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

Mereu, Federico; Leone, Francesca; Gentile, Chiara; Cordella, Francesca; Gruppioni, Emanuele; Zollo, Loredana

doi:10.3390/s21061953

Cited by 22 publications

(15 citation statements)

References 65 publications

(215 reference statements)

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“…To date, the single, hierarchical and parallel classification strategies, based on the LDA classifiers, were introduced to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

Leone

Gentile

Cordella

et al. 2022

J NeuroEngineering Rehabil

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Background In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes. Methods The parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the “Elbow classifier”, the “Wrist classifier”, and the “Hand classifier” provided the simultaneous control of the elbow, hand, and wrist joints, respectively. Results Both the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist). Conclusions In this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained.

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Section: Discussionmentioning

confidence: 99%

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

Leone

Gentile

Cordella

et al. 2022

J NeuroEngineering Rehabil

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“…In recent years, new techniques were developed allowing the creation of new muscle units to obtain more precise biological signals for the high-level [ 146 , 147 , 148 ]. This aspect linked to surgery would allow increasing the quality of control overcoming the limitations of current technologies [ 149 ] promoting the use of parallel-decoding-strategies of intentional-movements information from EMGs [ 150 ].…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Human-Inspired Control Strategies for Prosthetic Hands

Gentile

Cordella

Zollo

2022

Sensors

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The abilities of the human hand have always fascinated people, and many studies have been devoted to describing and understanding a mechanism so perfect and important for human activities. Hand loss can significantly affect the level of autonomy and the capability of performing the activities of daily life. the technological improvements have led to the development of mechanically advanced commercial prostheses, the control strategies are rather simple (proportional or on/off control). The use of these commercial systems is unnatural and not intuitive, and therefore frequently abandoned by amputees. The components of an active prosthetic hand are the mechatronic device, the decoding system of human biological signals into gestures and the control law that translates all the inputs into desired movements. The real challenge is the development of a control law replacing human hand functions. This paper presents a literature review of the control strategies of prosthetics hands with a multiple-layer or hierarchical structure, and points out the main critical aspects of the current solutions, in terms of human’ functions replicated with the prosthetic device. The paper finally provides several suggestions for designing a control strategy able to mimic the functions of the human hand.

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“…Under one fixed gesture, different purposes mainly correspond to different levels of muscle strength and can be mapped to multiple control logics of the anthropomorphic hand. In the classic, such myoelectric control directly associated with the proportion of strength is regarded as the direct control (DC) approach (Mereu et al, 2021 ). By going a step further to distinguish the strength level under different postures, a more diverse purpose of control can be provided to the myoelectric hand based on the existing posture control logic.…”

Section: Methodsmentioning

confidence: 99%

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength

Zhang

Lu²,

Fan³

et al. 2022

Front. Neurorobot.

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This study aimed to highlight the demand for upper limb compound motion decoding to provide a more diversified and flexible operation for the electromyographic hand. In total, 60 compound motions were selected, which were combined with four gestures, five wrist angles, and three strength levels. Both deep learning methods and machine learning classifiers were compared to analyze the decoding performance. For deep learning, three structures and two ways of label encoding were assessed for their training processes and accuracies; for machine learning, 24 classifiers, seven features, and a combination of classifier chains were analyzed. Results show that for this relatively small sample multi-target surface electromyography (sEMG) classification, feature combination (mean absolute value, root mean square, variance, 4th-autoregressive coefficient, wavelength, zero crossings, and slope signal change) with Support Vector Machine (quadric kernel) outstood because of its high accuracy, short training process, less computation cost, and stability (p < 0.05). The decoding result achieved an average test accuracy of 98.42 ± 1.71% with 150 ms sEMG. The average accuracy for separate gestures, wrist angles, and strength levels were 99.35 ± 0.67%, 99.34 ± 0.88%, and 99.04 ± 1.16%. Among all 60 motions, 58 showed a test accuracy greater than 95%, and one part was equal to 100%.

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

Cited by 22 publications

References 65 publications

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

Hierarchical Human-Inspired Control Strategies for Prosthetic Hands

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength

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