Analysis of Man-Machine Interfaces in Upper-Limb Prosthesis: A Review

Ribeiro, José Cairo Silva; Mota, Francisco Alan Xavier da; Cavalcante, Tarique da Silveira; Nogueira, Ingrid Correia; Gondim, Victor José Timbó; Albuquerque, Victor Hugo C. de; Alexandria, Auzuir Ripardo de

doi:10.3390/robotics8010016

Cited by 47 publications

(36 citation statements)

References 44 publications

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“…Additional HMI technologies exist that were not covered by this article [42], [115]. Common non-invasive HMIs include brain-computer interfaces like electroencephalography [116] (EEG), and near-infrared spectroscopy [117].…”

Section: Discussionmentioning

confidence: 99%

Transferrable Expertise From Bionic Arms to Robotic Exoskeletons: Perspectives for Stroke and Duchenne Muscular Dystrophy

Nizamis

Stienen

Kamper

et al. 2019

IEEE Trans. Med. Robot. Bionics

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Upper extremity function is affected by a variety of neurological conditions. Robotic exoskeletons offer a potential solution for motor restoration. However, their systematic adoption is limited by challenges relative to human intention detection and device control. This position article offers a focused perspective on this topic. That is, on how knowledge gained from the design and implementation of human-machine interfaces (HMIs) for bionic arms can benefit the field of rehabilitation exoskeletons. Three broadly used HMIs in bionic arms are here investigated, including surface electromyography, impedance, and body-powered control. We propose that combinations of these HMIs could push forward upper extremity exoskeleton development. In this context, we provide concrete applicative examples in two selected clinical scenarios, including post-stroke and Duchenne muscular dystrophy individuals. The discussed solutions can open new avenues for the translation of robotic exoskeletons in a large set of clinical settings and enable a class of exoskeleton technologies that could support a broader range of impairment and disease types.

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Section: Discussionmentioning

confidence: 99%

Transferrable Expertise From Bionic Arms to Robotic Exoskeletons: Perspectives for Stroke and Duchenne Muscular Dystrophy

Nizamis

Stienen

Kamper

et al. 2019

IEEE Trans. Med. Robot. Bionics

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“…Electroencephalogram (EEG), and ECog (Electrocortocogram) measures brain signals, and they could be used to supersede EMG for prostheses control. ECog electrodes are invasive as they are placed directly inside the head, whereas EEG electrodes are non-invasive, as they are positioned on the scalp area [100], where information regarding the targeted body movements are measurable [101]. EEG and ECog have currently found an application as a brain-machine interface [102], and in theory, can control the movement of the prosthesis similar to the EMG.…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

“…EEG and ECog have currently found an application as a brain-machine interface [102], and in theory, can control the movement of the prosthesis similar to the EMG. In other words, while EMG measures the electric current from muscles and provides the control signal according to the action intended by subject [103], brain-machine interface decodes the electrical signal generated from the brain and converts them to the control signal for the control of prostheses [104] without using a muscle as an intermediate [100]. Unfortunately, due to the invasiveness (ECog) and the problem associated with electrodes montage stability (EEG), generalised poor signal-to-noise ratio (SNR) and the poor spatial resolution of the signals, not to mention the discomfort related to the need of having multiple devices over the subject’s body (i.e., head and limbs), we believe that, at present, these devices may be better suited for patients with spinal cord injury where voluntary EMG signals may be not available.…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Parajuli

Sreenivasan

Bifulco³

et al. 2019

Sensors

229

128

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Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

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“…In terms of force feedback, in recent years a lot of work has been done on force feedback from upper limb prosthesis using vibrotactile stimulation [31], [32]. Most of the systems have used either one [5], [33], [34] or two [35], [36] vibrotactile elements along with a single force sensor to convey complete grasping force and make or break contact information [37].…”

Section: Introductionmentioning

confidence: 99%

Development and Testing of a Wearable Vibrotactile Haptic Feedback System for Proprioceptive Rehabilitation

et al. 2020

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The human sense of touch is an integral part of daily life. For tasks involving grasping and manipulation of objects, force feedback is a key requirement. Most of the systems give contact point or complete grasping force feedback; for precision grasping and other physical interactions, finger awareness and force feedback from independent fingers is essential. In this study a novel, wearable proprioceptive rehabilitation system is designed which restores the ability of identifying and distinguishing between individual fingers of a prosthetic hand or an exoskeleton in a non-invasive manner. Moreover, it provides different levels of force feedback from every finger as well, which enables the user to distinguish and control force in precision grasping activities. For testing the system accuracy, classical psychophysical methods were used on a group of 14 voluntary disabled subjects. The tests were conducted in both, ideal and real-world conditions i.e. without and with distractions and accuracies were calculated accordingly. A p-test was also conducted to observe significance between the samples of with and without distraction datasets. The system performed with an overall accuracy of 82.04% which was well above the min. performance measure of 60%. Vi-HaB is standalone system and can be mounted on any upper limb rehabilitation (prosthesis, exoskeleton) system for finger awareness and force feedback.

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Analysis of Man-Machine Interfaces in Upper-Limb Prosthesis: A Review

Cited by 47 publications

References 44 publications

Transferrable Expertise From Bionic Arms to Robotic Exoskeletons: Perspectives for Stroke and Duchenne Muscular Dystrophy

Transferrable Expertise From Bionic Arms to Robotic Exoskeletons: Perspectives for Stroke and Duchenne Muscular Dystrophy

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Development and Testing of a Wearable Vibrotactile Haptic Feedback System for Proprioceptive Rehabilitation

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