Is it Finger or Wrist Dexterity That is Missing in Current Hand Prostheses?

Montagnani, Federico; Controzzi, Marco; Cipriani, Christian

doi:10.1109/tnsre.2015.2398112

Cited by 128 publications

(97 citation statements)

References 37 publications

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“…Since the forearm contains muscles that move both the fingers and the wrist, the user must generate EMG activity to control the prosthetic fingers without significant wrist movement, which may generate myoelectric signals that disrupt control [8]. One recent study showed that when non-amputees are limited to two degrees of freedom at the wrist (pronation/supination and flexion/extension) and 1° of freedom at the hand (open/close), they perform similarly to when they are limited to a 1°-of-freedom rotating wrist coupled with their natural 22°-of-freedom hand [13]. Thus, a clinically successful partial-hand pattern recognition control system must both provide high performance accuracy and allow the individual to retain use of their wrist.…”

Section: Introductionmentioning

confidence: 99%

Resolving the effect of wrist position on myoelectric pattern recognition control

Adewuyi

Hargrove

Kuiken

2017

J NeuroEngineering Rehabil

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BackgroundThe use of pattern recognition-based methods to control myoelectric upper-limb prostheses has been well studied in individuals with high-level amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who often possess a functional wrist. This study’s objective was to evaluate strategies that allow partial-hand amputees to control a prosthetic hand while allowing retain wrist function.MethodsEMG data was recorded from the extrinsic and intrinsic hand muscles of six non-amputees and two partial-hand amputees while they performed 4 hand motions in 13 different wrist positions. The performance of 4 classification schemes using EMG data alone and EMG data combined with wrist positional information was evaluated. Using recorded wrist positional data, the relationship between EMG features and wrist position was modeled and used to develop a wrist position-independent classification scheme.ResultsA multi-layer perceptron artificial neural network classifier was better able to discriminate four hand motion classes in 13 wrist positions than a linear discriminant analysis classifier (p = 0.006), quadratic discriminant analysis classifier (p < 0.0001) and a linear perceptron artificial neural network classifier (p = 0.04). The addition of wrist position data to EMG data significantly improved performance (p < 0.001). Training the classifier with the combination of extrinsic and intrinsic muscle EMG data performed significantly better than using intrinsic (p < 0.0001) or extrinsic muscle EMG data alone (p < 0.0001), and training with intrinsic muscle EMG data performed significantly better than extrinsic muscle EMG data alone (p < 0.001). The same trends were observed for amputees, except training with intrinsic muscle EMG data, on average, performed worse than the extrinsic muscle EMG data. We propose a wrist position–independent controller that simulates data from multiple wrist positions and is able to significantly improve performance by 48–74% (p < 0.05) for non-amputees and by 45–66% for partial-hand amputees, compared to a classifier trained only with data from a neutral wrist position and tested with data from multiple positions.ConclusionsSensor fusion (using EMG and wrist position information), non-linear artificial neural networks, combining EMG data across multiple muscle sources, and simulating data from different wrist positions are effective strategies for mitigating the wrist position effect and improving classification performance.

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Section: Introductionmentioning

confidence: 99%

Resolving the effect of wrist position on myoelectric pattern recognition control

Adewuyi

Hargrove

Kuiken

2017

J NeuroEngineering Rehabil

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“…Partial-hand amputees often retain the ability to move their wrists, and preservation of residual wrist motion is critical for functional everyday activities. Montagnani et al ( 2015 ) showed that when non-amputees are limited to two degrees of freedom at the wrist (pronation/supination and flexion/extension) and one degree of freedom at the hand (open/close), they perform similarly to when they are limited to a one degree-of-freedom wrist (rotation) coupled with their intact, twenty-two degree-of-freedom hand. Thus, a clinically successful partial-hand pattern recognition control system must maintain high performance while allowing the individual to use their wrist.…”

Section: Introductionmentioning

confidence: 99%

Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control

2016

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Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.

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“…Second, the movements included in the original dataset are missing in some cases that could potentially improve the estimation of the dimensionality of forearm muscles, a notable one being the extension of all ngers. Also, the dataset includes only a few movements that focus on coordinated motion of the ngers and wrist, which are functionally important for manual dexterity [46]. Future studies that quantify muscle control capabilities in amputees should consider these limitations and adjust the experimental protocol accordingly.…”

Section: Methodological Limitationsmentioning

confidence: 99%

Dimensionality of forearm muscle activation in transradial amputees

McClanahan¹,

Moench²,

Fu³

2020

Preprint

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Background Establishing a natural communication interface between the user and the terminal device is one of the central challenges of hand neuroprosthetics research. Surface electromyography (EMG) is the most common source of neural signals for interpreting a user’s intent in these interfaces. However, how the capacity of EMG generation is affected by various clinical parameters remains largely unknown. Methods In this retrospective study using an open source database, we examined the EMG activity of forearm muscles recorded from 11 transradially amputated subjects and 40 able-bodied subjects who performed a wide range of movements. By using non-negative matrix factorization, we extracted the synergistic EMG patterns for each subject to estimate the dimensionality of muscle control, under the framework of motor synergies. Results We found that amputees exhibited less than four synergies (with substantial variability related to the length of remaining limb and age), whereas able-bodied subjects commonly demonstrate five or more synergies. Conclusions The results of this study provide novel insight into the muscle synergy framework and the design of natural myoelectric control interfaces.

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Is it Finger or Wrist Dexterity That is Missing in Current Hand Prostheses?

Cited by 128 publications

References 37 publications

Resolving the effect of wrist position on myoelectric pattern recognition control

Resolving the effect of wrist position on myoelectric pattern recognition control

Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control

Dimensionality of forearm muscle activation in transradial amputees

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