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Xiang Chen
University of Science and Technology of China, Hefei, China

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Journal article
Published: 09 March 2021 in IEEE Journal of Translational Engineering in Health and Medicine
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Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average $F_{1}$ score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.

ACS Style

Jing Zhang; Deng Liang; Aiping Liu; Min Gao; Xiang Chen; Xu Zhang; Xun Chen. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG. IEEE Journal of Translational Engineering in Health and Medicine 2021, 9, 1 -11.

AMA Style

Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chen. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG. IEEE Journal of Translational Engineering in Health and Medicine. 2021; 9 ():1-11.

Chicago/Turabian Style

Jing Zhang; Deng Liang; Aiping Liu; Min Gao; Xiang Chen; Xu Zhang; Xun Chen. 2021. "MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG." IEEE Journal of Translational Engineering in Health and Medicine 9, no. : 1-11.

Journal article
Published: 07 December 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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Estimation of muscle contraction force based on the macroscopic feature of surface electromyography (SEMG) has been widely reported, but the use of microscopic neural drive information has not been thoroughly investigated. In this study, a novel method is proposed to process individual motor unit (MU) activities (firing sequences and action potential waveforms) derived from the decomposition of high density SEMG (HD-SEMG), and it is applied to muscle force estimation. In the proposed method, a supervised machine learning approach was conducted to determine the twitch force of each MU according to its action potential waveforms, which enables separate calculation of every MU’s contribution to force. Thus, the muscle force was predicted through a physiologically meaningful muscle force model. In the experiment, HD-SEMG data were recorded from the abductor pollicis brevis muscles of eight healthy subjects during their performance of thumb abduction with the force increasing gradually from zero to four force levels (10%, 20%, 30%, 40% of the maximal voluntary contraction), while the true muscle force was measured simultaneously. When the proposed method was used, the root mean square difference (RMSD) of the error of the estimated force with respect to the measured force was reported to be 8.3% ± 2.8%. The proposed method also significantly outperformed the other four common methods for force estimation (RMSD: from 11.7% to 20%, p < 0.001), demonstrating its effectiveness. This study offers a useful tool for exploiting the neural drive information towards muscle force estimation with improved precision. The proposed method has wide applications in precise motor control, sport and rehabilitation medicine.

ACS Style

Xu Zhang; Ge Zhu; Maoqi Chen; Xun Chen; Xiang Chen; Ping Zhou. Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 3148 -3157.

AMA Style

Xu Zhang, Ge Zhu, Maoqi Chen, Xun Chen, Xiang Chen, Ping Zhou. Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (12):3148-3157.

Chicago/Turabian Style

Xu Zhang; Ge Zhu; Maoqi Chen; Xun Chen; Xiang Chen; Ping Zhou. 2020. "Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 12: 3148-3157.

Journal article
Published: 06 October 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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To reduce the bad effect of electrode shifts on myoelectric pattern recognition, this paper presents an adaptive electrode calibration method based on core activation regions of muscles. In the proposed method, the high-density surface electromyography (HD-sEMG) matrix collected during hand gesture execution is decomposed into source signal matrix and mixed coefficient matrix by fast independent component analysis algorithm firstly. The mixed coefficient vector whose source signal has the largest two-norm energy is selected as the major pattern, and core activation region of muscles is extracted by traversing the major pattern periodically using a sliding window. The electrode calibration is realized by aligning the core activation regions in unsupervised way. Gestural HD-sEMG data collection experiments with known and unknown electrode shifts are carried out on 9 gestures and 11 participants. A CNN+LSTM-based network is constructed and two network training strategies are adopted for the recognition task. The experimental results demonstrate the effectiveness of the proposed method in mitigating the bad effect of electrode shifts on gesture recognition accuracy and the potentials in reducing user training burden of myoelectric control systems. With the proposed electrode calibration method, the overall gesture recognition accuracies increase about (5.72∼7.69)%. In specific, the average recognition accuracy increases (13.32∼17.30)% when using only one batch of data in data diversity strategy, and increases (12.01∼13.75)% when using only one repetition of each gesture in model update strategy. The proposed electrode calibration algorithm can be extended and applied to improve the robustness of myoelectric control system.

ACS Style

Ruochen Hu; Xiang Chen; Xu Zhang; Xun Chen. Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 29, 11 -20.

AMA Style

Ruochen Hu, Xiang Chen, Xu Zhang, Xun Chen. Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 29 ():11-20.

Chicago/Turabian Style

Ruochen Hu; Xiang Chen; Xu Zhang; Xun Chen. 2020. "Adaptive Electrode Calibration Method Based on Muscle Core Activation Regions and Its Application in Myoelectric Pattern Recognition." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29, no. : 11-20.

Journal article
Published: 20 July 2020 in IEEE Transactions on Instrumentation and Measurement
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Acoustic localization is a viable solution for high-precision indoor positioning with current built-in smartphone sensors. However, the Doppler shift produces an additional error in the time difference of arrival (TDOA) estimation when the smartphone is in motion, resulting in degradation in the acoustic localization performance. In this paper, we present an indoor localization system for smartphones that overcomes the effects of the Doppler shift by combining inertial and acoustic sensors data. First, we establish error correction model related to the current position and velocity of the smartphone, which can estimate and mitigate the errors caused by the Doppler shift in the TDOA measurements. Second, a uniform acceleration dynamic model and an improved pedestrian dead reckoning (PDR) algorithm are designed to obtain the model parameter. Experiments show that the two proposed algorithms reduce the positioning error by 49.1% and 71.9 % under continuous acoustic signal conditions, and by 33.3 % and 69.8% under occasional abnormal signal conditions. The presented system can achieve a positioning accuracy of about 0.2m (RMS).

ACS Style

Tao Liu; Xiaoji Niu; Jian Kuang; Shuai Cao; Lei Zhang; Xiang Chen. Doppler Shift Mitigation in Acoustic Positioning Based on Pedestrian Dead Reckoning for Smartphone. IEEE Transactions on Instrumentation and Measurement 2020, 70, 1 -11.

AMA Style

Tao Liu, Xiaoji Niu, Jian Kuang, Shuai Cao, Lei Zhang, Xiang Chen. Doppler Shift Mitigation in Acoustic Positioning Based on Pedestrian Dead Reckoning for Smartphone. IEEE Transactions on Instrumentation and Measurement. 2020; 70 (99):1-11.

Chicago/Turabian Style

Tao Liu; Xiaoji Niu; Jian Kuang; Shuai Cao; Lei Zhang; Xiang Chen. 2020. "Doppler Shift Mitigation in Acoustic Positioning Based on Pedestrian Dead Reckoning for Smartphone." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-11.

Journal article
Published: 15 July 2020 in IEEE Journal of Biomedical and Health Informatics
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This paper presents an effective transfer learning (TL) strategy for the realization of surface electromyography (sEMG)-based gesture recognition with high generalization and low training burden. To realize the idea of taking a well-trained model as the feature extractor of the target networks, a convolutional neural network (CNN)-based source network is designed and trained as the general gesture EMG feature extraction network firstly. To fully cover possible muscle activation modes related to hand gestures, 30 hand gestures involving various states of finger joints, elbow joint and wrist joint are selected to compose the source task. Then, two types of target networks, in the forms of CNN-only and CNN+LSTM (long short-term memory) respectively, are designed with the same CNN architecture as the feature extraction network. Finally, gesture recognition experiments on three different target gesture datasets are carried out under TL and Non-TL strategies respectively. The experimental results verify the validity of the proposed TL strategy in improving hand gesture recognition accuracy and reducing training burden. For both the CNN-only and the CNN+LSTM target networks, on the three target datasets from new users, new gestures and different collection scheme, the proposed TL strategy improves the recognition accuracy by 10%~38%, reduces the training time to tens of times, and guarantees the recognition accuracy of more than 90% when only 2 repetitions of each gesture are used to fine-tune the parameters of target networks. The proposed TL strategy has important application value for promoting the development of myoelectric control systems.

ACS Style

Xiang Chen; Yu Li; Ruochen Hu; Xu Zhang; Xun Chen. Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method. IEEE Journal of Biomedical and Health Informatics 2020, 25, 1292 -1304.

AMA Style

Xiang Chen, Yu Li, Ruochen Hu, Xu Zhang, Xun Chen. Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method. IEEE Journal of Biomedical and Health Informatics. 2020; 25 (4):1292-1304.

Chicago/Turabian Style

Xiang Chen; Yu Li; Ruochen Hu; Xu Zhang; Xun Chen. 2020. "Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method." IEEE Journal of Biomedical and Health Informatics 25, no. 4: 1292-1304.

Journal article
Published: 16 June 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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The objective of this study is to explore the diagnostic decision and sensitivity of the surface electromyogram (EMG) clustering index (CI) with respect to post-stroke motor unit (MU) alterations through a simulation approach by the existing motor neuron pool model and surface EMG model. In the simulation analysis, three patterns of diagnostic decisions were presented in 24 groups representing eight types in three degrees of MU alterations. Specifically, the CI decision exhibited an abnormally increased pattern for five types, an abnormally decreased pattern for two types, and an invariant pattern for one type. Furthermore, the CI diagnostic decision was found to be highly sensitive to three types because a 50% degree of alteration in these types resulted in a distinct deviation of 2.5 in the CI Z-score. The mixed CI patterns were confirmed in experimental data collected from the paretic muscles of 14 subjects with stroke, as compared to the healthy muscles of 10 control subjects. Given the simulation results as a guideline, the CI diagnostic decision could be interpreted from general neural or muscular changes into specific MU changes (in eight types). This can further promote clinical applications of the convenient surface EMG tool in examining and monitoring paretic muscle changes toward customized stroke rehabilitation.

ACS Style

Xu Zhang; Xiao Tang; Zhongqing Wei; Xiang Chen; Xun Chen. Model-Based Sensitivity Analysis of EMG Clustering Index With Respect to Motor Unit Properties: Investigating Post-Stroke FDI Muscle. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 1836 -1845.

AMA Style

Xu Zhang, Xiao Tang, Zhongqing Wei, Xiang Chen, Xun Chen. Model-Based Sensitivity Analysis of EMG Clustering Index With Respect to Motor Unit Properties: Investigating Post-Stroke FDI Muscle. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (8):1836-1845.

Chicago/Turabian Style

Xu Zhang; Xiao Tang; Zhongqing Wei; Xiang Chen; Xun Chen. 2020. "Model-Based Sensitivity Analysis of EMG Clustering Index With Respect to Motor Unit Properties: Investigating Post-Stroke FDI Muscle." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 8: 1836-1845.

Journal article
Published: 13 April 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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High-density surface electromyography (HD-sEMG) can provide rich temporal and spatial information about muscle activation. However, HD-sEMG signals are often contaminated by power line interference (PLI) and white Gaussian noise (WGN). In the literature, independent component analysis (ICA) and canonical correlation analysis (CCA), as two popular used blind source separation techniques, are widely used for noise removal from HD-sEMG signals. In this paper, a novel method to remove PLI and WGN was proposed based on independent vector analysis (IVA). Taking advantage of both ICA and CCA, this method exploits the higher order and second-order statistical information simultaneously. Our proposed method was applied to both simulated and experimental EMG data for performance evaluation, which was at least 37.50% better than ICA and CCA methods in terms of relative root mean squared error and 28.84% better than ICA and CCA methods according to signal to noise ratio. The results demonstrated that our proposed method performed significantly better than either ICA or CCA. Specifically, the mean signal to noise ratio increased considerably. Our proposed method is a promising tool for denoising HD-sEMG signals while leading to a minimal distortion.

ACS Style

Kun Wang; Xun Chen; Le Wu; Xu Zhang; Xiang Chen; Z. Jane Wang. High-Density Surface EMG Denoising Using Independent Vector Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 1271 -1281.

AMA Style

Kun Wang, Xun Chen, Le Wu, Xu Zhang, Xiang Chen, Z. Jane Wang. High-Density Surface EMG Denoising Using Independent Vector Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (6):1271-1281.

Chicago/Turabian Style

Kun Wang; Xun Chen; Le Wu; Xu Zhang; Xiang Chen; Z. Jane Wang. 2020. "High-Density Surface EMG Denoising Using Independent Vector Analysis." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 6: 1271-1281.

Journal article
Published: 19 March 2020 in IEEE Transactions on Instrumentation and Measurement
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To achieve high precision and robust positioning on mobile devices, we propose a novel audio signal arrival time detection algorithm consisting of both coarse and fine searches. A coarse search uses the cosine theorem to extract the audio data segment (ADS) from the received signal of a mobile device, and a fine search then uses the waveform characteristics of the auto-correlation function of the source signal to estimate the audio arrival time (AAT) from the ADS. An indoor threshold determination experiment, a static positioning experiment, and a moving target positioning experiment were carried out on the developed acoustic indoor positioning system (AIPS) in a hall. The performance of the proposed audio detection algorithm was compared to two other typical detection algorithms. The results of the static positioning experiment show that, in an non-line of sight (NLOS) environment, 90% of the positioning results estimated by the proposed algorithm have an error of less than 0.50 m, whereas only 56% and 6% of the positioning results estimated by the other two classical algorithms have errors of less than 0.50 m. In the target moving positioning experiment, the positions estimated by the proposed algorithm are closer to the true trajectories and have fewer (less than 0.5%) abnormal positioning errors than the other two classical algorithms. The experiment results also show that the reliability judgement of the positioning results can further improve the robustness of the system. The proposed method has important application value for the implementation of a highly precise and reliable AIPS.

ACS Style

Shuai Cao; Xiang Chen; Xu Zhang; Xun Chen. Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning. IEEE Transactions on Instrumentation and Measurement 2020, 69, 7341 -7352.

AMA Style

Shuai Cao, Xiang Chen, Xu Zhang, Xun Chen. Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning. IEEE Transactions on Instrumentation and Measurement. 2020; 69 (10):7341-7352.

Chicago/Turabian Style

Shuai Cao; Xiang Chen; Xu Zhang; Xun Chen. 2020. "Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning." IEEE Transactions on Instrumentation and Measurement 69, no. 10: 7341-7352.

Original article
Published: 21 November 2019 in Medical & Biological Engineering & Computing
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Taking advantage of motion sensing technology, a quantitative assessment method for lower limbs motor function of cerebral palsy (CP) based on the gross motor function measurement (GMFM)-24 scale was explored in this study. According to the motion analysis on GMFM-24 scale, we translated the assessment problem of GMFM-24 scale into a detection problem of different motion modes including static state, fall, step, turning, alternating gait, walking, running, lifting legs, kicking balls, and jumping. The surface electromyography (sEMG) electrodes and inertial sensors were adopted to capture motion data, and a framework integrating a series of detection algorithms was presented for the assessment of lower limbs gross motor function. Two groups of participants including 8 healthy adults and 14 CP children were recruited. A self-developed data acquisition equipment integrating 24 sEMG electrodes and 9 inertial units was adopted for data acquisition. A platform based on two laser beam sensors was used to perform cross-border detection. The parameters/thresholds of motion detection algorithms were determined by the data from healthy adults, and the lower limbs gross motor function evaluation was conducted on 14 CP children. The experimental results verified the feasibility and effectiveness of the proposed quantitative assessment method. Compared to the clinical assessment score based on GMFM-24 scale, 90.1% accuracy was obtained for evaluation of 303 tasks in 14 CP children. The objective motor function assessment method proposed has potential application value for the quantitative assessment of lower limbs motor function of CP children in clinical practice. The algorithm framework for the assessment of lower limbs gross motor function. Using the GMFM-24 scale as the evaluation standard, a quantitative evaluation program for the lower limbs gross motor function of CP children based on motion sensing technology was proposed.

ACS Style

Xiang Chen; Qi Wu; Lu Tang; Shuai Cao; Xu Zhang; Xun Chen. Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors. Medical & Biological Engineering & Computing 2019, 58, 101 -116.

AMA Style

Xiang Chen, Qi Wu, Lu Tang, Shuai Cao, Xu Zhang, Xun Chen. Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors. Medical & Biological Engineering & Computing. 2019; 58 (1):101-116.

Chicago/Turabian Style

Xiang Chen; Qi Wu; Lu Tang; Shuai Cao; Xu Zhang; Xun Chen. 2019. "Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors." Medical & Biological Engineering & Computing 58, no. 1: 101-116.

Journal article
Published: 16 September 2019 in IEEE Journal of Biomedical and Health Informatics
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In this paper, deep belief net (DBN) was applied into the field of wearable-sensor based Chinese Sign Language (CSL) recognition. Eight subjects were involved in the study, and all of the subjects finished a five-day experiment performing CSL on a target word set consisting of 150 CSL subwords. During the experiment, surface electromyography (sEMG), accelerometer (ACC), and gyroscope (GYRO) signals were collected from the participants. In order to obtain the optimal structure of the network, three different sensor fusion strategies, including data-level fusion, feature-level fusion, and decision-level fusion, were explored. In addition, for the feature-level fusion strategy, two different feature sources, which are hand-crafted features and network generated features, and two different network structures, which are fully-connected net and DBN, were also compared. The result showed that feature level fusion could achieve the best recognition accuracy among the three fusion strategies, and feature-level fusion with network generated features and DBN could achieve the best recognition accuracy. The best recognition accuracy realized in this study was 95.1% for the user-dependent test and 88.2% for the user-independent test. The significance of the study is that it applied the deep learning method into the field of wearable sensors-based CSL recognition, and according to our knowledge it's the first study comparing human engineered features with the network generated features in the correspondent field. The results from the study shed lights on the method of using network-generated features during sensor fusion and CSL recognition.

ACS Style

Yi Yu; Xiang Chen; Shuai Cao; Xu Zhang; Xun Chen. Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net. IEEE Journal of Biomedical and Health Informatics 2019, 24, 1310 -1320.

AMA Style

Yi Yu, Xiang Chen, Shuai Cao, Xu Zhang, Xun Chen. Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net. IEEE Journal of Biomedical and Health Informatics. 2019; 24 (5):1310-1320.

Chicago/Turabian Style

Yi Yu; Xiang Chen; Shuai Cao; Xu Zhang; Xun Chen. 2019. "Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net." IEEE Journal of Biomedical and Health Informatics 24, no. 5: 1310-1320.

Accepted manuscript
Published: 01 July 2019 in Journal of Neural Engineering
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In this study, we proposed a dynamic voluntary contraction force estimation framework and implemented it for elbow-flexion force estimation during arm posture dynamically changing between pronation and supination. High-density surface electromyography (HD-sEMG) from biceps brachii and brachialis muscles and the elbow-flexion force were measured synchronously. The simplified Hill model was adopted to establish the relation between HD-sEMG and the elbow-flexion force. In the training process of the force estimation model, HD-sEMG data from static isometric elbow flexion tasks in two force modes (staircase and sinusoidal) and two postures (supination or pronation) were used. The nonnegative matrix factorization (NMF) algorithm was adopted to decompose HD-sEMG into activation patterns and the corresponding time-varying coefficient vectors. The major activation pattern was used to select the appropriate sEMG channels for extracting the input signal of the force estimation model. In the testing phase, the elbow-flexion force estimation during arm posture dynamically changing between pronation and supination was conducted. HD-sEMG was also decomposed using NMF algorithm. In this case, the concept of the major activation pattern is no longer applicable because the activation areas of biceps brachii and brachialis vary with the change of arm posture. An improved channel selection scheme based on the ratio of activation intensities was presented to extract the input signal of the well-trained simplified Hill model. (1) the improved channel selection scheme could locate effectively the primary muscle activation areas related to the change of arm posture; (2) the input signal extraction method based on the ratio of activation intensities obtained the best force estimation performance compared to the method based on all channels of HD-sEMG array and the method based on major activation pattern; (3) the force estimation performance was better when the simplified Hill model was calibrated with the sinusoidal force data rather than staircase data. This research provides an effective solution to realize muscle force estimation during a dynamic voluntary contraction task. It can be further extended to the research fields of biomechanics, sports, and rehabilitation medicine.

ACS Style

Ruochen Hu; Xiang Chen; Chengjun Huang; Shuai Cao; Xu Zhang; Xun Chen. Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination. Journal of Neural Engineering 2019, 16, 066005 .

AMA Style

Ruochen Hu, Xiang Chen, Chengjun Huang, Shuai Cao, Xu Zhang, Xun Chen. Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination. Journal of Neural Engineering. 2019; 16 (6):066005.

Chicago/Turabian Style

Ruochen Hu; Xiang Chen; Chengjun Huang; Shuai Cao; Xu Zhang; Xun Chen. 2019. "Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination." Journal of Neural Engineering 16, no. 6: 066005.

Journal article
Published: 22 May 2019 in Sensors
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This paper presents a novel audio indoor localization system. In the proposed system, four speakers placed at known positions transmit chirp signals according to the time-division multiple access (TDMA) plus frequency-division multiple access (FDMA) transmission scheme. A smartphone receives the signal via a built-in microphone and calculates the time differences of arrival (TDOAs). Using TDOA measurements, the position is estimated by the shrinking-circle method. In particular, to reduce the positioning error in moving conditions, a TDOA correction method based on Doppler shifts is proposed. The performance of the proposed system was evaluated in real-world experiments using a 10.971 m × 5.684 m positioning area. The results of the static-target positioning experiment showed that the TDMA+FDMA transmission scheme has more advantages in improving the update rate of the positioning system than the TDMA-only transmission scheme. The results of the moving-target positioning experiment under three different speeds demonstrated that the positioning errors were reduced by about 10 cm when the Doppler-shift-based TDOA correction method was adopted. This research provides a possible framework for the realization of a TDOA-chirp-based acoustic indoor positioning system with high positioning accuracy and update rate.

ACS Style

Xiang Chen; Yuheng Chen; Shuai Cao; Lei Zhang; Xu Zhang; Xun Chen. Acoustic Indoor Localization System Integrating TDMA+FDMA Transmission Scheme and Positioning Correction Technique. Sensors 2019, 19, 2353 .

AMA Style

Xiang Chen, Yuheng Chen, Shuai Cao, Lei Zhang, Xu Zhang, Xun Chen. Acoustic Indoor Localization System Integrating TDMA+FDMA Transmission Scheme and Positioning Correction Technique. Sensors. 2019; 19 (10):2353.

Chicago/Turabian Style

Xiang Chen; Yuheng Chen; Shuai Cao; Lei Zhang; Xu Zhang; Xun Chen. 2019. "Acoustic Indoor Localization System Integrating TDMA+FDMA Transmission Scheme and Positioning Correction Technique." Sensors 19, no. 10: 2353.

Review
Published: 21 March 2019 in IEEE Sensors Journal
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Electroencephalography (EEG) has been widely used for studying brain function. As cortical signals recorded by the EEG are very weak, they are often obscured by motion artifacts and various non-brain physiological activities, such as eye movement, heart rhythms, and muscle activity, adversely affecting subsequent analysis and interpretation. Over the past decades, a number of techniques have been developed for preprocessing EEG recordings to improve the signal-to-noise ratio. However, based on our extensive literature survey, despite an increasing trend recently, only 14.72% of published studies on EEG preprocessing were involved with the removal of electromygraphic (EMG) artifacts. Given that ambulatory healthcare systems are continuously emerging, artifacts induced by muscle contraction become unavoidable, whereas in the past data tended to be collected in well-controlled clinical/laboratory settings. Motivated by the fact that EMG artifact removal is becoming an important issue to be addressed, we investigated the state-of-the-art muscle artifact removal methods systematically and comparatively, from the perspective of signal processing. In this review, we first present the signal characteristics from brain and muscle activity, and highlight the importance of this issue for subsequent artifact removal. We then provide an overview and taxonomy of representative methods. Based on the results in reported studies, we describe the pros and cons of different methods and give suggestions on selecting a suitable technique in different scenarios (e.g., single-channel, few-channel, and multichannel). Finally, we discuss remaining challenges and provide feasible recommendations for further exploration in this field.

ACS Style

Xun Chen; Xueyuan Xu; Aiping Liu; Soojin Lee; Xiang Chen; Xu Zhang; Martin J. McKeown; Z. Jane Wang. Removal of Muscle Artifacts From the EEG: A Review and Recommendations. IEEE Sensors Journal 2019, 19, 5353 -5368.

AMA Style

Xun Chen, Xueyuan Xu, Aiping Liu, Soojin Lee, Xiang Chen, Xu Zhang, Martin J. McKeown, Z. Jane Wang. Removal of Muscle Artifacts From the EEG: A Review and Recommendations. IEEE Sensors Journal. 2019; 19 (14):5353-5368.

Chicago/Turabian Style

Xun Chen; Xueyuan Xu; Aiping Liu; Soojin Lee; Xiang Chen; Xu Zhang; Martin J. McKeown; Z. Jane Wang. 2019. "Removal of Muscle Artifacts From the EEG: A Review and Recommendations." IEEE Sensors Journal 19, no. 14: 5353-5368.

Journal article
Published: 08 August 2018 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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The objective of this paper was to develop sample entropy (SampEn) as a novel surface electromyogram (EMG) biomarker to quantitatively examine post-stroke neuromuscular alternations. The SampEn method was performed on surface EMG interference patterns recorded from biceps brachii muscles of nine healthy control subjects, fourteen subjects with subacute stroke, and eleven subjects with chronic stroke, respectively. Measurements were collected during isometric contractions of elbow flexion at different constant force levels. By producing diagnostic decisions for individual muscles, two categories of abnormalities in some paretic muscles were discriminated in terms of abnormally increased and decreased SampEn. The efficiency of the SampEn was demonstrated by its comparable performance with a previously reported clustering index (CI) method. Mixed SampEn (or CI) patterns were observed in paretic muscles of subjects with stroke indicating complex neuromuscular changes at work as a result of a hemispheric brain lesion. Although both categories of SampEn (or CI) abnormalities were observed in both subacute and chronic stages of stroke, the underlying processes contributing to the SampEn abnormalities might vary a lot in stroke stage. The SampEn abnormalities were also found in contralateral muscles of subjects with chronic stroke indicating the necessity of applying interventions to contralateral muscles during stroke rehabilitation. Our work not only presents a novel method for quantitative examination of neuromuscular changes, but also explains the neuropathological mechanisms of motor impairments and offers guidelines for a better design of effective rehabilitation protocols toward improved motor recovery.

ACS Style

Xiao Tang; Xu Zhang; Xiaoping Gao; Xiang Chen; Ping Zhou. A Novel Interpretation of Sample Entropy in Surface Electromyographic Examination of Complex Neuromuscular Alternations in Subacute and Chronic Stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2018, 26, 1878 -1888.

AMA Style

Xiao Tang, Xu Zhang, Xiaoping Gao, Xiang Chen, Ping Zhou. A Novel Interpretation of Sample Entropy in Surface Electromyographic Examination of Complex Neuromuscular Alternations in Subacute and Chronic Stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2018; 26 (9):1878-1888.

Chicago/Turabian Style

Xiao Tang; Xu Zhang; Xiaoping Gao; Xiang Chen; Ping Zhou. 2018. "A Novel Interpretation of Sample Entropy in Surface Electromyographic Examination of Complex Neuromuscular Alternations in Subacute and Chronic Stroke." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 9: 1878-1888.

Accepted manuscript
Published: 16 July 2018 in Journal of Neural Engineering
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OBJECTIVE. TO INVESTIGATE THE ACTIVATION HETEROGENEITY OF SKELETAL MUSCLES AND REALIZE THE JOINT FORCE ESTIMATION DURING ELBOW FLEXION TASK. APPROACH. When an isometric elbow flexion task was performed, high-density surface electromyography (HD-sEMG) signals from A 8×16 GRID covering the front and inside of the upper arm and the generated joint force were recorded synchronously. HD-sEMG signals were preprocessed and then decomposed into source signals corresponding to BICEPS BRACHHI (BB) AND BRACHIALIS (BR) and their contribution vectors using FAST INDEPENDENT COMPONENT ANALYSIS (FastICA) algorithm. The activation heterogeneity of BB and BR was investigated from activation level and activation region firstly. Then, the contribution combinations of two sources were classified into several major clusters using K-means clustering method. Afterwards, input signals for force estimation were extracted from the major clusters corresponding to different combinations, and the polynomial fitting technique was adopted as force estimation model. Finally, the force estimation results were obtained and the analysis around the force estimation performance using different input signals was conducted. MAIN RESULTS. TEN SUBJECTS WERE RECRUITED IN THIS RESEARCH. The experimental results demonstrated that it is feasible to analyze the activation heterogeneity of muscles from activation level and activation region, and to select the appropriate region of the HD-sEMG grid for high performance force estimation. For the isometric elbow flexion task, joint force estimation accuracy could be improved when the input signal was extracted from the special area that the contribution difference of BB and BR to the HD-sEMG signals were relatively small. SIGNIFICANCE. The proposed framework provided a novel way to explore the relationship between muscle activation and the generating joint force, and could be extended to multiple noteworthy research fields such as myoelectric prostheses, sports biomechanics, and muscle disease diagnosis.

ACS Style

Cong Zhang; Xiang Chen; Shuai Cao; Xu Zhang; Xun Chen. HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion. Journal of Neural Engineering 2018, 15, 056027 .

AMA Style

Cong Zhang, Xiang Chen, Shuai Cao, Xu Zhang, Xun Chen. HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion. Journal of Neural Engineering. 2018; 15 (5):056027.

Chicago/Turabian Style

Cong Zhang; Xiang Chen; Shuai Cao; Xu Zhang; Xun Chen. 2018. "HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion." Journal of Neural Engineering 15, no. 5: 056027.

Journal article
Published: 20 April 2018 in Sensors
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Time difference of arrival (TDoA) measurement is a promising approach for target localization based on a set of nodes with known positions, with high accuracy and low complexity. Common localization algorithms include the maximum-likelihood, non-linear least-squares and weighted least-squares methods. These methods have shortcomings such as high computational complexity, requiring an initial guess position, or having difficulty in finding the optimal solution. From the point of view of geometrical analysis, this study proposes two new shrinking-circle methods (SC-1 and SC-2) to solve the TDoA-based localization problem in a two-dimensional (2-D) space. In both methods, an optimal radius is obtained by shrinking the radius with a dichotomy algorithm, and the position of the target is determined by the optimal radius. The difference of the two methods is that a distance parameter is defined in SC-1, while an error function is introduced in SC-2 to guide the localization procedure. Simulations and indoor-localization experiments based on acoustic transducers were conducted to compare the performance differences between the proposed methods, algorithms based on weighted least-squares as well as the conventional shrinking-circle method. The experimental results demonstrate that the proposed methods can realize high-precision target localization based on TDoA measurements using three nodes, and have the advantages of speed and high robustness.

ACS Style

Mingzhi Luo; Xiang Chen; Shuai Cao; Xu Zhang. Two New Shrinking-Circle Methods for Source Localization Based on TDoA Measurements. Sensors 2018, 18, 1274 .

AMA Style

Mingzhi Luo, Xiang Chen, Shuai Cao, Xu Zhang. Two New Shrinking-Circle Methods for Source Localization Based on TDoA Measurements. Sensors. 2018; 18 (4):1274.

Chicago/Turabian Style

Mingzhi Luo; Xiang Chen; Shuai Cao; Xu Zhang. 2018. "Two New Shrinking-Circle Methods for Source Localization Based on TDoA Measurements." Sensors 18, no. 4: 1274.

Journal article
Published: 19 November 2017 in Entropy
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The objective of this study is to re-evaluate the relation between surface electromyogram (EMG) and muscle contraction torque in biceps brachii (BB) muscles of healthy subjects using two different complexity measures. Ten healthy subjects were recruited and asked to complete a series of elbow flexion tasks following different isometric muscle contraction levels ranging from 10% to 80% of maximum voluntary contraction (MVC) with each increment of 10%. Meanwhile, both the elbow flexion torque and surface EMG data from the muscle were recorded. The root mean square (RMS), sample entropy (SampEn) and fuzzy entropy (FuzzyEn) of corresponding EMG data were analyzed for each contraction level, and the relation between EMG and muscle torque was accordingly quantified. The experimental results showed a nonlinear relation between the traditional RMS amplitude of EMG and the muscle torque. By contrast, the FuzzyEn of EMG exhibited an improved linear correlation with the muscle torque than the RMS amplitude of EMG, which indicates its great value in estimating BB muscle strength in a simple and straightforward manner. In addition, the SampEn of EMG was found to be insensitive to the varying muscle torques, almost presenting a flat trend with the increment of muscle force. Such a character of the SampEn implied its potential application as a promising surface EMG biomarker for examining neuromuscular changes while overcoming interference from muscle strength.

ACS Style

Xiaofei Zhu; Xu Zhang; Xiao Tang; Xiaoping Gao; Xiang Chen. Re-Evaluating Electromyogram–Force Relation in Healthy Biceps Brachii Muscles Using Complexity Measures. Entropy 2017, 19, 624 .

AMA Style

Xiaofei Zhu, Xu Zhang, Xiao Tang, Xiaoping Gao, Xiang Chen. Re-Evaluating Electromyogram–Force Relation in Healthy Biceps Brachii Muscles Using Complexity Measures. Entropy. 2017; 19 (11):624.

Chicago/Turabian Style

Xiaofei Zhu; Xu Zhang; Xiao Tang; Xiaoping Gao; Xiang Chen. 2017. "Re-Evaluating Electromyogram–Force Relation in Healthy Biceps Brachii Muscles Using Complexity Measures." Entropy 19, no. 11: 624.

Journal article
Published: 04 October 2017 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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This study presents automatic decomposition of high density surface electromyogram (EMG) signals through a progressive FastICA peel-off (PFP) framework. By incorporating FastICA, constrained FastICA and a peel-off strategy, the PFP can progressively expand the set of motor unit spike trains contributing to the EMG signal. A series of signal processing techniques were applied and integrated in this study to automatically implement the two tasks that often require human operator interaction during application of the PFP framework, including extraction of motor unit spike trains from FastICA outputs and reliability judgement of the extracted motor units. Based on these advances, an automatic PFP (APFP) framework was consequently developed. The decomposition performance of APFP was validated using simulated high density surface EMG signals. The APFP was also evaluated with experimental surface EMG signals, and the decomposition results were comparable to those achieved from the PFP with human operator interaction.

ACS Style

Maoqi Chen; Xu Zhang; Xiang Chen; Ping Zhou. Automatic Implementation of Progressive FastICA Peel-Off for High Density Surface EMG Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017, 26, 144 -152.

AMA Style

Maoqi Chen, Xu Zhang, Xiang Chen, Ping Zhou. Automatic Implementation of Progressive FastICA Peel-Off for High Density Surface EMG Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017; 26 (1):144-152.

Chicago/Turabian Style

Maoqi Chen; Xu Zhang; Xiang Chen; Ping Zhou. 2017. "Automatic Implementation of Progressive FastICA Peel-Off for High Density Surface EMG Decomposition." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 1: 144-152.

Original article
Published: 18 July 2017 in Medical & Biological Engineering & Computing
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This study proposed an independent component analysis (ICA)-based framework for localization and activation level analysis of muscle–tendon units (MTUs) within skeletal muscles during dynamic motion. The gastrocnemius muscle and extensor digitorum communis were selected as target muscles. High-density electrode arrays were used to record surface electromyographic (sEMG) data of the targeted muscles during dynamic motion tasks. First, the ICA algorithm was used to decompose multi-channel sEMG data into a weight coefficient matrix and a source matrix. Then, the source signal matrix was analyzed to determine EMG sources and noise sources. The weight coefficient vectors corresponding to the EMG sources were mapped to target muscles to find the location of the MTUs. Meanwhile, the activation level changes in MTUs during dynamic motion tasks were analyzed based on the corresponding EMG source signals. Eight subjects were recruited for this study, and the experimental results verified the feasibility and practicality of the proposed ICA-based method for the MTUs’ localization and activation level analysis during dynamic motion. This study provided a new, in-depth way to analyze the functional state of MTUs during dynamic tasks and laid a solid foundation for MTU-based accurate muscle force estimation, muscle fatigue prediction, neuromuscular control characteristic analysis, etc.

ACS Style

Xiang Chen; Shaoping Wang; Chengjun Huang; Shuai Cao; Xu Zhang. ICA-based muscle–tendon units localization and activation analysis during dynamic motion tasks. Medical & Biological Engineering & Computing 2017, 56, 341 -353.

AMA Style

Xiang Chen, Shaoping Wang, Chengjun Huang, Shuai Cao, Xu Zhang. ICA-based muscle–tendon units localization and activation analysis during dynamic motion tasks. Medical & Biological Engineering & Computing. 2017; 56 (3):341-353.

Chicago/Turabian Style

Xiang Chen; Shaoping Wang; Chengjun Huang; Shuai Cao; Xu Zhang. 2017. "ICA-based muscle–tendon units localization and activation analysis during dynamic motion tasks." Medical & Biological Engineering & Computing 56, no. 3: 341-353.

Journal article
Published: 24 May 2017 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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The objective of this paperwas to characterize complex neuromuscular changes induced by a hemisphere stroke through a novel clustering index (CI) analysis of surface electromyogram (EMG). The CI analysis was performed using surface EMG signals collected bilaterally from the thenar muscles of 17 subjects with stroke and 12 age-matched healthy controls during their performance of varying levels of isometric muscle contractions. Compared with the neurologically intact or contralateral muscles, mixed CI patterns were observed in the paretic muscles. Two paretic muscles showed significantly increased CI implying dominant neurogenic changes, whereas three paretic muscles had significantly reduced CI indicating dominantmyopathic changes; the other paretic muscles did not demonstrate a significant CI alternation, likely due to a deficit of descending central drive or a combined effect of neuromuscular factors. Such discrimination of paretic muscles was further highlighted using a modified CI method that emphasizes between-side comparison for each individual subject. The CI findings suggest that there appears to be different central and peripheral processes at work in varying degrees after stroke. This paper provides a convenient and quantitative analysis to assess the nature of neuromuscular changes after stroke, without using any special equipment but conventional surface EMG recording. Such assessment is helpful for the development of appropriate rehabilitation strategies for recovery of motor function.

ACS Style

Xu Zhang; Zhongqing Wei; Xiaoting Ren; Xiaoping Gao; Xiang Chen; Ping Zhou. Complex Neuromuscular Changes Post-Stroke Revealed by Clustering Index Analysis of Surface Electromyogram. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017, 25, 2105 -2112.

AMA Style

Xu Zhang, Zhongqing Wei, Xiaoting Ren, Xiaoping Gao, Xiang Chen, Ping Zhou. Complex Neuromuscular Changes Post-Stroke Revealed by Clustering Index Analysis of Surface Electromyogram. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017; 25 (11):2105-2112.

Chicago/Turabian Style

Xu Zhang; Zhongqing Wei; Xiaoting Ren; Xiaoping Gao; Xiang Chen; Ping Zhou. 2017. "Complex Neuromuscular Changes Post-Stroke Revealed by Clustering Index Analysis of Surface Electromyogram." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 11: 2105-2112.