Implementation issues of EMG-based motion intention detection for exoskeletal robots

Kyeong, Seulki; Kim, Won Dong; Feng, Jirou; Kim, Jung

doi:10.1109/roman.2018.8525649

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The commonly used feature can be mainly divided into time domain feature, frequency domain feature, and timefrequency domain feature. For the time domain feature, mean absolute value (MAV) [27][28][29][30][31][32], root mean square (RMS) [29,31], variance (VAR) [29,31], standard deviation (SD) [29], zero count (ZC) [27,29,32], waveform length (WL) [27,29,32], slope sign change (SSC) [29,32], integrated EMG (IEMG) [33], and difference of mean absolute value (DMAV) [27] are commonly utilized. Although the calculation of time domain feature is simple, it is not enough to describe the information of signals.…”

Section: Machine Learning Based Discrete-motion Classificationmentioning

confidence: 99%

sEMG Based Human Motion Intention Recognition

Zhang

Liu

Han

et al. 2019

Journal of Robotics

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Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

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Section: Machine Learning Based Discrete-motion Classificationmentioning

confidence: 99%

sEMG Based Human Motion Intention Recognition

Zhang

Liu

Han

et al. 2019

Journal of Robotics

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“…Researchers have employed MuMIs for decoding hand gestures to control robotic hands or interaction with computer application [40][41][42][43], decoding continuous arm-hand motions [44][45][46], rehabilitation after strokes [47,48], and for games and entertainment [49]. Such MuMIs have also been employed in decoding walking patterns for an effective control of lower limb prosthesis [50,51], quantifying user fatigue during various tasks [52], and for decoding user intentions during collaborative tasks with robots [53,54]. Several researchers have also focused on employing electroencephalography signals to decode user intentions, hand and finger motions, decoding walking intentions etc.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

Dwivedi

Groll

Beckerle

2022

Sensors

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Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle–machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user’s intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.

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“…[ 10 , 11 , 12 ]. For the gait section prediction, many studies are being conducted to identify gait intervals, toe-off points, and heel strike points using surface electromyography (sEMG) sensors, foot sensors using force sensing resistors (FSRs), and encoders [ 13 , 14 , 15 , 16 , 17 ], etc. ; however, little research is being conducted on continuous gait pattern estimation.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network

Lee

Kim

Lee

2021

Sensors

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A lower-limb exoskeleton robot identifies the wearer′s walking intention and assists the walking movement through mechanical force; thus, it is important to be able to identify the wearer′s movement in real-time. Measurement of the angle of the knee and ankle can be difficult in the case of patients who cannot move the lower-limb joint properly. Therefore, in this study, the knee angle as well as the angles of the talocrural and subtalar joints of the ankle were estimated during walking by applying the neural network to two inertial measurement unit (IMU) sensors attached to the thigh and shank. First, for angle estimation, the gyroscope and accelerometer data of the IMU sensor were obtained while walking at a treadmill speed of 1 to 2.5 km/h while wearing an exoskeleton robot. The weights according to each walking speed were calculated using a neural network algorithm programmed in MATLAB software. Second, an appropriate weight was selected according to the walking speed through the IMU data, and the knee angle and the angles of the talocrural and subtalar joints of the ankle were estimated in real-time during walking through a feedforward neural network using the IMU data received in real-time. We confirmed that the angle estimation error was accurately estimated as 1.69° ± 1.43 (mean absolute error (MAE) ± standard deviation (SD)) for the knee joint, 1.29° ± 1.01 for the talocrural joint, and 0.82° ± 0.69 for the subtalar joint. Therefore, the proposed algorithm has potential for gait rehabilitation as it addresses the difficulty of estimating angles of lower extremity patients using torque and EMG sensors.

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Implementation issues of EMG-based motion intention detection for exoskeletal robots

Cited by 5 publications

References 11 publications

sEMG Based Human Motion Intention Recognition

sEMG Based Human Motion Intention Recognition

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network

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