Time and frequency domain features of EMG signal during Islamic prayer (Salat)

Ahamed, Nizam Uddin; Sikandar, Tasriva; Rabbi, Mohammad Fazle; Ghazali, Kamarul Hawari

doi:10.1109/cspa.2017.8064939

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The motion data consists of 8 channels of EMG data along with IMU data that contains unit quaternions, Euler angles for pitch, yaw, roll and 3-axis accelerometer, 3-axis gyroscope adding up to a total of 21 input signals for each recording. For each of the input signals, five time-domain features were captured [16][17][18][19][20]. These include: Mean Absolute Value (MAV), Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) [16].…”

Section: ) Motion Feature Extractionmentioning

confidence: 99%

Ensemble of Multimodal Deep Learning Models for Violin Bowing Techniques Classification

Muhammed,

Karunakaran,

Bhat

et al. 2024

JAIT

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Bowing gesture while playing violin refers to the motion of the violinist's arm. Violinists use different types of bow strokes to express musical phrases, played by the movement of the right arm holding the fiddle bow. Although the sound produced by each bow stroke is distinct, it can be difficult for new fiddlers to distinguish and recognize these bowing techniques. So, this paper presents a novel approach of an ensemble of multimodal deep learning models consisting of one Convolution Neural Network (CNN) and two Long Short-Term Memory (LSTM) models to classify into one of the five bowing classes: detaché , legato, martelé , spiccato and staccato. The dataset used consists of audio samples performed by 8 violinists along with the motion of their forearms measured using a Myo sensor device, to acquire 8-channels of Electromyogram (EMG) data and 13-channels of Inertial Measurement Unit (IMU) data. The audio features are extracted from audio excerpts and time domain features are extracted from EMG and IMU motion signals. These features are passed into an ensemble of deep learning models to make the final prediction using weighted voting. The proposed ensemble classifier was able to deliver optimal results with an overall accuracy of 99.5%, which is better than the previous studies that took only either audio or motion data into consideration.

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Section: ) Motion Feature Extractionmentioning

confidence: 99%

Ensemble of Multimodal Deep Learning Models for Violin Bowing Techniques Classification

Muhammed,

Karunakaran,

Bhat

et al. 2024

JAIT

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“…It should be noticed that we restrict the feature extraction procedure in this work to time-domain features, as we are interested in the identification of muscular activity rather than its classification and evaluation. From this perspective, the employment of further EMG features expressed in the frequency domain, e.g., mean frequency, median frequency or power spectral density [50], or mixed time-frequency domain, e.g., spectrogram or signal phase [51], assume a rather computationally complex extraction which is unjustified for our work.…”

Section: Lower-limb Emg Assessmentmentioning

confidence: 99%

A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics

2020

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Walking is the most basic form of human activity for achieving mobility. As an essential function of the human body, the examination of walking is directed towards the assessment of body mechanics in posture and during movement. This work proposes a wearable smart system for the monitoring and objective evaluation of foot biomechanics during gait. The proposed solution assumes the cross-correlation of the plantar pressure with lower-limb muscular activity, throughout the stance phase of walking. Plantar pressure is acquired with an array of resistive pressure sensors deployed onto a shoe insole along the center of gravity progression line. Lower-limb muscular activity is determined from the electromyogram of the tibialis anterior and gastrocnemius lower limb muscles respectively. Under this scenario, physiological gait assumes the interdependency of plantar pressure on the heel area with activation of the tibialis anterior, as well as plantar pressure on the metatarsal arch/toe area with activation of the gastrocnemius. As such, assessment of gait physiology is performed by comparison of a gait map, formulated based on the footprint–lower-limb muscle cross-correlation results, to a reference gait template. A laboratory proof of concept validates the proposed solution in a test scenario which assumes a normal walking and two pathological walking patterns.

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