Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions

Kim, Kang Soo; Choi, Heung‐Kook; Moon, Chang Soo; Mun, Chi-Woong

doi:10.1016/j.cap.2010.11.051

Cited by 293 publications

(133 citation statements)

References 14 publications

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“…Temporal characteristics were calculated based on the sEMG signal amplitude, which is simpler than the calculating frequency characteristic using the Fourier transform. Thus, in this paper three representative characteristics [11,12], which are MAV, RMS and logD, are Selected to decode the surface EMG by taking 8-channels sEMG characteristics at the same time.…”

Section: Methods For Grasping Force Estimation and Experimental Resultmentioning

confidence: 99%

Grasping force estimation for prosthetic hands via feature extraction of surface EMG

Xu¹,

Zhu²,

Duan³

2016

Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

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Abstract.A prosthetic hand with a self-regulated grip force could achieve different operation modes, which can help the upper limb amputees to fetch objects of different shapes. To get the appropriate grasping force with smaller samples and shorter training time, the method of threshold value judgment in this paper is effective on achieving the estimate of the discrete force basing on the mean absolute value (MAV) of EMG's level. The 10 subjects can be divided into 8 grasping patterns determined through three levels: the small, medium and great of grasping forces in experiments. Experimental results conditioned on small training samples and short training time show that the accuracy of force estimation is 72.91±9.58% and thereby convincing the effectiveness and reality of the proposed method.

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Section: Methods For Grasping Force Estimation and Experimental Resultmentioning

confidence: 99%

Grasping force estimation for prosthetic hands via feature extraction of surface EMG

Xu¹,

Zhu²,

Duan³

2016

Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

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“…The state of the art comprises a lot of machine learning methods that are applied to sEMG signals with promising results [15], [16], [17]. The main blocks of the classification chain needed in the prosthetic hand control consist on i) filtering and pre-processing; ii) segmentation; iii) features extraction and iv) classification.…”

Section: E Machine Learning Methods For Semg-based Hand Movement Clamentioning

confidence: 99%

Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control

Brunelli

Tadesse

Vodermayer

et al. 2015

2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI)

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Abstract-Prosthetic hand control based on the acquisition and processing of surface electromyography signals (sEMG) is a well-established method that makes use of the electric potentials evoked by the physiological contraction processes of one or more muscles. Furthermore intelligent mobile medical devices are on the brink of introducing safe and highly sophisticated systems to help a broad patient community to regain a considerable amount of life quality. The major challenges which are inherent in such integrated system's design are mainly to be found in obtaining a compact system with a long mobile autonomy, capable of delivering the required signal requirements for EMG based prosthetic control with up to 32 simultaneous acquisition channels and -with an eye on a possible future exploitation as a medical device -a proper perspective on a low priced system. Therefore, according to these requirements we present a wireless, mobile platform for acquisition and communication of sEMG signals embedded into a complete mobile control system structure. This environment further includes a portable device such as a laptop providing the necessary computational power for the control and a commercially available robotic handprosthesis. Means of communication among those devices are based on the Bluetooth standard. We show, that the developed low cost mobile device can be used for proper prosthesis control and that the device can rely on a continuous operation for the usual daily life usage of a patient.

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“…Krzanowski (1977) reviewed the performance of Fisher's linear discriminant function when underlying assumptions are violated. Many classification methods, both parametric and non-parametric, have been compared with LDA and QDA under normality and non-normality which include Ghosh and Chaudhuri (2005), Kim et al (2011) and Li et al (2012) among others.…”

Section: Introductionmentioning

confidence: 99%

On Misclassification Probabilities of Linear and Quadratic Classifiers

Makinde¹

2016

jas

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Abstract. We study the theoretical misclassification probability of linear and quadratic classifiers and examine the performance of these classifiers under distributional variations in theory and using simulation. We derive expression for Bayes errors for some competing distributions from the same family under location shift.Résumé. Nousétudions les probabilités théoriques de malclassification de méthodes de classification linéaires et quadratiques. Ensuite, nous examinons les performance de ces méthodes de classifications selon différentes distributions, sur le plan théorique en lesétayant avec deś etudes de simulations. Nous exprimons l'erreur de Bayes pour des distributions de même famille en compétition selon le changement du paramètre de centralisation.

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Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions

Cited by 293 publications

References 14 publications

Grasping force estimation for prosthetic hands via feature extraction of surface EMG

Grasping force estimation for prosthetic hands via feature extraction of surface EMG

Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control

On Misclassification Probabilities of Linear and Quadratic Classifiers

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