Time Derivative Moments Based Feature Extraction Approach for Recognition of Upper Limb Motions Using EMG

Pancholi, Sidharth; Joshi, Amit M.

doi:10.1109/lsens.2019.2906386

Cited by 31 publications

(18 citation statements)

References 8 publications

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“…EMG features set collected from several intact-limbed and amputees are accepted on multiple sparse and high-density (HD) for executing multiple degrees of freedom (hand and finger movements). Time-derivative moments (TDM) based feature extraction [44] is a novel feature set extraction proposed to enhance the performance of EMG-PR in upper limb motion classification. Furthermore, most of the previous studies had focused on time-domain features to reduce computational difficulty.…”

Section: Pattern Recognition-based Myoelectric Controlmentioning

confidence: 99%

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Parajuli

Sreenivasan

Bifulco³

et al. 2019

Sensors

240

128

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Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

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Section: Pattern Recognition-based Myoelectric Controlmentioning

confidence: 99%

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Parajuli

Sreenivasan

Bifulco³

et al. 2019

Sensors

240

128

View full text Add to dashboard Cite

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“…In the consultation [5], in recognition of Spanish language syllables provides an average classification rate of almost 70% for 30 classes. In the consultation [6] in the classification of the movement of the upper limb through various gestures, 97.6% effectiveness is obtained in the classification of 8 classes. In the consultation [7] in recognition of the letters of the American sign language alphabet, an 80% effectiveness was obtained.…”

Section: Discussionmentioning

confidence: 99%

“…In [6] a TDM (Time derivative moments) feature set extraction is proposed to improve the performance of EMG-PR (Electromyography Pattern Recognition) in the classification of upper limb movement. They use a standard database to examine the proposed feature set.…”

Section: Introductionmentioning

confidence: 99%

Acquisition and Classification System of EMG Signals for Interpreting the Alphabet of the Sign Language

Alvarado-Diaz¹,

Brian²,

Flores-Medina³

et al. 2019

IJACSA

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Taking into account that in Peru, there is an increase in people with difficulties in speaking or communicating. According to the National Institute of Statistics and Informatics of Peru (INEI for its acronym in Spanish), around 80000 people use the gesturing language. For this reason, this research proposes to use the electromyography (EMG) signals to detect the hand movement and identify the alphabet of the sign language to provide essential communication to people who need it. The idea is to classify the signals and recognize the letters of the Spanish alphabet, interpreted in the Peruvian sign language. The results show the classification of the 27 letters of the alphabet with a general success rate of 93.9%.

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“…In this study, a hybrid deep neural network is proposed which is a cascade of CNN followed by an LSTM network wherein, preceding CNN layers work as an automatic feature extractor for the latter LSTM network, which then classifies the Sz and Normal subjects. Figure 4 shows an overview of the implemented Sz detector with the help of EEG using machine learning and deep learning algorithm [47]- [50].…”

Section: Deep Learning Approach To Eegmentioning

confidence: 99%

Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

Sharma¹,

Joshi²

2021

Preprint

Self Cite

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In the field of neuroscience, Brain activity analysis is always considered as an important area. Schizophrenia(Sz) is a brain disorder that severely affects the thinking, behavior, and feelings of people all around the world. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection. EEG is a non-linear time-series signal and utilizing it for investigation is rather crucial due to its non-linear structure. This paper aims to improve the performance of EEG based Sz detection using a deep learning approach. A novel hybrid deep learning model known as SzHNN (Schizophrenia Hybrid Neural Network), a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) has been proposed. CNN network is used for local feature extraction and LSTM has been utilized for classification. The proposed model has been compared with CNN only, LSTM only, and machine learning based models. All the models have been evaluated on two different datasets wherein Dataset 1 consists of 19 subjects and Dataset 2 consists of 16 subjects. Several experiments have been conducted for the same using various parametric settings on different frequency bands and using different sets of electrodes on the scalp. Based on all the experiments, it is evident that the proposed hybrid model (SzHNN) provides the highest classification accuracy of 99.9% in comparison to other existing models. The proposed model overcomes the influence of different frequency bands and even showed a much better accuracy of 91% with only 5 electrodes. The proposed model is also evaluated in Internet of Medical Things (IoMT) framework for smart healthcare and remote monitoring applications.

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Time Derivative Moments Based Feature Extraction Approach for Recognition of Upper Limb Motions Using EMG

Cited by 31 publications

References 8 publications

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Acquisition and Classification System of EMG Signals for Interpreting the Alphabet of the Sign Language

Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

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