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Construction of Human Digital Twin Model Based on multimodal data and Its Application in Locomotion Mode Identifcation

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Chinese Journal of Mechanical Engineering 2023年第5期36卷 7-19页

作者： Ruirui Zhong Bingtao Hu Yixiong Feng Hao Zheng Zhaoxi Hong Shanhe Lou Jianrong Tan State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Hangzhou 310027China Hangzhou Innovation Institute Beihang UniversityHangzhou 310052China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 637460Singapore

With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efcient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difcult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identifcation is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identifcation models. The experimental results proved the superiority of the HDT framework for human locomotion mode identifcation.

关键词： Human digital twin Human-cyber-physical system Bidirectional long short-term memory Convolutional neural network multimodal data

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Analysis of Emotions Using multimodal data: A Case Study

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Journal of Biosciences and Medicines 2023年第12期11卷 54-68页

作者： Toshiya Akiyama Kyoko Osaka Hirokazu Ito Ryuichi Tanioka Allan Paulo Blaquera Leah Anne Christine Bollos Tetsuya Tanioka Graduate School of Health Sciences Tokushima University Tokushima Japan Department of Psychiatric Nursing Nursing Course of Kochi Medical School Kochi University Kochi Japan Department of Nursing Art and Sciences Tokushima University Tokushima Japan Department of Physical Therapy Hiroshima Cosmopolitan University Hiroshima Japan Department of Nursing Outcomes Management Institute of Biomedical Sciences Tokushima University Tokushima Japan

In this case study, we hypothesized that sympathetic nerve activity would be higher during conversation with PALRO robot, and that conversation would result in an increase in cerebral blood flow near the Broca’s area. The facial expressions of a human subject were recorded, and cerebral blood flow and heart rate variability were measured during interactions with the humanoid robot. These multimodal data were time-synchronized to quantitatively verify the change from the resting baseline by testing facial expression analysis, cerebral blood flow, and heart rate variability. In conclusion, this subject indicated that sympathetic nervous activity was dominant, suggesting that the subject may have enjoyed and been excited while talking to the robot (normalized High Frequency < normalized Low Frequency: 0.22 ± 0.16 < 0.78 ± 0.16). Cerebral blood flow values were higher during conversation and in the resting state after the experiment than in the resting state before the experiment. Talking increased cerebral blood flow in the frontal region. As the subject was left-handed, it was confirmed that the right side of the brain, where the Broca’s area is located, was particularly activated (Left < right: 0.15 ± 0.21 < 1.25 ± 0.17). In the sections where a “happy” facial emotion was recognized, the examiner-judged “happy” faces and the MTCNN “happy” results were also generally consistent.

关键词： Humanoid Robots multimodal data Emotion Analysis

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Early warning of emerging infectious diseases based on multimodal data

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Biosafety and Health 2023年第4期5卷 193-203页

作者： Haotian Ren Yunchao Ling Ruifang Cao Zhen Wang Yixue Li Tao Huang Bio-Med Big data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Guangzhou Laboratory Guangzhou 510005China School of Life Sciences and Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Collaborative Innovation Center for Genetics and Development Fudan UniversityShanghai 200433China

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

关键词： Emerging infectious disease SARS-CoV-2 multimodal data Early warning

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Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

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Frontiers of Environmental Science & Engineering 2024年第3期18卷 69-82页

作者： Junchen Li Sijie Lin Liang Zhang Yuheng Liu Yongzhen Peng Qing Hu School of Environment Harbin Institute of TechnologyHarbin 150090China School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Engineering Innovation Center of SUSTech(Beijing) Southern University of Science and TechnologyBeijing 100083China Faculty of Environment and Life Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of EducationBeijing 100124China

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment *** this study,we aimed to develop an integrated method for predicting effluent COD and NH3 *** employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 *** we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 *** we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this *** innovative model synergized sewage imagery with water quality data,enhancing prediction *** a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor ***,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.

关键词： Wastewater treatment system Water quality prediction data driven analysis Brain-inspired model multimodal data Attention mechanism

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Multi-level Deep Correlative Networks for Multi-modal Sentiment Analysis

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Chinese Journal of Electronics 2020年第6期29卷 1025-1038页

作者： CAI Guoyong LYU Guangrui LIN Yuming WEN Yimin School of Computer Science and Information Security Guilin University of Electronic Technology School of Information Science and Technology Dalian Maritime University

Multi-modal sentiment analysis(MSA)is increasingly becoming a hotspot because it extends the conventional Sentiment analysis(SA) based on texts to multi-modal content which can provide richer affective ***,compared with textbased sentiment analysis,multi-modal sentiment analysis has much more challenges,because the joint learning process on multi-modal data requires both fine-grained semantic matching and effective heterogeneous feature *** approaches generally infer sentiment type from splicing features extracted from different modalities but neglect the strong semantic correlation among cooccurrence data of different *** solve the challenges,a multi-level deep correlative network for multimodal sentiment analysis is proposed,which can reduce the semantic gap by analyzing simultaneously the middlelevel semantic features of images and the hierarchical deep ***,the most relevant cross-modal feature representation is generated with Multi-modal Deep and discriminative correlation analysis(Multi-DDCA) while keeping those respective modal feature representations to be ***,the high-level semantic outputs from multi-modal deep and discriminative correlation analysis are encoded into attention-correlation cross-modal feature representation through a co-attention-based multimodal correlation submodel,and then they are further merged by multi-layer neural network to train a sentiment classifier for predicting sentimental *** experimental results on five datasets demonstrate the effectiveness of the designed approach,which outperforms several state-of-the-art fusion strategies for sentiment analysis.

关键词： conventional Sentiment analysis discriminative correlation analysis multimodal Deep Image recognition text-based sentiment analysis multilayer neural network Document processing techniques Multilevel Deep correlative networks text analysis data handling techniques image fusion multimodal content Optical, image and video signal processing multilevel deep correlative network Computer vision and image processing techniques feature extraction respective modal feature representations relevant cross-modal feature representation attention-correlation cross-modal feature representation multimodal data image representation multimodal sentiment analysis learning (artificial intelligence) pattern classification Knowledge engineering techniques

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O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

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Journal of Traffic and Transportation Engineering(English Edition) 2024年第4期11卷 721-732页

作者： Damola M.Akinlana Arindam Fadikar Stefan M.Wild Natalia Zuniga-Garcia Joshua Auld Department of Mathematics and Statistics University of South FloridaTampaFL 33620USA Decision and Infrastructure Sciences Division Argonne National LaboratoryLemontIL 60439USA Applied Mathematics and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Department of Industrial Engineering and Management Sciences Northwestern UniversityEvanstonIL 60208USA Argonne National Laboratory Energy SystemsLemontIL 60439USA

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport ***,it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road *** to its good adaptability and flexibility for spatiotemporal data,the Gaussian process(GP)regression was employed to provide short-term forecasts using data collected by loop detectors(sensors)and supplemented by telematics *** GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the ***,the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training *** in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

关键词： Spatio-temporal data fusion Gaussian process multimodal data Airport traffic O'Hare Airport

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CRGMR: A Contextualized RGAT and GraphTransformer Method for multimodal Emotion Recognition

CRGMR: A Contextualized RGAT and GraphTransformer Method for...

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第43届中国控制会议

作者： Guoshun Chen Shuai Liu Xiaoxia Bi Qiuxian Chen Ting Meng School of Computer Science Xi'an University of Posts and Telecommunications

The recognition of emotions in dialogue is crucial for building empathetic *** research primarily focuses on learning emotion-related common features in multimodal data,neglecting the complex dependency relationships of emotional features within dialogues(local and global information).To address this issue,we propose A Contextualized RGAT and GraphTransformer Method for multimodal Emotion Recognition(CRGMR) that leverages both local information(dependencies between speakers) and global information(context of the dialogue) in the *** model utilizes Transformer to capture cross-modal global information,constructing a heterogeneous graph with extracted global ***,it employs RGAT and GraphTransformer to model both global and local information within the heterogeneous *** experiments demonstrate the effectiveness of CRGMR via its state-of-the-art results on the IEMOCAP and MELD datasets for the multimodal ERC task.

关键词： multimodal data Emotion recognition Deep learning Attention mechanism

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人工智能辅助的自闭症早期患者的筛查与诊断

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心理学 2023年第2期

作者：袁玉琢骆方

自闭症谱系障碍(Autistic Spectrum Disorders，ASD)的症状早在婴幼儿期就会显现，越早发现，越早干预，治疗效果越好。传统自闭症早期筛查与诊断在评估方法、流程上存在局限，无法满足大规模筛查和诊断需求。随着人工智能技术的快速发... 详细信息

自闭症谱系障碍(Autistic Spectrum Disorders，ASD)的症状早在婴幼儿期就会显现，越早发现，越早干预，治疗效果越好。传统自闭症早期筛查与诊断在评估方法、流程上存在局限，无法满足大规模筛查和诊断需求。随着人工智能技术的快速发展，使用智能化方法进行自闭症早期大规模无感筛查与诊断逐渐成为可能。近10年间，国内外对自闭症智能化识别方法的探索在经典任务行为、面部表情和情绪、眼动、脑影像、运动控制和运动模式、多模态6个领域积累了丰富的研究成果。未来研究应围绕构建国内自闭症早期智能医学筛查与诊断体系，开发针对婴幼儿患者的筛查工具，构建融合多模态数据的自闭症婴幼儿智能化识别模型，建立结合脑影像技术的自闭症精细化诊断方法等方面来开展。

关键词：自闭症谱系障碍自闭症早期筛查与诊断自闭症智能化识别人工智能多模态数据 autism spectrum disorder early screening and diagnosis of autism intelligent screening of autism artificial intelligence multimodal data

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