EMG pattern recognition based on artificial intelligence techniques

Park, Sang-Hui; Lee, Seok-Pil

doi:10.1109/86.736154

Cited by 163 publications

(32 citation statements)

References 21 publications

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“…For example, multilayer perceptron [15,16], SVM [9,17-20], hidden markov model [21], neural networks [22], bayesian classifier [23] and fuzzy classifier [24-26] techniques have been proposed. Multiple features have been investigated including AR model coefficients [22,24,26,27], mean absolute value [27,28], slope sign changes [29,30], zero crossings [27-29], waveform length [29,30] and wavelet packet transform [15,31]. …”

Section: Introductionmentioning

confidence: 99%

A preliminary investigation assessing the viability of classifying hand postures in seniors

2011

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BackgroundFear of frailty is a main concern for seniors. Surface electromyography (sEMG) controlled assistive devices for the upper extremities could potentially be used to augment seniors' force while training their muscles and reduce their fear of frailty. In fact, these devices could both improve self confidence and facilitate independent leaving in domestic environments. The successful implementation of sEMG controlled devices for the elderly strongly relies on the capability of properly determining seniors' actions from their sEMG signals. In this research we investigated the viability of classifying hand postures in seniors from sEMG signals of their forearm muscles.MethodsNineteen volunteers, including seniors (70 years old in average) and young people (27 years old in average), participated in this study and sEMG signals from four of their forearm muscles (i.e. Extensor Digitorum, Palmaris Longus, Flexor Carpi Ulnaris and Extensor Carpi Radialis) were recorded. The feature vectors were built by extracting features from each channel of sEMG including autoregressive (AR) model coefficients, waveform length and root mean square (RMS). Multi-class support vector machines (SVM) was used as a classifier to distinguish between fifteen different essential hand gestures including finger pinching.ResultsClassification of hand gestures both in the pronation and supination positions of the arm was possible. Classified hand gestures were: rest, ulnar deviation, radial deviation, grasp and four different finger pinching configurations. The obtained average classification accuracy was 90.6% for the seniors and 97.6% for the young volunteers.ConclusionsThe obtained results proved that the pattern recognition of sEMG signals in seniors is feasible for both pronation and supination positions of the arm and the use of only four EMG channel is sufficient. The outcome of this study therefore validates the hypothesis that, although there are significant neurological and physical changes occurring in humans while ageing, sEMG controlled hand assistive devices could potentially be used by the older people.

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Section: Introductionmentioning

confidence: 99%

A preliminary investigation assessing the viability of classifying hand postures in seniors

2011

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“…This feature is an indicator of firing motor unit action potentials (MUAP) and therefore an indicator of the muscle contraction level [25]. …”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…However, first, with the fast development of sensor technology, many sensors are designed with amplifiers, software selectable filters and motion artifact suppression, like the Trigno™ Wireless EMG System and SX230FW of Biometrics. Secondly, many studies have considered the knowledge of anatomical landmarks for the location of EMG sensors [1,3,7,19,20,21,22,23,24,25,26], and indeed recent years have seen the fast development of EMG technology in monitoring wearable systems. …”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

Tang

Miran

et al. 2017

Sensors

113

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Abstract:As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.

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“…Starting from one of the first works that made use of a multilayer perceptron (MLP) neural networks (NNs) [9-11], various classifiers such as linear discriminant analysis (LDA) [12-14], (neuro) fuzzy [15-20], Gaussian mixture models (GMMs) [21,22], hidden Markov models (HMMs) [18], and support vector machines (SVMs) [23-25] have been used. Some commonly investigated feature sets include time domain (TD) features: MAV, MAVS, ZC, SSC, WL [12,26], autoregressive (AR) coefficients [27], cepstral coefficients [28], the short-time Fourier transform (STFT), the wavelet transform (WT), the wavelet packet transform [13] (WPT), and concatenated TD and AR (TDAR) [21,29] features.…”

Section: Related Workmentioning

confidence: 99%

An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning

Camacho

Llanos

Berger

et al. 2013

BioMedical Engineering OnLine

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BackgroundThe information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers. The iPCA pre-processing implies an optimization stage which has not yet been deeply explored.MethodsThe present study considers two factors in the iPCA stage: namely A (the fitness function), and B (the search algorithm). The A factor comprises two levels, namely A1 (the classification error) and A2 (the correlation factor). Otherwise, the B factor has four levels, specifically B1 (the Sequential Forward Selection, SFS), B2 (the Sequential Floating Forward Selection, SFFS), B3 (Artificial Bee Colony, ABC), and B4 (Particle Swarm Optimization, PSO). This work evaluates the incidence of each one of the eight possible combinations between A and B factors over the classification error of the MCS.ResultsA two factor ANOVA was performed on the computed classification errors and determined that: (1) the interactive effects over the classification error are not significative (F0.01,3,72 = 4.0659 > f AB = 0.09), (2) the levels of factor A have significative effects on the classification error (F0.02,1,72 = 5.0162 < f A = 6.56), and (3) the levels of factor B over the classification error are not significative (F0.01,3,72 = 4.0659 > f B = 0.08).ConclusionsConsidering the classification performance we found a superiority of using the factor A2 in combination with any of the levels of factor B. With respect to the time performance the analysis suggests that the PSO algorithm is at least 14 percent better than its best competitor. The latter behavior has been observed for a particular configuration set of parameters in the search algorithms. Future works will investigate the effect of these parameters in the classification performance, such as length of the reduced size vector, number of particles and bees used during optimal search, the cognitive parameters in the PSO algorithm as well as the limit of cycles to improve a solution in the ABC algorithm.

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EMG pattern recognition based on artificial intelligence techniques

Cited by 163 publications

References 21 publications

A preliminary investigation assessing the viability of classifying hand postures in seniors

A preliminary investigation assessing the viability of classifying hand postures in seniors

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning

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