EMG pattern recognition by neural networks for multi fingers control

Uchida, Naoya; Hiraiwa, Akira; Sonehara, Noboru; Shimohara, Katsunori

doi:10.1109/iembs.1992.5761230

Cited by 22 publications

(9 citation statements)

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“…The proposed method can infer the motions more accurately than the studies of Uchida et al (1992) and Chen et al (2007)…”

Section: Resultsmentioning

confidence: 84%

“…Nagata et al ( 2007 ) used absolute sum analysis, canonical component analysis, and minimum Euclidean distance to classify four wrist motions and five finger motions. Uchida et al ( 1992 ) used FFT analysis and NN to classify four finger motions. Chen et al ( 2007 ) used mean absolute values (MAV), the ratio of the MAVs, autoregressive (AR) model, and linear Bayesian classifier to classify 5~16 finger motions.…”

Section: Introductionmentioning

confidence: 99%

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Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

2010

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We provide a novel method to infer finger flexing motions using a four-channel surface electromyogram (EMG). Surface EMG signals can be recorded from the human body non-invasively and easily. Surface EMG signals in this study were obtained from four channel electrodes placed around the forearm. The motions consist of the flexion of five single fingers (thumb, index finger, middle finger, ring finger, and little finger) and three multi.finger motions. The maximum likelihood estimation was used to infer the finger motions. Experimental results have shown that this method can successfully infer the finger flexing motions. The average accuracy was as high as 97.75%. In addition, we examined the influence of inference accuracies with the various arm postures.

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“…The proposed method can infer the motions more accurately than the studies of Uchida et al (1992) and Chen et al (2007)…”

Section: Resultsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

2010

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“…This can be expressed as follows: The mioelectric control of multifingered hand prostheses was studied in several papers, for example (Nishikawa et al, 1991), (Uchida et al, 1992), (Farry et al, 1996), andChen, 1999). Most of the ideas in these efforts were inspired by Hudgins (Hudgins et al, 1991).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Informatics in Control, Automation and Robotics II

2007

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“…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%

“…EMG signal activation is associated with the muscle contraction and can be used to identify the motion. Researchers have been working on this issue for several decades [ 1 , 3 , 8 , 9 , 10 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. The critical problem of these investigations is the choice and computation of effective features from the signals and classification techniques.…”

Section: Introductionmentioning

confidence: 99%

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

Tang

Miran

et al. 2017

Sensors

110

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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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EMG pattern recognition by neural networks for multi fingers control

Abstract: This paper proposes that EMG patterns can be analyzed and classified by neural networks. Through experiments and on-line

Cited by 22 publications

References 1 publication

Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

Informatics in Control, Automation and Robotics II

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

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