Refined Myoelectric Control in Below-Elbow Amputees Using Artificial Neural Networks and a Data Glove

Sebelius, Fredrik; Rosén, Birgitta; Lundborg, Göran

doi:10.1016/j.jhsa.2005.01.002

Cited by 105 publications

(91 citation statements)

References 47 publications

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“…In this case the situation seems much better even for amputees: residual muscle activity of excellent quality has recently been found in long-term amputees [30,10,33,13].…”

Section: Applicationsmentioning

confidence: 97%

Evidence of muscle synergies during human grasping

Castellini

Smagt

2013

Biol Cybern

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Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas so far the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e., that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.

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“…In this case the situation seems much better even for amputees: residual muscle activity of excellent quality has recently been found in long-term amputees [30,10,33,13].…”

Section: Applicationsmentioning

confidence: 97%

Evidence of muscle synergies during human grasping

Castellini

Smagt

2013

Biol Cybern

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“…Among many other features, amplitude features (mean absolute value and root-mean-square value) are often used to distinguish different contraction patterns (ranging from 4 to 8) and high accuracies (between 92% and 99%) can be achieved with them [20][21][22][23][24]. Examples of frequently used classifiers in the literature are linear discriminant analysis [22,[25][26][27][28] and artificial neural networks [29][30][31]. The highest achieved accuracies for both classifiers were around 98 percent.…”

Section: Sensing Requirement 1: Multiple Selectable Wrist Movements Amentioning

confidence: 99%

“…For a higher than 4-class problem, opening and closing of the hand is often included. Studies by Tenore et al [35] and Sebelius et al [21,29] incorporated flexion and extension of the separate fingers and thumb in classification. In recent studies, more focus is found on functional grasps, such as the cylindrical, tripod, and lateral grasps [28,[36][37][38].…”

Section: Sensing Requirement 1: Multiple Selectable Wrist Movements Amentioning

confidence: 99%

Myoelectric forearm prostheses: State of the art from a user-centered perspective

Peerdeman

Boere²,

Witteveen³

et al. 2011

JRRD

413

306

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Abstract-User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity.

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“…Even worse, there is no publicly available database so far, in which both intact subjects and amputees are classified according to their clinical history. As a result, each research group chooses a different way to record, store, and process the data, while the few experiments performed on amputees (among which [3], [13], [25], [26]) are only conclusive for the (small) set of subjects considered in the corresponding study.…”

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)

213

152

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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.

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Refined Myoelectric Control in Below-Elbow Amputees Using Artificial Neural Networks and a Data Glove

Cited by 105 publications

References 47 publications

Evidence of muscle synergies during human grasping

Evidence of muscle synergies during human grasping

Myoelectric forearm prostheses: State of the art from a user-centered perspective

Building the Ninapro database: A resource for the biorobotics community

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