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2024 International Convention on Rehabilitation Engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Cao, Wujing Guo, Xinqiang Zou, Yupeng Zhang, Shuo Luo, Mingxiang Kobsiriphat, Worawarit Wu, Xinyu Yin, Meng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518005 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems China School of Mechanical Engineering China University of Petroleum China National Metal and Materials Technology Center Thailand

ISBN: (纸本)9798350355123

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance. © 2024 IEEE.

关键词： Convolutional neural networks

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DrivingDojo dataset: advancing interactive and knowledge-enriched driving world model 24

DrivingDojo dataset: advancing interactive and knowledge-enr...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yuqi Wang Ke Cheng Jiawei He Qitai Wang Hengchen Dai Yuntao Chen Fei Xia Zhaoxiang Zhang New Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Meituan Inc. Centre for Artificial Intelligence and Robotics HKISI CAS New Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Centre for Artificial Intelligence and Robotics HKISI CAS

ISBN: (纸本)9798331314385

Driving world models have gained increasing attention due to their ability to model complex physical dynamics. However, their superb modeling capability is yet to be fully unleashed due to the limited video diversity in current driving datasets. We introduce DrivingDojo, the first dataset tailor-made for training interactive world models with complex driving dynamics. Our dataset features video clips with a complete set of driving maneuvers, diverse multi-agent interplay, and rich open-world driving knowledge, laying a stepping stone for future world model development. We further define an action instruction following (AIF) benchmark for world models and demonstrate the superiority of the proposed dataset for generating action-controlled future predictions.

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Joint Optimization of Service Placement and Computation Offloading for Mobile Edge Computing

Joint Optimization of Service Placement and Computation Offl...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Huaizhe Wang, Zhizongkai Wu, Jiaqi Gao, Lin Harbin Institute of Technology The School of Electronics and Information Engineering The Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798350345384

Mobile Edge Computing (MEC) is emerging as a promising approach for enhancing the quality-of-service (QoS) of delay-sensitive applications in the B5G/6G era, via offloading certain computation tasks to the network edge that approximates to end-users. Existing researches on computation offloading in MEC mainly focused on the hardware resource constraint (e.g., CPU and storage) at the edge nodes, without considering the specific software service requirements of applications (e.g., runtime environment and operating system). In this work, we study the computation offloading for delay-sensitive applications under both constraints of hardware resources and software services, where each application can be offloaded to an edge node only if both the required hardware resources and software services have been deployed at that node. We formulate a Joint Service Placement and Computation Offloading (JSPCO) problem, aiming at minimizing the offloading delay cost and the service operation cost. The problem is challenging due to the inherent coupling between service placement and computation offloading. To solve the problem, we introduce several equivalent transformation methods that convert the original problem into a Mixed Integer Linear Programming (MILP) problem, which can be solved efficiently using classic algorithms. Simulation results show that our proposed joint optimization solution can reduce the total system cost, service operation cost, and UE delay cost by up to 63.06%, 62.90%, and 54.76%, respectively, compared to existing baseline solutions. © 2023 IEEE.

关键词： Computation offloading

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Your Learned Constraint is Secretly a Backward Reachable Tube

arXiv

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arXiv 2025年

作者： Qadri, Mohamad Swamy, Gokul Francis, Jonathan Kaess, Michael Bajcsy, Andrea Carnegie Mellon University Robotics Institute United States Bosch Center for Artificial Intelligence United States

Inverse Constraint Learning (ICL) is the problem of inferring constraints from safe (i.e., constraint-satisfying) demonstrations. The hope is that these inferred constraints can then be used downstream to search for safe policies for new tasks and, potentially, under different dynamics. Our paper explores the question of what mathematical entity ICL recovers. Somewhat surprisingly, we show that both in theory and in practice, ICL recovers the set of states where failure is inevitable, rather than the set of states where failure has already happened. In the language of safe control, this means we recover a backwards reachable tube (BRT) rather than a failure set. In contrast to the failure set, the BRT depends on the dynamics of the data collection system. We discuss the implications of the dynamics-conditionedness of the recovered constraint on both the sample-efficiency of policy search and the transferability of learned constraints. © 2025, CC BY.

关键词： Contrastive Learning

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Local-to-Global Perception Network for Point Cloud Segmentation

Local-to-Global Perception Network for Point Cloud Segmentat...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Haoxuan Wang Ping Wei Shuaijia Chen Zhimin Liao Jialu Qin National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

LiDAR-based point cloud segmentation is a significant and challenging task for 3D scene understanding. Recent voxel-based methods are often built on submanifold sparse residual calculation with small kernel size, which limits the local feature interactions and neglects the global contextual information. In this paper, we propose a local-to-global perception LiDAR-based point cloud segmentation network LGPSeg. From the local perspective, we design the Dynamic Spatial Aggregation Convolution to expand the receptive field range while avoiding a large increase in the model parameters. From the global perspective, we propose the BEV-Voxel Fusion to aggregate the global contextual information on the BEV feature maps through advanced 2D operators. By combining the local and global features, the 3D object and background information can be better captured. Our method achieves state-of-the-art results on two large datasets, SemanticKITTI and nuScenes, and even outperformed multimodal-based methods.

关键词： Point cloud compression Three-dimensional displays Convolution Semantic segmentation Aggregates Kernel

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Early Diabetes prediction with optimal feature selection using ML based Prediction Framework 4

Early Diabetes prediction with optimal feature selection usi...

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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Ashisha, G.R. Mary, X. Anitha Ashif, H. Mohamed Karthikeyan, I. Roshan, J. Karunya Institute of Technology and Sciences Electronics and Instrumentation Engineering Coimbatore India Karunya Institute of Technology and Sciences Robotics Engineering Coimbatore India Karunya Institute of Technology and Sciences Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300772

The notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. The use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an International Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. This growing disease requires a lot of attention. Machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. The major goal of this work is to design a model that can more accurately predict patients' diabetes. The research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. The result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes. © 2023 IEEE.

关键词： Forecasting

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Design and Analysis of Programmed Face Monitoring System

Design and Analysis of Programmed Face Monitoring System

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Electrical, Electronics, Communication and Computers (ELEXCOM), International Conference on

作者： Ashutosh Kewat Manjhi Amit Singh Rajawat Anshika Srivastava Department of Information Technology Artificial Intelligence and Robotics Centre for Artificial Intelligence (Artificial Intelligence and Data Science) Madhav Institute of Technology & Science Gwalior India

This study proposes a unique method for a programmed face monitoring system that makes use of a Proportional-Integral-Derivative (PID) controller and Mediapipe, a well-known computer vision framework. The system makes use of Mediapipe's real-time face detection technology and a PID controller algorithm to precisely monitor the identified face by dynamically adjusting the pan and tilt angles of a servo motor system. The usefulness of the proposed approach is demonstrated by the graphical representation depicting setting time, steady-state error and the oscillation type. The results were compared between Proportional-Integral-Derivative (PID), Proportional-Derivative (PD) and Proportional (P) controllers. The PID controller showed favorable results among all three of the controllers utilized in the face monitoring system. For PID controller-based faced monitoring system, the setting time of the system designed is obtained as 1 second, the steady-state error is low and the oscillation type observed is critically damped. The usage of this method for programmed face monitoring systems has a potential application in a number of fields, including robotics, human-computer interaction, and surveillance systems.

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Magnetically driven microrobots moving in a flow:a review

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Frontiers of Information Technology & Electronic Engineering 2023年第11期24卷 1520-1540页

作者： Jiamiao MIAO Xiaopu WANG Yan ZHOU Min YE Hongyu ZHAO Ruoyu XU Huihuan QIAN School of Science and Engineering the Chinese University of Hong Kong(Shenzhen)Shenzhen 518172China Shenzhen Institute of Artificial Intelligence and Robotics for Society(AIRS) Shenzhen 518129China

Magnetically driven microrobots hold great potential to perform specific tasks more locally and less invasively in the human *** reach the lesion area in vivo,microrobots should usually be navigated in flowing blood,which is much more complex than static ***,it is more challenging to design a corresponding precise control scheme.A considerable amount of work has been done regarding control of magnetic microrobots in a flow and the corresponding *** this paper,we review and summarize the state-of-the-art research progress concerning magnetic microrobots in blood flow,including the establishment of flow systems,dynamics modeling of motion,and control *** addition,current challenges and limitations are *** hope this work can shed light on the efficient control of microrobots in complex flow environments and accelerate the study of microrobots for clinical use.

关键词： Microrobot Flow Dynamics modeling Control

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3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation

arXiv

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arXiv 2024年

作者： Zhong, Xingguang Pan, Yue Stachniss, Cyrill Behley, Jens Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Building accurate maps is a key building block to enable reliable localization, planning, and navigation of autonomous vehicles. We propose a novel approach for building accurate maps of dynamic environments utilizing a sequence of LiDAR scans. To this end, we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation, we extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids, a globally shared decoder, and time-dependent basis functions, which we jointly optimize in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans, we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach 1 on various scenes containing moving objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete 3D maps, outperforming several state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots

Efficient and Accurate Transformer-Based 3D Shape Completion...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Federico Magistri Rodrigo Marcuzzi Elias Marks Matteo Sodano Jens Behley Cyrill Stachniss Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robots that operate in agricultural environments need a robust perception system that can deal with occlusions, which are naturally present in agricultural scenarios. In this paper, we address the problem of estimating 3D shapes of fruits when only partial observations are available. Generally speaking, such a shape completion can be realized by exploiting prior knowledge about the geometry of the fruit. This is typically done by template matching using traditional optimization algorithms, which are slow but accurate, or by encoding such knowledge into the weights of a neural network, leading to faster but often less accurate estimates. Our approach combines the best of both worlds. It exploits the benefit of having a template representing our object of interest with the advantages of using a neural network to learn how to deform a template. Our experimental evaluation demonstrates that our approach yields accurate estimation at a competitively low inference time in challenging greenhouse environments.

关键词： Knowledge engineering Geometry Accuracy Three-dimensional displays Shape Neural networks Green products

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