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EEG-Based Motion Intention Detection for Robotic Rehabilitation: Evaluating Classification and Regression Algorithms

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SN computer science 2024年第8期5卷 1-14页

作者： Khan, Abdul Manan Khan, Fatima Dwivedi, Anshu Kumar Semwal, Vijay Bhaskar Martha, Sheshikala Bijalwan, Vishwanath Department of Aerospace Engineering University of Bristol Bristol United Kingdom Graduate School of Cancer Science and Policy National Cancer Center Ilsan South Korea School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Maulana Azad National Institute of Technology Bhopal India School of Computer Science & amp Artificial Intelligence SR University Warangal Warangal India Department of Electrical and Electronics Engineering University of West London London United Kingdom

Rehabilitation robots have shown significant efficacy in restoring lost motor functions in stroke survivors by providing compliant assistance during rehabilitation tasks. However, accurately understanding the desired motion intention (DMI) of the subjects is a significant challenge in providing effective robotic assistance. This work aims to determine the desired motion intention (DMI) for compliant robotic assistance through the analysis of upper limb motions using electroencephalography (EEG) signals. A group of fifteen individuals who were in good health carried out six distinct exercises. These movements were categorized using decision trees, random forest, and deep learning algorithms. Using deep learning, we achieved the highest accuracy rates, reaching 82% for movement-to-movement classification and 90% for movement-to-rest classification. The random forest method achieved superior performance compared to the other algorithms in regression tests, with an accuracy rate of 83%. The results demonstrate the effectiveness of our proposed EEG-based classification and regression techniques in detecting DMI for robotic help in *** study was conducted with a small sample size, exclusively involving healthy subjects, which is a critical factor to consider. This may limit the generalizability of the results, particularly when populations with motor impairments are taken into account. However, the study offers valuable insights into the potential of the EEG-based models for real-time rehabilitation applications despite this limitation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： C-RNN Deep learning Desired motion intention EEG signals Machine learning Rehabilitation

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Support Vector Machine Classification Model Based on Grey Wolf Optimizer 7

Support Vector Machine Classification Model Based on Grey Wo...

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7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Han, Xinxin Wang, Jibin Zheng, Kaiyuan He, Dingkun Lu, Dandan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Chang Chien College Nantong University Nantong China School of Computer Science Beijing Information Science & Technology University Beijing China School of National Defense Science Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

This paper proposes a support vector machine (SVM) classification model based on the grey wolf optimizer (GWO) to solve high-dimensional nonlinear classification problems in industrial production and daily life. To address the sensitivity of traditional SVM to parameter settings, this study utilizes the global search capability and fast convergence characteristics of the grey wolf optimizer to optimize the key parameters of SVM, including the penalty parameter C and the width parameter g of the radial basis function (RBF). By using the best accuracy rate achieved through five-fold cross-validation as the target function, the precise adjustment of SVM parameters was realized. Experiments using the Italian wine dataset were conducted for verification, and the results showed that the GWO-SVM model achieved a 100% accuracy rate on the test set, proving that the model has high classification accuracy and good generalization ability in handling complex classification problems. ©2024 IEEE.

关键词： Radial basis function networks

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Innovation of the Higher Education Grassroots Statistical Reports System based on Low-Code Development 7

Innovation of the Higher Education Grassroots Statistical Re...

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7th International Conference on Management engineering, Software engineering and Service sciences, ICMSS 2023

作者： Luo, Jinshang Hou, Mengshu Information Center University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665456067

The grassroots statistical reporting of higher education is not only a critical effort for the advancement of national education, but it also serves as a prominent foundation for colleges and universities in formulating development strategies. Due to numerous statistical objects, extensive fields, and complex contents, the problems of difficult data maintenance and low filling efficiency caused by traditional manual means are prone to occur. To address these issues, the low-code software development method is utilized to design and implement a higher education grassroots statistical reports system. Initially, the characteristics of low-code development are leveraged to achieve code reusability and simplify the development process. Moreover, the method emphasizes data verification to improve data quality. To handle reported data, an automatic filling flow is eventually constructed. The system's deployment optimizes data reporting, reduces the intensity of statistical work, strengthens data verification, enhances the quality of data reporting, and effectively supports statistical reports. © 2023 IEEE.

关键词： Software design

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Transferring fashion to surveillance with weak labels

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Neural Computing and Applications 2023年第18期35卷 13021-13035页

作者： Zheng, Qi He, Zheng Liang, Chao Chen, Jun Lin, Chia-Wen Tao, Dapeng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan School of Information Science and Engineering Yunnan University Yunnan Kunming650504 China

In this paper, we address the problem of automatic clothing parsing in surveillance images using the information from user-generated tags, such as "jeans" and "T-shirt." Although clothing parsing has achieved great success in the fashion domain, it is quite challenging to parse target under practical surveillance conditions due to the presence of complex environmental interference, such as that from low resolution, viewpoint variations and lighting changes. Our method is developed to capture target information from the fashion domain and apply this information to a surveillance domain by weakly supervised transfer learning. Most target tags convey strong location information (e.g., "T-shirt" is always shown in the upper region), which can be used as weak labels for our transfer method. Both quantitative and qualitative experiments conducted on practical surveillance datasets demonstrate the effectiveness of the proposed surveillance data enhancing method. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Monitoring

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Hierarchical Attention Decoder for Solving Math Word Problems 4th

Hierarchical Attention Decoder for Solving Math Word Proble...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Li, Jiajia Peng, Letian Wang, Ping Zhao, Hai School of Information Management Wuhan University Wuhan China Center for the Studies of Information Resources Wuhan University Wuhan China Department of Computer Science and Engineering University of California San Diego United States Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819787012

To answer math word problems (MWPs), models must formalize equations from the source text of math problems. Recently, the tree-structured decoder has significantly improved model performance on this task by generating the target equation in a tree format. However, current decoders usually ignore the hierarchical relationships between tree nodes and their parents, which hinders further improvement. Thus, we propose a structure called a hierarchical attention tree to aid the generation procedure of the decoder. As our decoder follows a graph-based encoder, our full model is therefore named Graph to Hierarchical Attention Tree (G2HAT). We show that a tree-structured decoder with hierarchical accumulative multi-head attention leads to a significant performance improvement and reaches a significant improvement on various strong baselines on both English MAWPS and Chinese Math23k MWP benchmarks. For further study, we also apply a pre-trained language model to G2HAT, which even results in a new higher performance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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Robust Malicious Domain Detection Based on Spatio-Temporal Hypergraph Networks 22

Robust Malicious Domain Detection Based on Spatio-Temporal H...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Gong, Liangyi Lv, Kunxian Long, Chun Fu, Hao Wang, Huanran Wan, Wei Yang, Wu Chinese Academy of Sciences Computer Network Information Center Beijing China University of Chinese Academy of Sciences Beijing China Harbin Engineering University School of Computer Science and Technology Heilongjiang China

ISBN: (纸本)9798331509712

Malicious domains serve as significant resources for adversaries to execute cyber attacks and are crucial indicators for detecting network intrusions. In practical scenarios, malicious domains associated with various attacks are intermingled within DNS traffic, leading to variability in the performance of machine learning-based detection methods. To address this challenge, we have collected extensive DNS traffic data spanning 12 months from a real-world large-scale network with 1 million users. From this dataset, we have extracted numerous requested domains, encompassing 267 attacks that exploit malicious domain names. Furthermore, we have observed that the distinct properties of malicious domains associated with different attacks contribute to the fluctuating performance of machine learning-based detection models. Consequently, we have introduced a spatiotemporal hypergraph network model, which establishes high-order relationships among domain properties to enhance the generalization capability and robustness of the detection model. The results of extensive testing experiments demonstrate that our model achieves remarkable performance, with an average precision of 97% and recall of 98%. © 2024 IEEE.

关键词： Network intrusion

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PZT-based Multi-Mode Cantilever for Viscosity Sensing

PZT-based Multi-Mode Cantilever for Viscosity Sensing

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2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Javed, N. Singh, Priyanka Tiwari, Sudhanshu Jain, Pavitra Sandeep, Kongbrailatpam Pratap, Rudra Pillai, Gayathri Indian Institute of Science Center for Nanoscience and Engineering Bengaluru India Purdue University The Elmore Family School of Electrical and Computer Engineering United States

ISBN: (纸本)9798350311426

We report a thin film Lead Zirconate Titanate (PZT) on Silicon-on-Insulator (SOI) based microcantilever for fluid dynamic viscosity (ηfluid) sensing using optical and electrical characterization schemes. The viscosity of the medium is modulated by adding glycerol to deionized (DI) water. The measurements are carried over a viscosity range of 0.98-24.24 cP, which covers the operational range for biomedical applications. The fundamental cantilever mode yields a quality factor (Q) of 630 and 15 in air and DI water, respectively, whilst the second mode exhibits a Q of 840 and 30 in air and DI water, respectively. Sensor characterization using Laser Doppler Vibrometer (LDV) shows a power law relation between Q and ηfluid (Q = aηfluidb). A high relative responsivity of 0.38 and 0.41 with respect to ηfluid is observed for the first and second flexural modes of the microcantilever, respectively. These results indicate the development of a good platform for on-chip viscosity measurements for biomedical and industrial applications. © 2023 IEEE.

关键词： MEMS Microcantilever Modes Sensor Viscosity

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RPRB-Net: A Sign Language Recognition Based on Multi-dimension Attentions

RPRB-Net: A Sign Language Recognition Based on Multi-dimensi...

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2023 International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Yang, Tian Shen, Cong Wang, Xinyue Ma, Xiaoyu Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350306545

Sign Language Recognition (SLR) is the process of translating sign language into text or speech, which can also be viewed as a communication and expression tool between deaf individuals and hearing people. SLR using deep neural network has undergone significant development over the last decade. In this study, we design a feature-enhanced SLR technique based on serval spaces. To aid the network learning features from channel and spatial dimensions, we inject various attention mechanisms into the feature extractor backbone. As a result, we named our approach ResNet + Pyramid-Split-Attention + Recurrent-Criss-Cross-Attention + BiLSTM Network (RPRB-Net). Besides, we employ the BiLSTM architecture to encode high-level representations in temporal sequence. The network is ultimately trained using an end-to-end CTC alignment strategy. Extensive trials show that RPRB-Net achieves WERs of 3.5% and 23.6% on the USTC and RWTH-2014 datasets, respectively. Besides, it implies that RPRB-Net significantly surpasses the cutting-edge SLR approaches. Additionally, numerous ablation experiments have confirmed the efficiency of configuration. © 2023 IEEE.

关键词： Audition

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AoI Minimization in UAV-Assisted IoT Network: A Reinforcement Learning Approach

AoI Minimization in UAV-Assisted IoT Network: A Reinforcemen...

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2023 International Conference on Ubiquitous Communication, Ucom 2023

作者： Li, Xulong Xu, Xinyi Huo, Jiahao Huangfu, Wei School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services Beijing China

ISBN: (纸本)9798350340433

Unmanned aerial vehicles (UAVs) are often used to assist internet of things (IoT) networks to collect data from IoT devices more securely and efficiently by virtue of their flexibility and low deployment cost. In this paper, we consider a UAV-assisted IoT network in which a large number of IoT devices are deployed along with eavesdroppers. The communication UAV and the jamming UAV cooperating to collect data from IoT devices. In order to minimize the age of information (AoI) of data from IoT devices at the communication UAV, this paper introduces reinforcement learning (RL) solutions. Considering the number of IoT devices is not fixed in the coverage area of communication UAV, which incurs the dimensional variations for the observation and action space, so we propose an effective twin delayed deep deterministic policy gradient (TD3) algorithm based on attention mechanisms to design the policy and critic networks. Simulation results show that the proposed algorithm is able to significantly reduce the average AoI of data from IoT devices at the communication UAV compared to baseline algorithms. © 2023 IEEE.

关键词： Reinforcement learning

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhou, Hang Liu, Haichao Lu, Hongliang Ma, Jun Ji, Yiding Robotics and Autonomous Systems Thrust Systems Hub Guangzhou China School of Engineering The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong Intelligent Transportation Thrust Systems Hub Guangzhou China

ISBN: (纸本)9781665481090

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark. © 2024 IEEE.

关键词： Deep neural networks

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