Control within a virtual environment is correlated to functional outcomes when using a physical prosthesis

Hargrove, Levi J.; Miller, Laura A.; Turner, Kristi; Kuiken, Todd A.

doi:10.1186/s12984-018-0402-y

Cited by 33 publications

(42 citation statements)

References 21 publications

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“…The selected studies have been classified into two main groups according to the used control strategy (DC or PR); one more group was devoted to papers on comparison among strategies: Direct Control strategy—six papers: Kuiken et al (2004) [ 21 ], Kuiken et al (2005) [ 31 ], Kuiken et al (2007) [ 24 ], Miller et al (2008) [ 32 ], O’Shaughnessy et al (2008) [ 33 ], and Miller et al (2008-b) [ 34 ] Pattern Recognition strategy—10 papers: Mastinu et al (2018) [ 28 ], Kuiken et al (2009) [ 35 ], Smith et al (2013) [ 36 ], Huang et al (2008) [ 37 ], Zhou et al (2007) [ 38 ], Batzianoulis et al (2019) [ 39 ], Batzianoulis et al (2018) [ 40 ], Xu et al (2018) [ 41 ], Hargrove et al (2018) [ 42 ], and Tkach et al (2014) [ 43 ]. Comparison of different types of control—four papers: Hargrove et al (2013) [ 44 ], Wurth and Hargrove (2014) [ 45 ], Hargrove et al (2017) [ 46 ], and Young et al (2014) [ 47 ] …”

Section: Methodsmentioning

confidence: 99%

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review

Mereu

Leone

Gentile

et al. 2021

Sensors

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The evolution of technological and surgical techniques has made it possible to obtain an even more intuitive control of multiple joints using advanced prosthetic systems. Targeted Muscle Reinnervation (TMR) is considered to be an innovative and relevant surgical technique for improving the prosthetic control for people with different amputation levels of the limb. Indeed, TMR surgery makes it possible to obtain reinnervated areas that act as biological amplifiers of the motor control. On the technological side, a great deal of research has been conducted in order to evaluate various types of myoelectric prosthetic control strategies, whether direct control or pattern recognition-based control. In the literature, different control performance metrics, which have been evaluated on TMR subjects, have been introduced, but no accepted reference standard defines the better strategy for evaluating the prosthetic control. Indeed, the presence of several evaluation tests that are based on different metrics makes it difficult the definition of standard guidelines for comprehending the potentiality of the proposed control systems. Additionally, there is a lack of evidence about the comparison of different evaluation approaches or the presence of guidelines on the most suitable test to proceed for a TMR patients case study. Thus, this review aims at identifying these limitations by examining the several studies in the literature on TMR subjects, with different amputation levels, and proposing a standard method for evaluating the control performance metrics.

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Section: Methodsmentioning

confidence: 99%

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review

Mereu

Leone

Gentile

et al. 2021

Sensors

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“…Therefore, an amputee requires training to master the myoelectric control [9]. Different training systems have been presented in the past, from simple visualization of myoelectric signals [10] to using these signals to play computer games [11][12][13] or control a virtual prosthesis shown on the screen [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The physics was also simulated, and a virtual Box and Block test was implemented and assessed in three able-bodied subjects. A recent study presented a virtual reality framework for interactive training and assessment of pattern classification myoelectric control integrating the target achievement control test [16,17] and serious gaming (controlling a virtual crossbow) [20]. Although virtual reality systems can be effective instruments for myoelectric control training, they also have drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

Boschmann

Neuhaus

Vogt

et al. 2021

J NeuroEngineering Rehabil

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Background Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training. Methods In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback. Results The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7). Conclusion The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development.

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“…Several online comparisons have demonstrated differences among control strategies [28,30,31,37,42,[54][55][56][57][58]. The relationship of online performance to real-world performance is debated [59][60][61]. Comparing decoders through real-world activities of daily living provides the most realistic approximation of how a decoder would function in everyday life.…”

Section: Introductionmentioning

confidence: 99%

Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

Paskett

Brinton

Hansen

et al. 2021

J NeuroEngineering Rehabil

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Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

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Control within a virtual environment is correlated to functional outcomes when using a physical prosthesis

Cited by 33 publications

References 21 publications

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review

Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

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