Surface EMG Classification of Basic Hand Movement

Omama, Youssef; Haddad, Christelle; Machaalany, Maroun; Hamoudi, Ahmad; Hajj-Hassan, Mohamad; Ali, Mohamad Abou; Hamawy, Lara

doi:10.1109/icabme47164.2019.8940352

Cited by 10 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Third, it was reported that the loose attachment between the sensor and the muscle could dramatically affect the acquired signal because of any sudden movement of the loosened sensor [46]. Therefore, a tie was applied to prevent the sensor from being loosely attached to the muscle.…”

Section: Resultsmentioning

confidence: 99%

“…This chosen sensor is characterized by its three bio-potential pins, two of which are differential pins and one a reference pin. The three pins were connected to Covidien disposable electrodes (H124SG) for better acquisition of the sEMG signals [46]. As shown in Figure 1, the two differential electrodes of the sensor were attached in a straight line over the belly of the middle deltoid [47,48].…”

Section: Acquisition Of the Semg And Hardware Setupmentioning

confidence: 99%

See 1 more Smart Citation

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier

Qassim

Hasan

Ramli

et al. 2022

Sensors

View full text Add to dashboard Cite

The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Acquisition Of the Semg And Hardware Setupmentioning

confidence: 99%

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier

Qassim

Hasan

Ramli

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The wheelchair control for paraplegic patients is demonstrated by Dandamudi et al [17]. The nger and hand movement detection is carried out by using Myoware muscle sensors [18,19].…”

Section: Introductionmentioning

confidence: 99%

A Wearable Low-cost Muscle Activated Switch for Wireless Control for the Physically Challenged Patients

Iqbal

2021

Preprint

View full text Add to dashboard Cite

BackgroundTo control the environmental conditions of the living place and to use the electrical appliances and computers etc. are one of the major challenges for the physically disabled persons and elderly people. The surface electro myography (sEMG) is employed to detect the muscle movement and used for games therapy and various others applications. This paper presents a low-cost wearable muscle switch designed to acquire sEMG signal and process it to provides the control, gaming therapy and mode of communication to the physically disabled patients.ResultsThis paper proposes a wearable low-cost muscle activated switch to assist the physically challenged members of the society. The designed muscle activated switch consists of Myoware muscle sensors that is used for data acquisition and to detect the muscle activity from the target muscle using Electromyography (EMG) signals. The Arduino board provides the human-computer interface and control to the devices based on the muscle signal. The Bluesmirf bluetooth is used for the wireless connectivity to our designed system that assists the physically disabled persons to use the computer and to control the home appliances via commercially available Grid 3 software and wifi switches. The novelty of this muscle sensor switch is that it can be connected to any bluetooth compatible devices that can be controlled from any targeted muscle. The system was tested with Grid 3 software on laptop for environmental control and text to speech conversion.ConclusionsA low cost muscle sensor based Bluetooth switch system for the physically disabled patients has been presented in this paper. The designed switch can be connected to any Bluetooth enabled device as demonstrated in this paper. The physically disable persons can select any module from the Grid 3 software such as the environmental control (to control the environment via electrical appliances), text to speech converter (for Aphasia patients), gaming therapy etc. The designed system can also be utilized to use the Bluetooth enabled electric gadgets such as Mobile Phones, Ipad and android Tablets etc. in accessibility mode.

show abstract

“…Este sensor genera a su salida una magnitud de voltaje, el cual puede ser captado por un conversor análogo digital (CAD) para su posterior almacenamiento o procesamiento. Esta tarjeta ha sido utilizada en algunos proyectos relacionados para la caracterización de músculos [118][119], desarrollo de sistemas biónicos [120][121][122], entre otros. a b Figura 28. a. Distribución de pines sensor Mioware y b. Sensor de electromiografía Myoware Fuente: [117] Dentro de las principales características del sensor están:…”

Section: Instrumentación Señal De Electromiografíaunclassified

Diseño e implementación de dos dispositivos biomédicos para pacientes con Síndrome de Apnea Obstructiva del Sueño (SAOS) basados en la detección de apneas e hipopneas y rehabilitación por medio de estimulación eléctrica

Moscoso¹

View full text Add to dashboard Cite

Surface EMG Classification of Basic Hand Movement

Cited by 10 publications

References 3 publications

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier

A Wearable Low-cost Muscle Activated Switch for Wireless Control for the Physically Challenged Patients

Diseño e implementación de dos dispositivos biomédicos para pacientes con Síndrome de Apnea Obstructiva del Sueño (SAOS) basados en la detección de apneas e hipopneas y rehabilitación por medio de estimulación eléctrica

Contact Info

Product

Resources

About