Homomorphic Deconvolution for MUAP Estimation From Surface EMG Signals

Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio

doi:10.1109/jbhi.2016.2530943

Cited by 27 publications

(22 citation statements)

References 22 publications

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“…7 , 8 , and 9 . This would have been much harder to do on the sEMG signal alone, and acceleration information also helps discriminate between the concentric and eccentric phases, an important but complicated task if the sEMG is to be used to evaluate muscle fatigue [ 27 ]. Of course, acceleration is not of much use in evaluating the isometric contraction (third segment, in purple), which, on the other hand, clearly stands out in the sEMG track relative to the biceps brachii muscle being exerted.…”

Section: Discussionmentioning

confidence: 99%

“…This is due to the fact that it can be obtained using intrinsically noninvasive measurement devices and is relatively easy to acquire. Indeed, this signal being originated from the electrical potentials generated by contracting muscles [ 27 , 28 ] can be collected simply by contacting electrodes to the skin surface. It is worth to note that the relatively low amplitude of the sEMG signal requires a carefully designed, high-input-impedance, low-noise amplifier for processing it before its recording [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes

et al. 2018

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BackgroundThe human activity monitoring technology is one of the most important technologies for ambient assisted living, surveillance-based security, sport and fitness activities, healthcare of elderly people. The activity monitoring is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) and accelerometer signals for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications.ResultsThe proposed system consists of several ultralight wireless sensing nodes that are able to acquire, process and efficiently transmit the motion-related (biological and accelerometer) body signals to one or more base stations through a 2.4 GHz radio link using an ad-hoc communication protocol designed on top of the IEEE 802.15.4 physical layer. A user interface software for viewing, recording, and analysing the data was implemented on a control personal computer that is connected through a USB link to the base stations. To demonstrate the capability of the system of detecting the user’s activity, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. The system was tested on four different exercises performed by three people, the automatic classifier achieved an overall accuracy of 85.7% combining the features extracted from acceleration and sEMG signals.ConclusionsA low cost wireless system for the acquisition of sEMG and accelerometer signals has been presented for healthcare and fitness applications. The system consists of wearable sensing nodes that wirelessly transmit the biological and accelerometer signals to one or more base stations. The signals so acquired will be combined and processed in order to detect, monitor and recognize human activities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes

et al. 2018

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“…Generally, the EMG signal is a result of the comprehensive effect of motor unit action potential of muscle fiber both in time and in space for surface muscle [38], [39]. The sEMG signals can be used in three applications: indicator of the muscle activation, representation of the force based on human muscle, and a descriptor of the fatigue for the muscle [38].…”

Section: B Muscle Activation Descriptormentioning

confidence: 99%

Haptics Electromyography Perception and Learning Enhanced Intelligence for Teleoperated Robot

Yang

Liu

et al. 2019

IEEE Trans. Automat. Sci. Eng.

101

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Due to the lack of transparent and friendly human-robot interaction (HRI) interface, as well as various uncertainties, it is usually a challenge to remotely manipulate a robot to accomplish a complicated task. To improve the teleoperation performance, we propose a new perception mechanism by integrating a novel learning method to operate the robots in the distance. In order to enhance the perception of the teleoperation system, we utilize a surface electromyogram signal to extract the human operator's muscle activation. As a response to the changes in the external environment, as sensed through haptic and visual feedback, a human operator naturally reacts with various muscle activations. By imitating the human behaviors in task execution, not only motion trajectory but also arm stiffness adjusted by muscle activation, it is expected that the robot would be able to carry out the repetitive tasks autonomously or uncertain tasks with improved intelligence. To this end, we develop a robot learning algorithm based on probability statistics under an integrated framework of the hidden semi-Markov model (HSMM) and the Gaussian mixture method. This method is employed to obtain a generative task model based on the robot's trajectory. Then, Gaussian mixture regression based on HSMM is applied to correct the robot trajectory with the reproduced results from the learned task model. The execution procedures consist of a learning phase and a reproduction phase. To guarantee the stability, immersion, and maneuverability of the teleoperation system, a variable gain control method that involves electromyography (EMG) is introduced. Experimental results have demonstrated the effectiveness of the proposed method. Note to Practitioners-This paper is inspired by the limitations of teleoperation to perform a task with unfriendly HRI and lack of intelligence. The human operators need to concentrate

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“…Researchers are looking into the feasibility of using electroencephalography (EEG) and EMG to decode the user's intentions [3]. For example, a data glove that is used to capture hand gestures may be replaced by a forearm band with EMG sensors, making the interaction process more natural since the user does not have to wear a glove.…”

Section: Introductionmentioning

confidence: 99%

A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction

et al. 2019

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This paper presents a literature review on pattern recognition of electromyography (EMG) signals and its applications. The EMG technology is introduced and the most relevant aspects for the design of an EMG-based system are highlighted, including signal acquisition and filtering. EMG-based systems have been used with relative success to control upper-and lower-limb prostheses, electronic devices and machines, and for monitoring human behavior. Nevertheless, the existing systems are still inadequate and are often abandoned by their users, prompting for further research. Besides controlling prostheses, EMG technology is also beneficial for the development of machine learning-based devices that can capture the intention of able-bodied users by detecting their gestures, opening the way for new human-machine interaction (HMI) modalities. This paper also reviews the current feature extraction techniques, including signal processing and data dimensionality reduction. Novel classification methods and approaches for detecting non-trained gestures are discussed. Finally, current applications are reviewed, through the comparison of different EMG systems and discussion of their advantages and drawbacks.INDEX TERMS EMG, human-machine interaction, pattern classification, regression.

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Homomorphic Deconvolution for MUAP Estimation From Surface EMG Signals

Cited by 27 publications

References 22 publications

Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes

Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes

Haptics Electromyography Perception and Learning Enhanced Intelligence for Teleoperated Robot

A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction

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