Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements

Al-Quraishi, Maged S.; Aris, Ishak; Ahmad, Siti Anom; Hasan, Mohammad Khatim

doi:10.1109/ascc.2015.7244844

Cited by 15 publications

(10 citation statements)

References 18 publications

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“…From earlier sources, it was known that ankle joint movements can be decoded from the recorded sEMG data of healthy subjects [ 23 ]. Furthermore, it was reported that the intent of performing lower limb movement (as decoded from sEMG signals) can be used to control a rehabilitative device [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…Six surface EMG electrodes (Ambu Neuroline 720 surface electrodes, REF 72000-S/25, Baltropbakken 13, DK-2750, Ballerup, Denmark) were placed on the paretic leg on Tibialis anterior, gastrocnemius, and peroneus longus [ 23 ]. Two electrodes were placed in bipolar configuration 2 cm apart on the belly of each muscle to acquire signal from a given muscle.…”

Section: Methodsmentioning

confidence: 99%

“…The available literature on the decoding of ankle joint motions from the movement intention of a user using surface electromyography (sEMG) in healthy subjects as well as in stroke patients is limited. However, Al-Quraishi et al [ 23 ] successfully decoded ankle joint movements in healthy subjects while investigating the impact of different feature extraction and dimensionality reduction techniques on classification accuracies using autoregressive (AR) features and the following classifiers: K-nearest neighbor (k-NN), multilayer perceptron (MLP), and linear discriminant analysis (LDA). Their findings suggested that k-NN along with fuzzy neighborhood preserving analysis with QR (FNPA-QR) decomposition, as a dimensionality reduction technique, provides superior results with an average accuracy of 96.20% ± 4.1%.…”

Section: Introductionmentioning

confidence: 99%

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Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

Noor

Waris

Gilani

et al. 2021

Sensors

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Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

Noor

Waris

Gilani

et al. 2021

Sensors

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“…For electroencephalography (EEG) signals involved in BCI design, the causal relationship between a user's intent and his EEG pattern is not intuitive at first glance. Specific features are extracted from the raw signals before processing and classification, to confer a reliable classification scheme [40]. Depending on the chosen criteria, analyzing s EMG signals can be a lot easier and more intuitive.…”

Section: Methodology and Control Algorithmmentioning

confidence: 99%

Wireless sEMG-Based Body–Machine Interface for Assistive Technology Devices

Fall

Gagnon-Turcotte

Dube

et al. 2017

IEEE J. Biomed. Health Inform.

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Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.

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“…utilizan videocámaras o cámaras de profundidad demuestran resultados confiables, estos presentan limitaciones causadas por oclusiones parciales del objetivo [15,16].…”

Section: Energía De Rotación Promediounclassified

Análisis de reducción de dimensiones para el reconocimiento de actividades físicas humanas usando fusión multimodal

Almanza

Salcedo

Baena

2021

Ingeniare. Rev. chil. ing.

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El reconocimiento automático de actividades físicas humanas es una tarea importante en aplicaciones de visión por computador. Los enfoques robustos que utilizan unos o varios sensores generalmente vinculan características redundantes que consumen recursos computaciones y tiempo de cómputo durante el proceso de clasificación de subactividades. En este artículo se explora la reducción de dimensión para el reconocimiento de actividades y movimientos primitivos humanos fusionando datos provenientes de sensores de profundidad visual Kinect, sensores inerciales IMUs y electrodos de registro electromiográfico (EMGs). También se enseña un estudio comparativo donde se evalúa diferentes técnicas de reducción de características del estado arte, se analiza el comportamiento en base al desempeño en el reconocimiento de actividades física y el tiempo de cómputo de los métodos bajo estudio. Los resultados muestran que, metodologías del estado de arte pueden tener un menor costo temporal a la hora de su implementación sin afectar considerablemente el desempeño al reconocer la actividad.Palabra clave: Actividades físicas, movimientos primitivos, reducción de dimensiones, aprendizaje de máquina.

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Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements

Cited by 15 publications

References 18 publications

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

Wireless sEMG-Based Body–Machine Interface for Assistive Technology Devices

Análisis de reducción de dimensiones para el reconocimiento de actividades físicas humanas usando fusión multimodal

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