A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis

Karlık, Bekir; Tokhi, M. O.; Alcı, Musa

doi:10.1109/tbme.2003.818469

Cited by 166 publications

(71 citation statements)

References 21 publications

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“…It can be influenced by many factors, e.g., muscle cross talk [7] and interelectrode distance [8]. During the past decade, many efforts have been directed at developing different algorithms to process EMG signals, including classification of EMG using artificial network [9], fuzzy logic [10], and pattern recognition (multichannel EMG [11]) and decomposition of EMG signals with the Bayesian method [12]. However, in addition to the complexity of the required signal processing methods, use of EMG for noninvasively measuring deep muscles is difficult because the deep muscle EMG signal may be more attenuated and/or mixed with the superficial muscle EMG signal by the time it reaches the skin surface.…”

Section: Introductionmentioning

confidence: 99%

Dynamic monitoring of forearm muscles using one-dimensional sonomyography system

Guo

Zheng

Huang

et al. 2008

JRRD

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Abstract-We introduce a method, known as one-dimensional sonomyography (1-D SMG), that uses A-mode ultrasound signals to detect dynamic thickness changes in skeletal muscle during contraction. We custom-designed a 1-D SMG system to collect synchronized A-mode ultrasound, joint angle, and surface electromyography (EMG) signals of forearm muscles during wrist extension. We extracted the 1-D SMG signal from the ultrasound signal by automatically tracking the corresponding echoes, which we then used to calculate muscle thickness changes. We tested the right forearm muscles of nine nondisabled young subjects while they performed wrist extensions at 15.0, 22.5, and 30.0 cycles/min and their largest wrist extension angle ranged from 80° to 90°. We found that the muscle deformation and EMG root mean square signals correlated linearly with wrist extension angle. The ratio of deformation to wrist angle was significantly different among the subjects (p < 0.001) but not among the trials of different extension rates for each subject (p = 0.9). The results demonstrate that 1-D SMG can be reliably performed and that it has the potential for skeletal muscle assessment and prosthesis control.

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Section: Introductionmentioning

confidence: 99%

Dynamic monitoring of forearm muscles using one-dimensional sonomyography system

Guo

Zheng

Huang

et al. 2008

JRRD

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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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“…The extracted features were then fed into the fuzzy logic (FL) classifier for the developed control system. FL developed by Lofty Zadeh [35][36][37][38][39][40][41] provides a simple way to arrive at a definite conclusion based solely on imprecise input information. A summary of the feature extraction process from the forearm muscles is shown in Table 2 according to motion.…”

Section: Pattern Recognition With Fuzzy Logic Algorithmmentioning

confidence: 99%

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

Taşar¹,

Gülten²

2017

Modern Fuzzy Control Systems and Its Applications

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In recent years, researchers have conducted many studies on the design and control of prosthesis devices that take the place of a missing limb. Functional ability of prosthesis hands that mimic biological hand functions increases depending on the number of independent finger movements possible. From this perspective, in this study, six different finger movements were given to a prosthesis hand via bioelectrical signals, and the functionality of the prosthesis hand was increased. Bioelectrical signals were recorded by surface electromyography for four muscles with the help of surface electrodes. The recorded bioelectrical signals were subjected to a series of preprocessing and feature extraction processes. In order to create meaningful patterns of motion and an effective cognitive interaction network between the human and the prosthetic hand, fuzzy logic classification algorithms were developed. A five-fingered and 15-jointed prosthetic hand was designed via SolidWorks, and a prosthetic prototype was produced by a 3D printer. In addition, prosthetic hand simulator was designed in Matlab/SimMechanics. Pattern control of both the simulator and the prototype hand in real time was achieved. Position control of motors connected to each joint of the prosthetic hand was provided by a PID controller. Thus, an effective cognitive communication network established between the user, and the real-time pattern control of the prosthesis was provided by bioelectrical signals.

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A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis

Cited by 166 publications

References 21 publications

Dynamic monitoring of forearm muscles using one-dimensional sonomyography system

Dynamic monitoring of forearm muscles using one-dimensional sonomyography system

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

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

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