Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation

Wang, Wendong; Li, Hanhao; Xiao, Manyu; Chao, Yang; Yuan, Xiao; Ming, Xing; Zhang, Fangfang

doi:10.1016/j.medengphy.2020.01.016

Cited by 34 publications

(24 citation statements)

References 21 publications

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“…The same robotic structure was used in three different documents [39,43,47]. The system reported by these investigations presents an adequate level of portability and a comparatively light weight (1.6 kg) for use mainly at the elbow joint.…”

Section: Portabilitymentioning

confidence: 99%

“…Some of them proposed different methodologies to detect the movement intentions and to replicate them using a leader-follower architecture. Although hard exoskeletons have been traditionally accepted [39,43,47,54], several alternatives have proposed improvements in terms of weight or functionalities [30,35,44].…”

Section: Exoskeleton Typementioning

confidence: 99%

“…Despite the all-metal structures, in [24,49] the proposal of new cable-operated joints was highlighted, contributing to making the exoskeleton light, compact, and portable. Finally, as described in the portability section, the same structure reported in three papers [39,43,47], was composed of metals and a minimum number of other elements. In order to reduce the overall weight and ensure the required strength, the main structure was composed of 5052 aluminium alloy.…”

Section: Structural Materialsmentioning

confidence: 99%

“…Various developments [24][25][26]31,42] do not specifically refer to the implemented operation modes; however, it was deduced that an active control was implemented. In other works [39,40,43,45,47,51], the robotic exoskeleton has provided complete rehabilitation assistance through active control.…”

mentioning

confidence: 99%

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Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Vélez-Guerrero

Callejas-Cuervo

Mazzoleni

2021

Sensors

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Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

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Section: Portabilitymentioning

confidence: 99%

Section: Exoskeleton Typementioning

confidence: 99%

Section: Structural Materialsmentioning

confidence: 99%

mentioning

confidence: 99%

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Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Vélez-Guerrero

Callejas-Cuervo

Mazzoleni

2021

Sensors

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“…Training that is “active” rather than “passive” (i.e., that centers on the patients' intended motions throughout a session rather than forcing them into a set regimen that does not individually vary) can significantly enhance the effects of training and improve patients' rehabilitation experiences [ 9 – 11 ]. Currently, existing man-machine interactive interfaces for body motion intent recognition function are based on three modes: mechanical sensor signals [ 8 ], surface myoelectric (sEMG) signals [ 12 ], and biological EEG signals [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

Guo

Yang

et al. 2020

Computational Intelligence and Neuroscience

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Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

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Towards a Gait Planning Training Strategy Using Lokomat

de Albuquerque,

da Costa,

da Silva

et al. 2024

Lecture Notes in Networks and Systems

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Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation

Cited by 34 publications

References 21 publications

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

Towards a Gait Planning Training Strategy Using Lokomat

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