EMG-Driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation

Sierotowicz, Marek; Lotti, Nicola; Nell, Laura; Missiroli, Francesco; Alicea, Ryan; Zhang, Xiao-Hui; Xiloyannis, Michele; Rupp, R.; Papp, E.; Krzywinski, Jens; Castellini, Claudio; Masia, Lorenzo

doi:10.1109/lra.2021.3140055

Cited by 39 publications

(21 citation statements)

References 34 publications

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“…(a) Structure of Hand of Hope 81 ; (b) an attention-controlled hand exoskeleton 51 ; (c) close-up view of the EMG-driven machine learning control of a soft glove 30 ; and (d) close-up view of the probabilistic model-based learning control of a soft pneumatic. 52 …”

Section: Classification Of Intelligent Control Strategiesmentioning

confidence: 99%

“…Sierotowicz et al 30 of the Institute for Robotics and Mechatronics at the German Aerospace Center (DLR) designed an EMG-driven, machine-learning controlled soft glove for grip assistance and rehabilitation. Soekadar et al 31 published three papers, in 2014, 2015, and 2016, describing a novel brain/neuro-computer interaction (BNCI) system for controlling a hand exoskeleton robot that integrates electroencephalography (EEG) and electrooculography (EOG).…”

Section: Introductionmentioning

confidence: 99%

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A review on the application of intelligent control strategies for post-stroke hand rehabilitation machines

Liu

Guo

et al. 2023

Advances in Mechanical Engineering

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Among the approaches to functional hand rehabilitation after stroke, the use of hand rehabilitation robots can provide functional training of the hand or assist the paralyzed hand in activities of daily living, and in particular, intelligent control strategies play a very important role in rehabilitation robotics. The purpose of this review is to summarize the current status of commercially available intelligently controlled hand rehabilitation robots developed in recent years. Firstly, we summarize the theoretical basis of post-stroke hand rehabilitation in the reviewed literature, and the intelligent control strategies of hand rehabilitation robots. Then, representative intelligently controlled hand rehabilitation robots are listed. Finally, we discuss the future directions of the intelligently controlled hand rehabilitation robots. The results show that the development trend in recent years is more inclined to: combining the theoretical basis of stroke hand rehabilitation and studying intelligent control methods of hand function rehabilitation robots that combine human bioelectrical signals to achieve prediction of patients’ motor intentions and also induced stimulation of neuroplasticity in the brain. As well as fusing different bioelectrical signals as a way to improve the applicability of the assistive device in real life. This paper will help researchers to understand the current state of the art regarding intelligently controlled hand rehabilitation robotics after stroke in recent years.

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Section: Classification Of Intelligent Control Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on the application of intelligent control strategies for post-stroke hand rehabilitation machines

Liu

Guo

et al. 2023

Advances in Mechanical Engineering

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“…State-of-the-art EMG-based approaches mainly employ regression algorithms that map EMG signals to the motion intention through numerical functions [7]. For example, Sierotowicz et al presented a soft glove with a control scheme to estimate motion intention based on the ridge regression algorithm [8]. Wu et al used a Gaussian radial basis function network to estimate the motion intention for an adaptive controller in the upper limb rehabilitation robot [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cooperative Control Strategy for a Wrist Exoskeleton Using Model-Based Joint Impedance Estimation

Zhao

Qian

Sheng

et al. 2023

IEEE/ASME Trans. Mechatron.

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Wrist rehabilitation exoskeletons have gained much attention over the last decades, striving to restore motor functions for patients with neuromuscular disorders. Electromyography (EMG) signal has been employed to estimate the motion intention to achieve interactive training schemes. However, it is a challenging task to estimate the joint impedance in realtime, as it is a crucial parameter for control of exoskeletons. This paper proposes an adaptive cooperative control strategy for a wrist exoskeleton based on a real-time joint impedance estimation approach. By explicitly interpreting the underlying transformation in the muscular and skeletal systems, the proposed approach estimates the motion intention and the joint impedance of a human subject simultaneously without additional calibration procedures and regulates the training trajectories and assistance accordingly. Results indicate the proposed method outperforms other training protocols, including the trajectory tracking control and the fixed cooperative control. The proposed control strategy provides an additional 66.25% motion deviation when estimated joint torque increases 12.36%, which enhances the training effectiveness and the interaction safety and promotes subjects' active engagement.

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“… Blana et al (2020) applied a biomechanical model to prosthesis control, and the average correlation between the model and real movement reached 0.89. Sierotowicz et al (2022) proposed a soft glove that can detect the user’s motion intention by EMG to help patients with grip assistance and rehabilitation training. Xie et al (2021) also made an exoskeleton for hand movement to help stroke patients reach and grasp.…”

Section: Introductionmentioning

confidence: 99%

Natural grasping movement recognition and force estimation using electromyography

Zhang²,

Yang³

et al. 2022

Front. Neurosci.

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Electromyography (EMG) generated by human hand movements is usually used to decode different action types with high accuracy. However, the classifications of the gestures rarely consider the impact of force, and the estimation of the grasp force when performing natural grasping movements is so far overlooked. Decoding natural grasping movements and estimating the force generated by the associated movements can help patients to improve the accuracy of prosthesis control. This study mainly focused on two aspects: the classification of four natural grasping movements and the force estimation of these actions. For this purpose, we designed an experimental platform where subjects could perform four common natural grasping movements in daily life, including pinch, palmar, twist, and plug grasp, to complete target profiles. On the one hand, the results showed that, for natural grasping movements with different levels of force (three levels at 20, 50, and 80%), the average accuracy could reach from 91.43 to 97.33% under five classification schemes. On the other hand, the feasibility of force estimation for natural grasping movements was demonstrated. Furthermore, in the process of force estimation, we confirmed that the regression performance about plug grasp was the best, and the average R2 could reach 0.9082. Besides, we found that the regression results were affected by the speed of force application. These findings contribute to the natural control of myoelectric prosthesis and the EMG-based rehabilitation training system, improving the user’s experience and acceptance.

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EMG-Driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation

Cited by 39 publications

References 34 publications

A review on the application of intelligent control strategies for post-stroke hand rehabilitation machines

A review on the application of intelligent control strategies for post-stroke hand rehabilitation machines

Adaptive Cooperative Control Strategy for a Wrist Exoskeleton Using Model-Based Joint Impedance Estimation

Natural grasping movement recognition and force estimation using electromyography

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