Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

Rodríguez-Tapia, Bernabé; Soto, Israel; Marinez, Daniela M.; Arballo, Norma Candolfi

doi:10.1109/access.2019.2963881

Cited by 75 publications

(45 citation statements)

References 67 publications

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“…These electrical signals cause muscles to contract and relax, thus producing the required articulatory movements and gestures. EMG measures the electrical potentials generated by depolarisation of the external membrane of the muscle fibres in response to the stimulation of the muscles by the motor neurons [212]. The EMG signal resulting from the application of this technique is complex and dependent on the anatomical and physiological properties of the muscles [213].…”

Section: B Muscle Activitymentioning

confidence: 99%

“…Besides its application in SSIs (see next section), EMG is being used in clinical rehabilitation (e.g., for the recovery of facial muscular activity in patients with motor speech disorders [220] and other articulatory disturbances [221]), assistance and as an input device [212]. In particular, these previous studies have reported the benefits of EMG biofeedback in therapy aimed at increasing muscle activity of the oral articulators in dysarthric speakers with neurological conditions [220], [222], [223].…”

Section: ) Emg and Speech Productionmentioning

confidence: 99%

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Silent Speech Interfaces for Speech Restoration: A Review

et al. 2020

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This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.

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Section: B Muscle Activitymentioning

confidence: 99%

Section: ) Emg and Speech Productionmentioning

confidence: 99%

Silent Speech Interfaces for Speech Restoration: A Review

et al. 2020

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“…As for the muscle activity, the RMS electromyography (EMG) of Tibialis Anterior (TA) and Gastrocnemius (G) muscles were collected using a Biopac instrument and Acqknowledge software. Firstly, the raw EMG data were gathered with a sampling frequency of 1000 Hz [37]- [40]. Then, the raw EMG is filtered using a low pass filtered (cut-off frequency of 6 Hz) and rectified before the RMS EMG is calculated [8].…”

Section: A Data Collectionmentioning

confidence: 99%

Improving Passive Ankle Foot Orthosis System Using Estimated Ankle Velocity Reference

Adiputra¹,

Rahman

Ubaidillah

et al. 2020

IEEE Access

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This study aims to investigate an appropriate ankle velocity reference (ωref), which is the average ankle velocity of a certain healthy subject when walking with a certain walking speed. The goal is to improve a Passive Controllable Ankle Foot Orthosis (PICAFO) by implementing the ankle velocity reference. Firstly, the function to estimate ωref, based on walking speed and body mass index (BMI), is obtained from 16 able-bodied subjects walking gait data. The effect of controlled stiffness (based on ωref) to the user's ankle kinematics and muscle activity was evaluated by comparing it to other settings, such as walking barefooted and various constant damping stiffness (0%, 30%, 60%, and 100% of the maximum damping stiffness). Two able-bodied subjects (normal and overweight) participated in the evaluation, where they had to walk at two different walking speeds (1 and 2 km/h). The result showed that ankle kinematics and muscle activity were improved when ω was controlled during walking speed of 1 km/h for both subjects. In terms of ankle kinematics, the toe clearance occurred, and walking stability increased. In terms of muscle activity, the average muscle activity had reduced by ~29% for the normal subject and by ~57% for an overweight subject, which shows that PICAFO provides necessary damping stiffness to replace the muscle works partially. In the future, by using the ωref based on walking speed and BMI, the therapists can skip the trial and error process of finding an appropriate PICAFO prescription for a post-stroke patient.

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“…Additionally, three main types of noise sources contribute to the process of sEMG signal acquisition, those are the inherent noise of electronic instrument, the ambient noise from the electromagnetic radiation in the environment, and motion artifact. These noise lies mostly in the frequency range from 0-20 Hz [17], [18]. Meanwhile, due to the quasi-random nature of the firing rate of the muscular motor units, the frequency components between 0 and 20 Hz are mostly unstable contains unstable components [17].…”

Section: B Data Pre-processing 1) Band-pass Filtermentioning

confidence: 99%

“…These noise lies mostly in the frequency range from 0-20 Hz [17], [18]. Meanwhile, due to the quasi-random nature of the firing rate of the muscular motor units, the frequency components between 0 and 20 Hz are mostly unstable contains unstable components [17]. Therefore, a 20-500 Hz band-pass filter was used to eliminate the interfering signals and the artifacts.…”

Section: B Data Pre-processing 1) Band-pass Filtermentioning

confidence: 99%

A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

Lin

Ruan

2020

IEEE Access

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Recently, surface electromyography (sEMG) has been used to detect running-related works. sEMG provides a non-invasive and real-time method that allows quantification of muscle energy. However, noises in sEMG signals are a serious issue to be considered as these will interrupt the analysis of muscular activity. Hence, this work aims at distinguishing between sEMG valid signals and noises during running exercise by taking advantage of the combination of 3D-CNN and LSTM, which we called 3D-LCNN. Furthermore, according to the possible cases that happen in the sEMG data-collection procedure, we proposed two data-augmentation approaches to expend our sEMG dataset, which are the simulation of the surface electrodes displacement on the skin and the muscle fatigue. Experiment results show that the classification accuracy of the proposed 3D-LCNN can achieve 90.52%. Additionally, this work provides excellent serviceoriented architecture (SOA). The recognition process can be done after the subject placed the sEMG sensors and performed a trial. Therefore, the process can help clinicians or therapists to distinguish between sEMG valid signals and noises more efficiently.

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Myoelectric Interfaces and Related Applications: Current State of EMG Signal Processing–A Systematic Review

Cited by 75 publications

References 67 publications

Silent Speech Interfaces for Speech Restoration: A Review

Silent Speech Interfaces for Speech Restoration: A Review

Improving Passive Ankle Foot Orthosis System Using Estimated Ankle Velocity Reference

A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

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