the NINAPRO database, containing kinematic and sEMG data from the upper limbs of 27 intact subjects while performing 52 finger, hand and wrist movements of interest. The setup and experimental protocol are distilled from existing literature and thoroughly described; the data are then analysed and the results are discussed. In particular, it is clear that standard analysis techniques are no longer enough when so many subjects and movements are taken into account. The database is publicly available to download in standard ASCII format.The database is an ongoing work lasting several years, which is planned to contain data from more than 100 intact subjects and 50 trans-radial amputees; characteristics of the amputations, phantom limbs and prosthesis usage will be stored. We therefore hope that it will constitute a standard, widely accepted benchmark for all novel myoelectric hand prosthesis control methods, as well as a fundamental tool to deliver insight into the needs of trans-radial amputees.

show abstract

“…Pattern recognition algorithms have been widely investigated in terms of real-time implementation and performance [7,[10][11], and a pattern recognition-based control system has recently been commercially deployed [12]. Low classification errors, in the range of 2.2 to 11.3 percent, have been reported for varying numbers (6 to 10) of wrist and hand movements using EMG pattern recognition techniques, such as linear discriminant analysis (LDA), artificial neural networks, and support vector machines (SVMs) [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

High-density force myography: A possible alternative for upper-limb prosthetic control

Radmand¹,

Scheme²,

Englehart³

2016

J Rehabil Res Dev

108

View full text Add to dashboard Cite

Abstract-Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in higher acceptance rates. This work investigates the suitability of using highdensity force myography (HD-FMG) for prosthetic control. HD-FMG uses a high-density array of pressure sensors to detect changes in the pressure patterns between the residual limb and socket caused by the contraction of the forearm muscles. In this work, HD-FMG outperforms the standard electromyography (EMG)-based system in detecting different wrist and hand gestures. With the arm in a fixed, static position, eight hand and wrist motions were classified with 0.33% error using the HD-FMG technique. Comparatively, classification errors in the range of 2.2%-11.3% have been reported in the literature for multichannel EMG-based approaches. As with EMG, position variation in HD-FMG can introduce classification error, but incorporating position variation into the training protocol reduces this effect. Channel reduction was also applied to the HD-FMG technique to decrease the dimensionality of the problem as well as the size of the sensorized area. We found that with informed, symmetric channel reduction, classification error could be decreased to 0.02%.

show abstract

Control of Hand Prostheses Using Peripheral Information

Cited by 334 publications

References 143 publications

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Building the Ninapro database: A resource for the biorobotics community

High-density force myography: A possible alternative for upper-limb prosthetic control

Contact Info

Product

Resources

About