A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration

Bi, Luzheng; Feleke, Aberham Genetu; Guan, Cuntai

doi:10.1016/j.bspc.2019.02.011

Cited by 275 publications

(220 citation statements)

References 131 publications

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“…The importance of the EMG comes from its ability to estimate the required torque for each specific movement [76]. An electromyograph (EMG) signal extracted from the muscles fibers is used to estimate the required torque for each specific movement [77]. Using an EMG signal exhibits many advantages.…”

Section: Resultsmentioning

confidence: 99%

A Review on Upper Limb Rehabilitation Robots

2020

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Rehabilitation is the process of treating post-stroke consequences. Impaired limbs are considered the common outcomes of stroke, which require a professional therapist to rehabilitate the impaired limbs and restore fully or partially its function. Due to the shortage in the number of therapists and other considerations, researchers have been working on developing robots that have the ability to perform the rehabilitation process. During the last two decades, different robots were invented to help in rehabilitation procedures. This paper explains the types of rehabilitation treatments and robot classifications. In addition, a few examples of well-known rehabilitation robots will be explained in terms of their efficiency and controlling mechanisms.

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Section: Resultsmentioning

confidence: 99%

A Review on Upper Limb Rehabilitation Robots

2020

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“…Many types of non-verbal user interfaces have been developed for controlling assistive robot arms that rely on a variety of input signals, such as electrocorticographic (ECoG) (Hochberg et al, 2012 ), gestures (Rogalla et al, 2002 ), electromyography (EMG) (Bi et al, 2019 ), and electroencephalography (EEG) (Bi et al, 2013 ; Salazar-Gomez et al, 2017 ). Although ECoG has been mapped to continuous, high-DOF hand and arm motion (Chao et al, 2010 ; Wang et al, 2013 ), a disadvantage is that an invasive surgical procedure is required.…”

Section: Related Workmentioning

confidence: 99%

Toward Shared Autonomy Control Schemes for Human-Robot Systems: Action Primitive Recognition Using Eye Gaze Features

2020

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The functional independence of individuals with upper limb impairment could be enhanced by teleoperated robots that can assist with activities of daily living. However, robot control is not always intuitive for the operator. In this work, eye gaze was leveraged as a natural way to infer human intent and advance action recognition for shared autonomy control schemes. We introduced a classifier structure for recognizing low-level action primitives that incorporates novel three-dimensional gaze-related features. We defined an action primitive as a triplet comprised of a verb, target object, and hand object. A recurrent neural network was trained to recognize a verb and target object, and was tested on three different activities. For a representative activity (making a powdered drink), the average recognition accuracy was 77% for the verb and 83% for the target object. Using a non-specific approach to classifying and indexing objects in the workspace, we observed a modest level of generalizability of the action primitive classifier across activities, including those for which the classifier was not trained. The novel input features of gaze object angle and its rate of change were especially useful for accurately recognizing action primitives and reducing the observational latency of the classifier.

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“…The generation of EMG also precedes movement. There are two acquisition approaches [19]. One is to insert needle electrodes into muscle for detection.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Human Intention Recognition of Multi-Joints Based on MYO

Sun

et al. 2020

IEEE Access

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Hill musculoskeletal model (HMM) is commonly used to estimate human motion intentions. HMM utilizes electromyography (EMG) signals as the nonlinear model input to obtain muscle forces or torques. However, due to the fact that it contains many physiological parameters that are difficult to measure, HMM is generally applied in simple continuous intention estimation of a single joint. In this work, we aimed at recogonizing shoulder and elbow joints angles and their angular vecocities continuously in real time. Firstly, we used MYO armband as the EMG sensor. Then, a reasonable prediction model was deduced based on HMM and human dynamics to realize online continuous recognition of the four angles and angular velocities of shoulder and elbow joints. Nonlinear autoregressive with external input neural network (NARX) replaced the prediction equation. In addition, the framework of state space model was completed by constructing an observation equation. Thus, the closed-loop characteristic was realized to eliminate the influence of cumulative error and ensure good estimation performance. Experimental results verified the feasibility and accuracy of the algorithm. For predefined trajectory and random trajectory seperately, the RMSE were 0.955 and 1.15 (degree) for angles estimation and 2.8, 3.40 for angular velocities (degree/s). Compared with the normally used back-propagation neural network (BPNN), the method proposed in this paper obviously got more accurate and smooth results.

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A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration

Cited by 275 publications

References 131 publications

A Review on Upper Limb Rehabilitation Robots

A Review on Upper Limb Rehabilitation Robots

Toward Shared Autonomy Control Schemes for Human-Robot Systems: Action Primitive Recognition Using Eye Gaze Features

Real-Time Human Intention Recognition of Multi-Joints Based on MYO

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