The present study used electroencephalography to examine mu rhythm suppression （a putative index of human mirror neuron system activation） at frontal sites （F3, Fz and F4）, central sites （C3, Cz and C4）, parieta...The present study used electroencephalography to examine mu rhythm suppression （a putative index of human mirror neuron system activation） at frontal sites （F3, Fz and F4）, central sites （C3, Cz and C4）, parietal sites （P3, Pz and P4） and occipital sites （O1 and O2）, while subjects observed real hand motion （real hand motion condition） and illustrative depictions of hand motion （drawn hand motion condition）. Experimental data revealed that mu rhythm suppression was exhibited in the mirror neuron system when subjects observed both real and drawn hand motion. Moreover, the mu rhythm recorded at the F3, Fz, F4, and Pz poles was significantly suppressed while observing both stimulus types, but no obvious mu suppression occurred at the O1, 02 and 03 poles. These results suggest that the observation of drawings of human hand actions can activate the human mirror neuron system. This evidence supports the hypothesis that the mirror neuron system may be involved in intransitively abstract action understanding.展开更多
This paper presents a real-time Kinect- based hand pose estimation method. Different from model-based and appearance-based approaches, our approach retrieves continuous hand motion parameters in real time. First, the ...This paper presents a real-time Kinect- based hand pose estimation method. Different from model-based and appearance-based approaches, our approach retrieves continuous hand motion parameters in real time. First, the hand region is segmented from the depth image. Then, some specific feature points on the hand are located by the random forest classifier, and the relative displacements of these feature points are transformed to a rotation invariant feature vector. Finally, the system retrieves the hand joint parameters by applying the regression functions on the feature vectors. Experimental results are compared with the ground truth dataset obtained by a data glove to show the effectiveness of our approach. The effects of different distances and different rotation angles for the estimation accuracy are also evaluated.展开更多
Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which...Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.展开更多
This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand inf...This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand information, and uses the improved PCA<sub><img src="Edit_d6662636-9073-4fbd-855f-9a36e871d5a4.png" width="10" height="15" alt="" /></sub> (Principal Component Analysis) to perform data fusion on the real-time data collected by the sensor to obtain more hands with fewer principal components, and improve the stability and accuracy of the data. Immediately, the use of improved SVM<sub><img src="Edit_10c78725-e09e-4dcf-ae05-e21205df4acc.png" width="10" height="15" alt="" /></sub> (Support Vector Machine) and KNN<sub><img src="Edit_0ee97f55-2773-4b48-93b3-93f61aa25577.png" width="10" height="15" alt="" /></sub> (K-Nearest Neighbor Algorithm) for gesture recognition and classification is proposed to enable patients to perform rehabilitation training more effectively. Finally, the effective evaluation results of the rehabilitation effect of patients by the idea of AHP<sub><img src="Edit_70dd1964-28be-4137-afa5-9a184704f08e.png" width="10" height="15" alt="" /></sub> (Analytic Hierarchy Process) are taken as necessary reference factors for doctors to follow up treatment. Various experimental results show that the system has achieved the expected results and has a good application prospect.展开更多
Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy.A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor functio...Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy.A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method.Through a series of novel design concepts,including the integration of a detecting circuit and an analog-to-digital converter,a miniaturized functional electrical stimulation circuit technique,a low-power super-regeneration chip for wireless receiving,and two wearable armbands,a prototype system has been established with reduced size,power,and overall cost.Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects,the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy.Test results showed that wrist flexion/extension,hand grasp,and finger extension could be reproduced with high accuracy and low latency.This system can build a bridge of information transmission between healthy limbs and paralyzed limbs,effectively improve voluntary participation of hemiplegic patients,and elevate efficiency of rehabilitation training.展开更多
According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network c...According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network consisting of six neurons is pro- posed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.展开更多
基金the Grants from the National Natural Science Foundation of China, No. 60775019, 60970062the Shanghai Pujiang Program, No. 09PJ1410200the Project-sponsored by SRF for ROCS, SEM
文摘The present study used electroencephalography to examine mu rhythm suppression （a putative index of human mirror neuron system activation） at frontal sites （F3, Fz and F4）, central sites （C3, Cz and C4）, parietal sites （P3, Pz and P4） and occipital sites （O1 and O2）, while subjects observed real hand motion （real hand motion condition） and illustrative depictions of hand motion （drawn hand motion condition）. Experimental data revealed that mu rhythm suppression was exhibited in the mirror neuron system when subjects observed both real and drawn hand motion. Moreover, the mu rhythm recorded at the F3, Fz, F4, and Pz poles was significantly suppressed while observing both stimulus types, but no obvious mu suppression occurred at the O1, 02 and 03 poles. These results suggest that the observation of drawings of human hand actions can activate the human mirror neuron system. This evidence supports the hypothesis that the mirror neuron system may be involved in intransitively abstract action understanding.
基金supported by NSC under Grand No.101-2221-E-468-030
文摘This paper presents a real-time Kinect- based hand pose estimation method. Different from model-based and appearance-based approaches, our approach retrieves continuous hand motion parameters in real time. First, the hand region is segmented from the depth image. Then, some specific feature points on the hand are located by the random forest classifier, and the relative displacements of these feature points are transformed to a rotation invariant feature vector. Finally, the system retrieves the hand joint parameters by applying the regression functions on the feature vectors. Experimental results are compared with the ground truth dataset obtained by a data glove to show the effectiveness of our approach. The effects of different distances and different rotation angles for the estimation accuracy are also evaluated.
文摘Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.
文摘This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand information, and uses the improved PCA<sub><img src="Edit_d6662636-9073-4fbd-855f-9a36e871d5a4.png" width="10" height="15" alt="" /></sub> (Principal Component Analysis) to perform data fusion on the real-time data collected by the sensor to obtain more hands with fewer principal components, and improve the stability and accuracy of the data. Immediately, the use of improved SVM<sub><img src="Edit_10c78725-e09e-4dcf-ae05-e21205df4acc.png" width="10" height="15" alt="" /></sub> (Support Vector Machine) and KNN<sub><img src="Edit_0ee97f55-2773-4b48-93b3-93f61aa25577.png" width="10" height="15" alt="" /></sub> (K-Nearest Neighbor Algorithm) for gesture recognition and classification is proposed to enable patients to perform rehabilitation training more effectively. Finally, the effective evaluation results of the rehabilitation effect of patients by the idea of AHP<sub><img src="Edit_70dd1964-28be-4137-afa5-9a184704f08e.png" width="10" height="15" alt="" /></sub> (Analytic Hierarchy Process) are taken as necessary reference factors for doctors to follow up treatment. Various experimental results show that the system has achieved the expected results and has a good application prospect.
基金supported by the National Natural Science Foundation of China,No.90307013,90707005,61534003the Science&Technology Pillar Program of Jiangsu Province in China,No.BE2013706
文摘Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy.A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method.Through a series of novel design concepts,including the integration of a detecting circuit and an analog-to-digital converter,a miniaturized functional electrical stimulation circuit technique,a low-power super-regeneration chip for wireless receiving,and two wearable armbands,a prototype system has been established with reduced size,power,and overall cost.Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects,the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy.Test results showed that wrist flexion/extension,hand grasp,and finger extension could be reproduced with high accuracy and low latency.This system can build a bridge of information transmission between healthy limbs and paralyzed limbs,effectively improve voluntary participation of hemiplegic patients,and elevate efficiency of rehabilitation training.
基金supported by the National Natural Science Foundation of China(Nos.11232005 and11472104)
文摘According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network consisting of six neurons is pro- posed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.
