Wearable Supernumerary Robotic Limb System Using a Hybrid Control Approach Based on Motor Imagery and Object Detection

Tang, Zhichuan; Zhang, Lingtao; Ying, Jichen; Wang, Xinyang; Wang, Hang; Cui, Zhixuan

doi:10.1109/tnsre.2022.3172974

Cited by 23 publications

(14 citation statements)

References 45 publications

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“…Different from the public dataset, the in-house dataset deletes the 6 electrode data of “HEOL,” “HEOR,” “A1,” “A2,” “VEOU,” and “VEOL.” 26 EEG channels are retained to reduces the false recording caused by the EOG signal. According to the principle of event related desynchronization (ERD) and event related synchronization (ERS) ( Tang et al, 2022 ), when people perform MI tasks, the cerebral cortex will produce obvious rhythm signals, which are divided into 8–12 Hz μ rhythm signals and 13–30 Hz β rhythm signals. In order to improve the signal-to-noise ratio of EEG signals, 8–30 Hz band-pass filtering is used to process the data and remove the baseline.…”

Section: Resultsmentioning

confidence: 99%

“…BCI is able to translate neural responses into control instructions by decoding brain activity patterns from electroencephalogram (EEG) ( Narayanan and Bertrand, 2019 ) signals. Motor Imagery (MI) ( Tang et al, 2022 ) based BCI paradigm (MI-BCI) is one of the most popular paradigms nowadays. MI is defined as the cognitive process in which a person imagines their muscles or limbs moving without actually moving.…”

Section: Introductionmentioning

confidence: 99%

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Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

Feng

Jiang

et al. 2022

Front. Hum. Neurosci.

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IntroductionElectroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training samples of MI electroencephalogram (MI-EEG) for a single subject and the great individual differences of MI-EEG among different subjects, the generalization and accuracy of the model on the specific MI task may be poor.MethodsTo solve these problems, an adaptive cross-subject transfer learning algorithm is proposed, which is based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) method. First, the common spatial pattern (CSP) is used to extract the spatial features. Then, in order to make the feature distribution more similar among different subjects, the KMM algorithm is used to compute a sample weight matrix for aligning the mean between source and target domains and reducing distribution differences among different subjects. Finally, the sample weight matrix from KMM is used as the initialization weight of TrAdaBoost, and then TrAdaBoost is used to adaptively select source domain samples that are closer to the target task distribution to assist in building a classification model.ResultsIn order to verify the effectiveness and feasibility of the proposed method, the algorithm is applied to BCI Competition IV datasets and in-house datasets. The results show that the average classification accuracy of the proposed method on the public datasets is 89.1%, and the average classification accuracy on the in-house datasets is 80.4%.DiscussionCompared with the existing methods, the proposed method effectively improves the classification accuracy of MI-EEG signals. At the same time, this paper also applies the proposed algorithm to the in-house dataset, the results verify the effectiveness of the algorithm again, and the results of this study have certain clinical guiding significance for brain rehabilitation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

Feng

Jiang

et al. 2022

Front. Hum. Neurosci.

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“…SRLs are controlled using different control methods including demonstration-based, [20,21] modelbased, [6,9,11,[22][23][24][25] manual, [5,26,27] limb mapping, [3,14,[28][29][30] and bioelectrical-based control. [15,18,[31][32][33] When the wearer performs tasks with the assistance of SRL, human movements lead to changes in the position and attitude of the robot base, which change the dynamics of the SRL, thus greatly increasing the difficulty of auxiliary operations. However, most studies on control have focused on the direct manipulation of SRL by capturing the intention of the wearer, and far too little attention has been paid to the effects of human-machine coupling on SRL control.…”

Section: Introductionmentioning

confidence: 99%

Motion‐Compensation Control of Supernumerary Robotic Arms Subject to Human‐Induced Disturbances

Zhang,

Sui,

Jing

et al. 2024

Advanced Intelligent Systems

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Supernumerary robotic limbs (SRLs) are a novel type of wearable robot used as the third limb to assist the wearer to perform operations that are difficult or impossible for human hands. Although SRLs can compensate for and enhance human physiological abilities, the unpredictable disturbances caused by human movements significantly affect the coordinating control between the robot and wearer. In this study, a general modeling of supernumerary robotic arms (SRAs) on an omnidirectional floating base is presented. Using position and orientation feedback from sensors at the base and tip, three control methods based on different sensor feedback are proposed to improve tracking accuracy. Experiments on point and trajectory tracking are conducted on the SRAs. In the results (point and circular trajectory‐tracking errors with the manipulator as floating base: 1.18 ± 0.56 mm [mean ± standard deviation(SD) error] and 1.42 ± 0.43 mm, point trajectory‐tracking errors with the human shoulder as floating base: 1.37 ± 0.58 mm, and the performance in perforation positioning operation experiment), it is demonstrated that the proposed controllers enable the SRAs to achieve high‐precision tracking and good adaptability to different user movements and frequencies. Also, in the results, future studies on dynamic high‐precision manipulation of SRLs are motivated.

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“…Capturing exoskeleton user intention traditionally relies on feedback from sensors, such as force transducers [ 8 , 9 , 10 ], cameras [ 11 ], strain gauges [ 12 ], and lasers [ 13 ], each of which possesses inadequate sensor–machine synergy in dealing with complex gesture/action and leads to low intention-recognition accuracy. Recently, electromyography (EMG) and electroencephalogram (EEG) have been extensively studied for HMI due to their high intention-detection accuracy potential, which benefits from multiple signal channels [ 14 , 15 , 16 ]. However, EMG and EEG usually require huge data manipulation efforts, which lead to a significant delay in real-time control [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

et al. 2022

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Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.

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Wearable Supernumerary Robotic Limb System Using a Hybrid Control Approach Based on Motor Imagery and Object Detection

Cited by 23 publications

References 45 publications

Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning

Motion‐Compensation Control of Supernumerary Robotic Arms Subject to Human‐Induced Disturbances

Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation

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