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IEEE International Conference on Robotics and Biomimetics

作者： Huanqing Zhang Jun Xie Yi Xiao Yuzhe Yang Zhiyuan Ren Ruiwen Zhang Yuanzhe Dong Xi Tang Guanglin Li Peng Fang School of Mechanical Engineering Xi'an Jiaotong University Xi'an China National Key Laboratory of Human Factors Engineering China Astronauts Research and Training Center Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9781665481106

With the rapid development of human-computer interaction technology, it has become very common to use various sensors for gesture recognition to control machines or to complete specified tasks. This study proposes a fusion method that uses Leap Motion for gesture recognition and gForcePro+ to evaluate actual force of hands. A three-dimensional (3-D) virtual gesture recognition platform was designed in Unity. Leap Motion was used to control gestures in the virtual environment, and surface electromyography (sEMG) data was used to distinguish different forces of the same gesture. The success of grasping objects was judged by detecting and analyzing the sEMG signals and Leap Motion signals, combined with the size of the grasping objects. Experimental results showed that Leap Motion and gForcePro+ can be used together to control hand motion. In addition, the proposed system can be used to distinguish the same gesture with different force sizes, which can reflect different forces when the hand grasp objects with the same action. It was proved that the fusion technique of gesture recognition system based on Leap Motion and sEMG is effective for online gesture recognition.

关键词： human computer interaction Ergonomics Force Virtual environments Gesture recognition Grasping human factors

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Speaker Embedding Augmentation with Noise Distribution Matching

Speaker Embedding Augmentation with Noise Distribution Match...

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International Symposium on Chinese Spoken Language Processing

作者： Xun Gong Zhengyang Chen Yexin Yang Shuai Wang Lan Wang Yanmin Qian MoE Key Lab of Artificial Intelligence SpeechLab Shanghai Jiao Tong University China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology

ISBN: (数字)9781728169941

ISBN: (纸本)9781728169958

Data augmentation (DA) is an effective strategy to help building robust systems with good generalization ability. In the embedding based speaker verification, data augmentation could be applied to either the front-end embedding extractor or the back-end PLDA. Unlike the conventional back-end augmentation method which adds noises to the raw audios and then extracts augmented embeddings, in this work, we proposed a noise distribution matching (NDM) based algorithm in the speaker embedding space. The basic idea is to use distributions such as Gaussian to model the difference between the clean and original augmented noisy speaker embeddings. Experiments are carried out on SRE16 dataset, where consistent performance improvement could be obtained by the novel NDM. Furthermore, we found that the proposed NDM could be robustly estimated using only a small amount of training data, which saves time and disk cost compared to the conventional augmentation method.

关键词： Image color analysis Training data Data visualization Manuals Gaussian distribution Noise measurement Data mining

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Performance Evaluation of Head-Mounted Display based Brain-Computer Interface for Virtual Robotic Arm Operation*

Performance Evaluation of Head-Mounted Display based Brain-C...

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IEEE International Conference on Robotics and Biomimetics

作者： Yuzhe Yang Jun Xie Yi Xiao Zhiyuan Ren Huanqing Zhang Xianzi Xiao Yuanzhe Dong Xi Tang Guanglin Li Peng Fang School of Mechanical Engineering Xi’an Jiaotong University Xi'an China National Key Laboratory of Human Factors Engineering China Astronauts Research and Training Center Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and the Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9781665481106

The steady-state visual evoked potential based brain-computer interface (SSVEP BCI) mostly uses desktop display to present the visual stimulation, and it cannot meet the needs of immersive brain-computer interaction. This study proposed the head-mounted display (HMD) implementation of the SSVEP BCI. And based on the head-mounted virtual reality (VR) glasses device, this study established the BCI system in the VR environment for the operation of the virtual robotic arm. In offline experiments, the system accuracy was 86.66% and the information transfer rate (ITR) was 18.33 bits/min. And in the online experiment, the recognition accuracy was 88.08% and the ITR was 19.26 bits/min. Experimental results verified the effectiveness of the proposed HMD based BCI system in the operation of the virtual robotic arm.

关键词： Performance evaluation Visualization Head-mounted displays Resists Virtual reality Manipulators Brain-computer interfaces

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SA-GCNN: Spatial Attention Based Graph Convolutional Neural Network for Pedestrian Trajectory Prediction

SA-GCNN: Spatial Attention Based Graph Convolutional Neural ...

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IEEE International Conference on Robotics and Biomimetics

作者： Xuesong Li Qieshi Zhang Wanting Wang Jian Tang Dong Liu Jun Cheng CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong China Midea Group (Shanghai) Co. Ltd China

Accurate predicting the trajectories of moving pedestrians is a key technology in automatic driving system, which is challenging due to the complex interactions in pedestrians. Recent studies have shown that Spatio-Temporal (ST) graph has great ability to capture interactions between pedestrians. However, these methods neglect pedestrian’s limited vision and contains many invalid interactions. In order to tackle this issue, we proposed a Spatial Attention based Graph Convolutional Neural Network (SA-GCNN), which uses SA module to construct ST graph and focus on the most useful interactions. Meanwhile, SA-GCNN introduces temporal convolution module to capture temporal dependency between ST graphs. Moreover, the Graph Convolutional Network (GCN) and Temporal Convolution Network (TCN) are combined to extract graph features and decode multi-modal trajectories. Our model is trained on the widely-accepted benchmark datasets ETH and UCY. The empirical findings demonstrate that our SA-GCNN surpasses the performance of existing state-of-the-art methods used for comparison, suggests that our proposed model exhibits enhanced proficiency in capturing pedestrian interactions.

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A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition

arXiv

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arXiv 2021年

作者： Li, Jin Su, Rongfeng Xie, Xurong Yan, Nan Wang, Lan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Institute of Software Chinese Academy of Sciences Beijing China

Transformer based end-to-end modelling approaches with multiple stream inputs have been achieved great success in various automatic speech recognition (ASR) tasks. An important issue associated with such approaches is that the intermediate features derived from each stream might have similar representations and thus it is lacking of feature diversity, such as the descriptions related to speaker characteristics. To address this issue, this paper proposed a novel multi-level acoustic feature extraction framework that can be easily combined with Transformer based ASR models. The framework consists of two input streams: a shallow stream with high-resolution spectrograms and a deep stream with low-resolution spectrograms. The shallow stream is used to acquire traditional shallow features that is beneficial for the classification of phones or words while the deep stream is used to obtain utterance-level speaker-invariant deep features for improving the feature diversity. A feature correlation based fusion strategy is used to aggregate both features across the frequency and time domains and then fed into the Transformer encoder-decoder module. By using the proposed multi-level acoustic feature extraction framework, state-of-the-art word error rate of 21.7% and 2.5% were obtained on the HKUST Mandarin telephone and Librispeech speech recognition tasks respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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EEG Feature in Amputee with Five Projected Fingers: A Comparison Study

EEG Feature in Amputee with Five Projected Fingers: A Compar...

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IEEE International Conference on Robotics and Biomimetics

作者： Wenyuan Liang Changyu Qin Yuanzhe Dong Xi Tang Guanglin Li Peng Fang Sheng Bi Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability National Research Center for Rehabilitation Technical Aids Beijing China Beijing Machine and Equipment Institute Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9781665481106

The somatosensory ERP provides a tool to understand the tactile sensation mechanism for the projected finger. A total of four subjects (1 amputee & 3 able-bodied) were recruited. The tactile sensation is evoked via TENS. Through applying the stimulation electrodes on the target fingers, simultaneously the EEG data was recorded. Two major findings are concluded as follows. First, the somatosensory ERP of the early-stage in three groups are similar, and then it could infer that the projected finger may have a similar delivery pathway as the natural finger. Second, since three groups show similar cortical processing characteristics, it could infer that the projected finger evoke similar somatosensory neuro information as the natural finger. This tactile sensation mechanism study shows the uniformity between the natural finger and the natural finger.

关键词： Somatosensory Electrodes Biomimetics Fingers Robot sensing systems Electroencephalography

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AGA-RFNN: Adaptive Genetic Algorithm-based Random Forest Neural Network for Pavement Deterioration Prediction

AGA-RFNN: Adaptive Genetic Algorithm-based Random Forest Neu...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Zhenyu Xu Lenian Meng Qieshi Zhang Jun Cheng Babitha Philip Hamad AlJassmi Zhiyong Yang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Economics and Management China University of Geosciences Wuhan Wuhan China Department of Civil and Environmental Engineering United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Yantai Institute of Technology Shandong China

Pavement deterioration prediction is an important task of road inspection mobile robots. Tree-based deep learning models perform well in this prediction but their accuracy remains unsatisfactory. In this paper, to enhance the prediction accuracy of pavement deterioration, we propose a SELECTOR module and Adaptive Genetic Algorithm-based Random Forest Neural Networks (AGA-RFNNs). The SELECTOR module is first used to refine the input data by exploiting correlation analysis, then the RFNNs are designed to make superior accurate and general predictions by learning predictions from random forests, and finally the AGA is applied to train the RFNNs, which considers both the average and maximum fitness of the population and thereby obtaining the optimal solution. Integrating AGA and RFNN, the proposed AGA-RFNN enhances the accuracy of pavement deterioration prediction systems. Experimental results demonstrate that AGA-RFNN outperforms existing models on four deterioration datasets related to cracking, deflection, international roughness index and rutting, respectively.

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Development of Automated Neonatal Sleep-Wake Cycle Intelligent Detection Algorithm

Development of Automated Neonatal Sleep-Wake Cycle Intellige...

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International Conference on Sensing Technology, ICST

作者： Jiaxi Wu Xin Wang Ke Zhang Weimin Huang Lina Men Shixiong Chen School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Department of Otolaryngology Shenzhen Children's Hospital Shenzhen China Department of Neonatology Shenzhen Children's Hospital Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

While there are various algorithms for automatic identification of sleep-wake cycles based on electroencephalography (EEG), there is a notable lack of approaches utilizing amplitude-integrated electroencephalography (aEEG). As EEG is increasingly replaced by aEEG in many contexts due to its large data size and challenges in information capture, it is essential to advance sleep-wake cycle (SWC) detection algorithms based on aEEG. Previous research primarily established a threshold to differentiate between quiet sleep (QS) and non-QS, but this required manual adjustment of individual thresholds for each patient's data, leading to considerable inconvenience. Facing this challenge, the study developed an algorithm for the automatic identification of boundaries between QS and non-QS segments in the dataset. Each entry can have its own QS/non-QS boundary, eliminating the need for manual setting. The algorithm was developed under EEG recordings from seven neonates with post-menstrual ages ranging from 32 to 41 weeks. First, EEG data was converted into aEEG and subjected to smoothing. The upper and lower boundaries of the smoothed signal were utilized as the foundation for boundary assessment. The stable segment selection method was then applied to identify wide segments with limited variability, which were classified as QS periods. The analysis reveals that the detection algorithm performed well for extended QS periods (lasting over 15 minutes). In contrast, detection for shorter QS periods (less than 3 minutes) presented variability, with both false positives and false negatives observed. However, with proper adjustments, this method can effectively assist physicians in identifying suspicious QS phases and directly reviewing the corresponding video for validation.

关键词： Pediatrics Smoothing methods Sleep Manuals Medical services Electroencephalography Sensors Recording Detection algorithms Monitoring

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Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel

Unsupervised Cross-Lingual Speech Emotion Recognition Using ...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Jin Li Nan Yan Lan Wang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781665437400

Speech Emotion Recognition (SER) in a single language has achieved remarkable results through deep learning approaches in the last decade. However, cross-lingual SER remains a challenge in real-world applications due to a great difference between the source and target domain distributions. To address this issue, we propose an unsupervised cross-lingual Neural Network with Pseudo Multilabel (NNPM) that is trained to learn the emotion similarities between source domain features inside an external memory adjusted to identify emotion in cross-lingual databases. NNPM introduces a novel approach that leverages external memory to store source domain features and generates pseudo multilabel for each target domain data by computing the similarities between the external memory and the target domain features. We evaluate our approach on multiple different languages of speech emotion databases. Experimental results show our proposed approach significantly improves the weighted accuracy (WA) across multiple low-resource languages on Urdu, Skropus, ShEMO, and EMO-DB corpus. To facilitate further research, code is available at https://***/happyjin/NNPM

关键词： Deep learning Emotion recognition Codes Databases Speech coding Conferences Artificial neural networks

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Development and evaluation of a hand exoskeleton for finger rehabilitation*

Development and evaluation of a hand exoskeleton for finger ...

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IEEE International Conference on Robotics and Biomimetics

作者： Haiyi Luo Zhenyu Sun Xiaobei Jing Bairui Shu Shixiong Chen Xu Yong Hiroshi Yokoi Department of Mechanical Engineering and Intelligent Systems The University of Electro-Communications Chofu Tokyo Japan CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China Center for Neuroscience and Biomedical Engineering The University of Electro-communications Chofu Tokyo Japan

ISBN: (纸本)9781665481106

Exoskeleton robots are now prevalent in hand rehabilitation medical training, and they can effectively drive a variety of rehabilitative movements in a hand that has lost its motor ability. To adapt to the hand's physiological structure and motion characteristics, a hybrid-driven exoskeleton hand based on tendon rope and linkage and its validation experiments are proposed in this paper. The exoskeleton hand can assist one to five fingers independently or even assist a joint alone. Wearing the robot retains the physiological touch of the hand to the maximum extent, which is beneficial to rehabilitation. In addition, patients can also carry out rehabilitation training independently, and the control mode is simple and practical. To verify whether the exoskeleton can reach the grip standard of healthy hands, the Leap Motion Controller is also used to conduct experimental verification of finger movement wearing the exoskeleton. The results show that the maximum average differences between the angles of the finger flexion motion joints (MCP and PIP) with and without the exoskeleton are 10.33 degrees and 11.06 degrees. It was verified that the exoskeleton could meet the requirements of finger flexion and extension for assisted motion within a specific error range.

关键词： Training Couplings Exoskeletons Thumb Bending Physiology Main-secondary

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