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International Workshop on Advanced Imaging Technology (IWAIT)

作者： Priyadarshani, Smita Watanabe, Hiroshi Waseda Univ Fac Fundamental Sci & Engn Dept Comp Sci & Commun Engn Shillman Hall 4013-14-9 OkuboShinjuku Ku Tokyo 1690072 Japan

ISBN: (数字)9781510679931

ISBN: (纸本)9781510679931;9781510679924

Physical rehabilitation exercises are crucial in the post-operative recovery and treatment of various musculoskeletal conditions. An automated vision-based rehabilitation exercise assessment is a portable and cost-effective way of evaluating the patients' performance in a home-based setting and predicting a performance score by analyzing the correct and incorrect exercise sequences performed by the patient. Recent works have shown that exploring spatial and temporal features of the skeleton data is vital for this task. However, most of the methods treat all body joints equally and fail to capture the correlation information between all joints. Hence, to address this limitation, we have proposed an attention-enhanced spatial-temporal graph convolutional network that captures the spatial and temporal dependencies among the body joints incorporating an attention mechanism to learn global information for the joint-specific roles to provide better assessment results.

关键词： Physical rehabilitation exercise assessment spatial-temporal graph convolutional network non-local attention

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Multi-Source Information Fusion graph Convolution network for traffic flow prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期252卷

作者： Li, Qin Xu, Pai He, Deqiang Wu, Yuankai Tan, Huachun Yang, Xuan Guangxi Univ Sch Mech Engn 100 East Daxue Rd Nanning 530004 Peoples R China Sichuan Univ Coll Comp Sci 29 Jiuyanqiao Wangjiang Rd Chengdu 610064 Sichuan Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci 5 South Zhongguancun St Beijing 100081 Peoples R China

As a fundamental technology in the field of intelligent transportation systems, traffic flow prediction has a wide range of applications. The utilization of graph convolutional network (GCN) models is notable for capturing the complex spatial-temporal dependencies in traffic data, leading to a significant improvement in prediction accuracy. However, most existing graph construction methods overlook joint impact of auxiliary information such as weather and traffic speed on the road topology. Moreover, the research on interactions within time series at coarse temporal resolutions remains insufficiently explored, giving rise to unsatisfactory longterm prediction performance. In this study, we present a novel framework, namely Multi -Source Information Fusion graph Convolution network (MIFGCN), for spatial-temporal traffic flow prediction. Our key innovation lies in creating a dynamic graph that integrates weather, traffic speed, and global spatial information, effectively simulating significant traffic fluctuations caused by subtle ancillary information in the road network. Simultaneously, it captures the evolving hidden adjacency relationships between nodes over time. Furthermore, by combining with an attention -based temporal interaction module, MIFGCN learns multiscale temporal correlations at course temporal resolutions, enhancing the ability for long-term prediction. Experiments conducted on four real -world traffic datasets demonstrate that MIFGCN outperforms various state-of-the-art baselines, especially achieving a 10.50% average improvement on the PeMS08 dataset.

关键词： Traffic flow prediction spatial-temporal graph convolutional network Multi-source information Dynamic graph

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Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 252卷

作者： Wang, Junyan Wang, Naiyu Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Peoples R China

Rapid prediction of Road network Functionality (RNF) during extreme rainfall-induced flooding is crucial for supporting proactive and real-time emergency planning, such as rescue, evacuation planning, and emergency supply distribution. Unlike normal operational conditions, extreme rainfall events introduce complex nonstationary, non-Euclidean characteristics to RNF due to intricate meteorological and hydrological processes, as well as the role of a community's road network in emergency response planning. Conventional physics-based flood simulations and flow-based road network analyses typically lack the computational efficiency required for real-time RNF predictions, hindering timely risk mitigation decisions. This study leverages the accuracy of physics-based simulations and the efficacy of deep-learning technologies to develop a deep learning-based surrogate model for Rain-to-RNF (R2R) predictions. This model couples Long Short-Term Memory (LSTM) networks with spatial-temporal graph convolutional networks (ST-GCNs) to uniquely capture the spatiotemporal dynamics of RNF under extreme rainfall events. The predictive accuracy, stability, and versatility of the R2R surrogate model are demonstrated in four flood-prone communities in Zhejiang Province. Its implementation during Typhoon Fitow (2013) over a 30-hour intense rainfall showcases its promising predictive capacity and unparalleled computational efficiency. This research advances disaster management, enhancing the resilience and responsiveness of community infrastructure during extreme weather events.

关键词： Deep learning Extreme rainfall Road network functionality Rapid prediction Real-time forecast spatial-temporal graph convolutional network (ST-GCN) Long Short-Term Memory (LSTM) network

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Video-Based Quantification of Gait Impairments in Parkinson's Disease Using Skeleton-Silhouette Fusion Convolution network

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 2912-2922页

作者： Zeng, Qingyi Liu, Peipei Yu, Ningbo Wu, Jialing Huo, Weiguang Han, Jianda Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Tianjin Huanhu Hosp Dept Neurol Tianjin 300350 Peoples R China Tianjin Neurosurg Inst Tianjin Key Lab Cerebral Vasc & Neurodegenerat Dis Tianjin 300350 Peoples R China ShenzhenResearch Inst Nankai Univ Inst Intelligence Technol & Robot Syst Shenzhen 518083 Peoples R China

Gait impairments are among the most common hallmarks of Parkinson's disease (PD), usually appearing in the early stage and becoming a major cause of disability with disease progression. Accurate assessment of gait features is critical to personalized rehabilitation for patients with PD, yet difficult to be routinely carried out as clinical diagnosis using rating scales relies heavily on clinical experience. Moreover, the popular rating scales cannot ensure fine quantification of gait impairments for patients with mild symptoms. Developing quantitative assessment methods that can be used in natural and home-based environments is highly demanded. In this study, we address the challenges by developing an automated video-based Parkinsonian gait assessment method using a novel skeleton-silhouette fusion convolution network. In addition, seven network-derived supplementary features, including critical aspects of gait impairment (gait velocity, arm swing, etc.), are extracted to provide continuous measures enhancing low-resolution clinical rating scales. Evaluation experiments were conducted on a dataset collected with 54 patients with early PD and 26 healthy controls. The results show that the proposed method accurately predicted the patients' unified Parkinson's disease rating scale (UPDRS) gait scores (71.25% match on clinical assessment) and discriminated between PD patients and healthy subjects with a sensitivity of 92.6%. Moreover, three proposed supplementary features (i.e., arm swing amplitude, gait velocity, and neck forward bending angle) turned out to be effective gait dysfunction indicators with Spearman correlation coefficients of 0.78, 0.73, and 0.43 matching the rating scores, respectively. Since the proposed system requires only two smartphones, it holds a significant benefit for home-based quantitative assessment of PD, especially for detecting early-stage PD. Furthermore, the proposed supplementary features can enable high-resolution assessments of PD for

关键词： Feature extraction Diseases Skeleton Smart phones Convolution Neck Legged locomotion Parkinson's disease gait impairments video-based assessment spatial-temporal graph convolutional network

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spatial-temporal graphs Plus Transformers for Geometry-Guided Facial Expression Recognition

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2023年第4期14卷 2751-2767页

作者： Zhao, Rui Liu, Tianshan Huang, Zixun Lun, Daniel P. K. Lam, Kin-Man Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

Facial expression recognition (FER) is of great interest to the current studies of human-computer interaction. In this paper, we propose a novel geometry-guided facial expression recognition framework, based on graph convolutional networks and transformers, to perform effective emotion recognition from videos. Specifically, we detect and utilize facial landmarks to construct a spatial-temporal graph, based on both the landmark coordinates and local appearance, for representing a facial expression sequence. The graph convolutional blocks and transformer modules are employed to produce high-semantic emotion-related representations from the structured facial graphs, which facilitate the framework to establish both the local and non-local dependency between the vertices. Moreover, spatial and temporal attention mechanisms are introduced into graph-based learning to promote FER reasoning, via the emphasis on the most informative facial components and frames. Extensive experiments demonstrate that the proposed framework achieves promising performance for geometry-based FER and shows great generalization and robustness in real-world applications.

关键词： Feature extraction Facial expression recognition spatial-temporal graph convolutional network spatial-temporal transformer attention mechanism

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Data-Driven Stochastic Game With Social Attributes for Peer-to-Peer Energy Sharing

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IEEE TRANSACTIONS ON SMART GRID 2021年第6期12卷 5158-5171页

作者： Chen, Liudong Liu, Nian Liu, Liangying Yu, Xinghuo Xue, Yusheng North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia State Grid Elect Power Res Inst Nanjing 210003 Peoples R China

Distributed energy resources create a prosumers era. Peer-to-peer (P2P) energy sharing is an effective way to conduct prosumers-based energy management. Integrating prosumers' social attributes in energy engineering substantially affects their decisions, which brings challenges to the energy scheduling under the cyber-physical-social environment. In this paper, a data-driven stochastic game model with prosumers' social attributes is proposed for P2P energy sharing. According to social psychology, the prosumers' social attributes are expressed as subjective probabilities, which are studied by the spatial-temporal graph convolutional networks. In the network, a double-layer feature graph is built to learn the social attributes based on the social survey data and load metering data. The P2P energy sharing incurred randomness comes from social attributes of interactive prosumers, which is formulated as a stochastic game model. In this game, a subjective utility model is proposed for prosumers, and the energy scheduling is conducted with the designed dynamic interval adjustment method. Numerical analysis reveals the results of the learning network and generalized Nash equilibrium. Through comparing with rational scenarios, the influence of prosumers' social attributes on P2P energy sharing is concluded.

关键词： Games Stochastic processes Load modeling Psychology Data models Power system dynamics Peer-to-peer computing Cyber-physical-social system spatial-temporal graph convolutional network social attributes P2P energy sharing stochastic games

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Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 2336-2345页

作者： Zhao, Peijun Alencastre-Miranda, Moises Shen, Zhan O'Neill, Ciaran Whiteman, David Gervas-Arruga, Javier Igo Krebs, Hermano MIT Dept Mech Engn Cambridge MA 02139 USA JPMorganChase Global Technol Appl Res New York NY 10017 USA Univ Michigan Robot Inst Ann Arbor MI 48109 USA Sofia Univ Dept Mech Engn Palo Alto CA 94303 USA Takeda Dev Ctr Amer Inc Lexington MA 02142 USA

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( kappa(lw)=0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.

关键词： Motors Measurement Feature extraction Estimation Pediatrics Legged locomotion Computer vision Cerebral Palsy gross motor function computer vision metric learning transfer learning spatial-temporal graph convolutional network

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Real-Time Safety Behavior Detection Technology of Indoors Power Personnel Based on Human Key Points

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Journal of Shanghai Jiaotong university(Science) 2024年第2期29卷 309-315页

作者：杨坚李聪敏洪道鉴卢东祁林秋佳方兴其喻谦张乾 State Grid Zhejiang Taizhou Power Supply Company Taizhou 318000ZhejiangChina Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Zhejiang Huayun Information Technology Co. Ltd.Hangzhou 310012China

Safety production is of great significance to the development of enterprises and *** often cause great losses because of the particularity environment of electric ***,it is important to improve the safety supervision and protection in the electric power *** this paper,we simulate the actual electric power operation scenario by monitoring equipment and propose a real-time detection method of illegal actions based on human body key points to ensure safety behavior in real *** this method,the human body key points in video frames were first extracted by the high-resolution network,and then classified in real time by spatial-temporal graph convolutional *** results show that this method can effectively detect illegal actions in the simulated scene.

关键词： real-time behavior recognition human key points high-resolution network spatial-temporal graph convolutional network

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Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition

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SENSORS 2021年第18期21卷 6309页

作者： Budisteanu, Elena-Alexandra Mocanu, Irina Georgiana Univ Politehn Bucuresti Comp Sci Dept RO-060042 Bucharest Romania

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.

关键词： human activity recognition skeleton spatial-temporal graph convolutional network clustering k-means Gaussian mixture model

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The analysis of regional ice and snow tourist destinations under back propagation neural network

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Heliyon 2024年第23期10卷 e40035页

作者： Wang, Fuxue Tourism and Culture School the Tourism College of Changchun University Changchun 130000 China Northeast Asia Research Center on Leisure Economics Changchun 130000 China Jilin Province Research Center for Cultural Tourism Education and Enterprise Development Changchun 130000 China Changchun Industry Convergence Research Center of Culture and Tourism Changchun 130000 China

This study aims to analyze the evolutionary characteristics and development levels of regional ice and snow tourist destinations by integrating the Back Propagation Neural network (BPNN) within an Internet of Things (IoT) framework. Data from multiple sources are gathered through web scraping technology from various online platforms and are then subjected to cleaning, standardization, and normalization. A feature recognition model for ice and snow tourism is constructed based on a BPNN combined with a spatial-temporal graph convolutional network (ST-GCN) algorithm. Experimental results demonstrate that this model excels in convergence speed and prediction accuracy, achieving a final convergence value of 0.059 and a prediction accuracy of 95.74 %, which is at least 4 % higher than that of the baseline BPNN algorithm. Additionally, the model yields Recall and F1 scores of 91.57 % and 89.31 %, respectively. After 98 iterations, the Root Mean Square Error (RMSE) is 6.26, significantly outperforming other model algorithms. These results indicate that the proposed model offers substantial advantages in enhancing the management and service quality of ice and snow tourist destinations, thereby providing valuable technical support and guidance for future intelligent tourism management. © 2024

关键词： Back propagation neural network Evolutionary characteristics Ice and snow tourism Internet of things spatial-temporal graph convolutional network

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