Estimate human-force from sEMG signals for a lower-limb rehabilitation robot

Khanjani, Irandokht; Khoshdel, Vahab; Akbarzadeh, Alireza

doi:10.1109/iraniancee.2017.7985275

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…Khoshdel introduced a method that applies an ANN to estimate lower-limb strength from sEMG signals and apply it to a knee rehabilitation robot. Both human and robot models were simulated and experimentally validated using OpenSim [62,98]. In [4], a 6-DOF parallel robot was controlled by four channels of sEMG signals from the lower limb and applied to lower-limb rehabilitation.…”

Section: Full-human Continuous Control Strategy (S1)mentioning

confidence: 99%

Review of sEMG for Robot Control: Techniques and Applications

Song,

Yan,

Guo

et al. 2023

Applied Sciences

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Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.

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Section: Full-human Continuous Control Strategy (S1)mentioning

confidence: 99%

Review of sEMG for Robot Control: Techniques and Applications

Song,

Yan,

Guo

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Therefore, estimating the torque of the knee joint muscles by the neural network and various methods is one of the most important steps to improve the control function of human interaction in the knee rehabilitation robot [37]. Using ANN, Khanjani et al were able to estimate lower limb forces using EMG signal [38]. In another study, the authors were able to calculate the amount of knee torque by a support vector regression based on electromagnetic signals [39].…”

Section: Introductionmentioning

confidence: 99%

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

Mokri

Bamdad

Abolghasemi

2022

Med Biol Eng Comput

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The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.

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Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

et al. 2023

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Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects’ thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.

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Estimate human-force from sEMG signals for a lower-limb rehabilitation robot

Cited by 10 publications

References 17 publications

Review of sEMG for Robot Control: Techniques and Applications

Review of sEMG for Robot Control: Techniques and Applications

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

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