Hand Movement Recognition Using Cross Spectrum Image Analysis of EMG Signals-A Deep Learning Approach

Roy, Sayanjit Singha; Samanta, Kaniska; Chatterjee, Soumya; Dey, Sayantan; Nandi, Arnab; Bhowmik, Ronjoy; Mondal, Shoumosree

doi:10.1109/ncetstea48365.2020.9119911

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…It is an effective way to convert the one-dimensional time series to a corresponding two-dimensional image representation. Several previous studies have utilized TF images for sEMG based classification tasks [47], [48]. In particular, two different approaches: Short-Time Fourier Transform and Continuous Wavelet Transform are investigated for the task of EMG based airwriting recognition (depicted in Figure 5), which are briefly introduced in the following subsections.…”

Section: Time-frequency Analysis Approachesmentioning

confidence: 99%

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Tripathi¹,

Kumar²,

Prathosh³

et al. 2022

Preprint

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Airwriting Recognition is the task of identifying letters written in free space with finger movement. It is a dynamic gesture recognition with the vocabulary of gestures corresponding to letters in a given language. Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition. Most of the current research in gesture recognition is focused on identifying static gestures. However, dynamic gestures are natural and userfriendly for being used as alternate input methods in Human-Computer Interaction applications. Airwriting recognition using EMG signals recorded from forearm muscles is therefore a viable solution. Since the user does not need to learn any new gestures and a large range of words can be formed by concatenating these letters, it is generalizable to a wider population. There has been limited work in recognition of airwriting using EMG signals and forms the core idea of the current work. The SurfMyoAiR dataset comprising of EMG signals recorded during writing English uppercase alphabets is constructed. Several different time-domain features to construct EMG envelope and two different timefrequency image representations: Short-Time Fourier Transform and Continuous Wavelet Transform were explored to form the input to a deep learning model for airwriting recognition. Several different deep learning architectures were exploited for this task. Additionally, the effect of various parameters such as signal length, window length and interpolation techniques on the recognition performance is comprehensively explored. The best-achieved accuracy was 78.50% and 62.19% in userdependent and independent scenarios respectively by using Short-Time Fourier Transform in conjunction with a 2D Convolutional Neural Network based classifier. Airwriting has great potential as a user-friendly modality to be used as an alternate input method in Human-Computer Interaction applications.

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Section: Time-frequency Analysis Approachesmentioning

confidence: 99%

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Tripathi¹,

Kumar²,

Prathosh³

et al. 2022

Preprint

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“…For instance, Morbidoni et al reported that a deep learning feature-based method was able to classify the gait phase with higher accuracy than a handcrafted features-based approach [ 17 ]. Roy et al also proposed a deep learning-based classification framework that classifies hand motion with high accuracy [ 22 ]. In that study, the authors adopted an approach where features were extracted using deep learning as this leads to better overall classification performance.…”

Section: Introductionmentioning

confidence: 99%

Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only

Kim

Lee

Shin

2021

Sensors

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Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walking environments using sEMG only. Therefore, the purpose of this study is to classify the current walking environment based on the entire sEMG profile gathered from selected muscles in the lower extremities. The muscle activations of selected muscles in the lower extremities were measured in 27 participants while they walked over flat-ground, upstairs, downstairs, uphill, and downhill. An artificial neural network (ANN) was employed to classify these walking environments using the entire sEMG profile recorded for all muscles during the stance phase. The result shows that the ANN was able to classify the current walking environment with high accuracy of 96.3% when using activation from all muscles. When muscle activation from flexor/extensor groups in the knee, ankle, and metatarsophalangeal joints were used individually to classify the environment, the triceps surae muscle activation showed the highest classification accuracy of 88.9%. In conclusion, a current walking environment was classified with high accuracy using an ANN based on only sEMG signals.

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A Deep CNN-Based Approach for 10-Class with Two-Channel EMG Signal Classification

Triwiyanto,

Yulianto,

Rahmawati

et al. 2024

Lecture Notes in Electrical Engineering

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Hand Movement Recognition Using Cross Spectrum Image Analysis of EMG Signals-A Deep Learning Approach

Cited by 8 publications

References 18 publications

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only

A Deep CNN-Based Approach for 10-Class with Two-Channel EMG Signal Classification

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