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Analysis of Gait Data Based on Deep Learning to Predict Multiple Balance Scale Scores

IEEE Sensors Journal 2024年第1期24卷 920-930页

作者： Lin, Chueh-Ho Peng, Chih-Wei Lee, I-Jung Wu, Chi-Ming Lin, Bor-Shing Taipei Medical University International Ph.D. Program in Gerontology and Long-Term Care Taipei City110 Taiwan Taipei Medical University Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei City110 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei City110 Taiwan Taipei Medical University School of Gerontology and Long-Term Care College of Nursing Taipei City110 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237 Taiwan

Unbalanced walking is increasingly common among older adults;therefore, routinely assessing the balance of older adults is crucial. The traditional method of assessing balance uses scales, requires the supervision of a physical therapist (PT), and is time-consuming. The present study, therefore, proposes a deep learning (DL) model architecture that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network model to predict the scores of three scales, namely the Berg balance scale (BBS), timed up and go (TUG), and single-leg stance (SLS). The gait data of 15 m of walking were collected from seven inertial measurement units (IMUs) and input into the CNN-LSTM model for evaluation. The BBS and TUG only require the participants to wear two IMUs on the left and right thighs, respectively, for accurate predictions. The mean absolute errors (MAEs) of predicting the scores of the BBS and TUG are 1.2562 and 1.4016 s, respectively;however, the MAE of score predictions of SLS is higher than that of the BBS and TUG, indicating that gait data cannot be used for assessing SLS;moreover, participants only wear one IMU on the right calf for BBS and TUG evaluations, which yield MAEs of 1.4334 and 1.5229 s, respectively. The proposed system can quickly and accurately predict the scores of the BBS and TUG. The proposed model can assist PTs with making clinical decisions. © 2001-2012 IEEE.

关键词： Long short-term memory

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Spatially Small-scale Approach-avoidance Behaviors Allow Learning-free Machine Inference of Object Preferences in Human Minds

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International Journal of Social robotics 2023年第6期15卷 999-1006页

作者： Huang, Tsung-Ren Chen, Tzu-Chun Lin, Ting-Yu Goh, Joshua O. S. Chang, Yu-Ling Yeh, Su-Ling Fu, Li-Chen Department of Psychology National Taiwan University No. 1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Estimating human preference is an essential capability of a social robot. Such a machine Theory of Mind for others’ preferences is studied predominantly under the framework of inverse reinforcement learning, which however requires millions of approach-avoidance trajectories in a simulated grid world as training samples to achieve state-of-the-art results. Here, the present study explores the utility of taking spatially small-scale approach-avoidance behaviors in real life as predictive cues for efficiently estimating human preferences. Using a toy-playing scenario as an example (ΣN = 58 young and older adults), we found that a person’s subjectively reported and objectively inferred levels of attention and happiness could well predict the person’s subjective levels of toy preference. Importantly, attention and happiness can be rapidly estimated from gaze directions and emotional expressions by computer vision techniques in a learning-free manner. Compared with preference learning from spatially larger-scale movement behaviors, robot estimation of human preferences from these smaller-scale behavioral cues can be more efficient and generalizable to unlearned situations in real life. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Human robot interaction

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Bishoy Gerges Barbara Bazzana Nicolò Botteghi Youssef Aboudorra Antonio Franchi Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands Department of Applied Mathematics University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.

关键词： Location awareness Visualization Pipelines Noise reduction Autonomous aerial vehicles Visual servoing Trajectory Planning Standards Robots

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Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning 1

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13th International Advanced Computing Conference, IACC 2023

作者： Kumbhar, Sujit Khot, Sanjay Jujare, Varsha Patil, Vishal Husainy, Avesahemad Shedbalkar, Koustubha Department of Technology Shivaji University Maharashtra Kolhapur416004 India Department of Automation and Robotics Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Computer Science and Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Electrical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India

ISBN: (数字)9783031567001

ISBN: (纸本)9783031566998

In the current research work, a single cylinder spark (S.I)ignition engine were used for investigations of premium level gasoline-ethanol-alkane experimentally with different operating conditions e.g. variation spark ignition timing. The Engine Lab and PE3 software were used for engine control and data acquisition system. The data obtained after experimentation were used to predict the engine emissions for different operating conditions. The engine emission characteristics were predicted using three machine learning algorithmsviz linear regression, decision tree and random forest. It was found that emissions characteristics such as carbon monoxide, unburnt hydrocarbon found to be minimum for 24°bTDC experimentally as well as predicted by machine learning algorithms with different operating conditions than other spark timing positions such as 15°, 18°, 21°, 27°, 30° bTDC. All three machine learning algorithms gave better results but the random forest algorithm were more accurate than linear regression and decision trees. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Paraffins

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YOLOS-MCI: Automating Microservices Coupling Index Calculation using Vision Transformers

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Procedia computer science 2024年 245卷 924-933页

作者： Gintoro Yaya Heryadi Lukas Lili Ayu Wulandhari Ilvico Sonata Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

Microservice Architectures (MSA) provide flexibility and scalability in software development. However, accurately measuring the level of interdependence among Microservices continues to be a difficult task. Precisely evaluating this connection is essential for efficient MSA design, maintenance, and future development. Conventional techniques for assessing Microservice coupling are frequently done by hand, require a significant amount of time, and are susceptible to mistakes. This impedes the capacity to make well-informed judgments regarding the integration and adjustment of services. This study introduces a new method for automating the computation of the Microservice Coupling Index (MCI) by utilizing the You Only Look at One Sequence (YOLOS) object identification technique in combination with Vision Transformer (ViTs) technology. YOLOS is utilized for identifying constituents within Unified Modeling Language (UML) Component Diagrams, facilitating precise classification and effective assessment of coupling. The model exhibits varying performance over multiple Intersection over Union (IoU) thresholds and object sizes, with an average precision (AP) of 0.406 over IoU values ranging from 0.50 to 0.95. The maximum precision is achieved at an IoU of 0.50, with an AP of 0.709. The model demonstrates good performance in identifying smaller components, especially when evaluated at a 0.75 IoU threshold. However, it faces challenges in detecting small items, suggesting potential areas for improvement in future iterations. Initial results indicate that this automation greatly decreases the need for manual, labor-intensive tasks and enhances the precision of measuring coupling in MSA, hence facilitating effective decision-making in service integration and modification. Automating the computation of the coupling index has the potential to significantly influence the design and management of durable and readily controllable microservice architectures.

关键词： Coupling Microservices Microservices Coupling Index UML Component Diagrams Vision Transformer YOLO

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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia’s Sugar Industry

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ditdit Nugeraha Utama Muhammad Asrol Suharjito Christian Harito Napthaleni Muhammad Syafrudin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul South Korea

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89.

关键词： Fuzzy logic Seminars Shape Object oriented modeling Plantations Manuals Predictive models Sugar industry Intelligent systems Information technology

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WildGaussians: 3D Gaussian splatting in the wild 24

WildGaussians: 3D Gaussian splatting in the wild

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

作者： Jonas Kulhanek Songyou Peng Zuzana Kukelova Marc Pollefeys Torsten Sattler Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague and Faculty of Electrical Engineering Czech Technical University in Prague and Department of Computer Science ETH Zurich Department of Computer Science ETH Zurich Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

ISBN: (纸本)9798331314385

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework.

关键词：

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Design and Implementation of IoT Enabled Smart Assistive Systems for Healthcare Applications 2

Design and Implementation of IoT Enabled Smart Assistive Sys...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Dankan Gowda, V. Srinivas, D. Srinivas, Rachakonda Ingole, Balaji Shesharao Jadhav, Priya Dongare Prasad, K.D.V. Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Sr University School of Business Telangana Warangal506371 India Aditya Institute of Technology and Management Department of Computer Science & Engineering Tekkali Andhra Pradesh Srikakulam India GA United States Department of Robotics and Automation Maharashtra Pune412115 India Symbiosis Institute of Business Management Hyderabad India Pune India

ISBN: (纸本)9798350388916

The use of IoT technology in healthcare systems has been adopted in patient monitoring because IoT technology has been adopted rapidly in the systems. This paper discusses on design and implementation of an IoT integrated smart assistive health care system to enhance the precision and speed of patient health assessment. In the proposed system, wearable sensors are used to capture respiratory rate, pulse rate, blood pressure, oxygen saturation, and body temperature with data to be sent to the cloud server as and when it is received. Some of the features are, however, small data transmission delays, high accuracy in the physiological signals and long durations of monitoring due to the low power consumption. The simulations showed that the response time falls below 500ms, and physicians retain up to a 2 % variance to clinical grade devices, which actualized robust and effective patient care. The incorporation of predicting capacities, thus providing ways of early identification of health complications, makes the system applicable in chronic disease treatments and telemedicine. © 2024 IEEE.

关键词： Blood pressure

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Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models

arXiv

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arXiv 2025年

作者： Martini, Luca Zolezzi, Daniele Iacono, Saverio Vercelli, Gianni Viardo Department of Languages and Modern Culture University of Genova Genova16124 Italy Department of Computer Science and Technology Bioengineering Robotics and Systems Engineering University of Genova Genova16145 Italy

The reconstruction of low-resolution football broadcast images presents a significant challenge in sports broadcasting, where detailed visuals are essential for analysis and audience engagement. This study introduces a multi-stage generative upscaling framework leveraging Diffusion Models to enhance degraded images, transforming inputs as small as 64 × 64 pixels into high-fidelity 1024 × 1024 outputs. By integrating an image-to-image pipeline, ControlNet conditioning, and LoRA fine-tuning, our approach surpasses traditional upscaling methods in restoring intricate textures, sharp edges, and domain-specific elements such as player details and jersey logos. The custom LoRA is trained on a custom football dataset, ensuring adaptability to sports broadcast needs. Experimental results demonstrate substantial improvements over conventional models, with ControlNet refining fine details and LoRA enhancing task-specific elements. These findings highlight the potential of diffusion-based image reconstruction in sports media, paving the way for future applications in automated video enhancement and real-time sports analytics. © 2025, CC BY-NC-ND.

关键词： Image reconstruction

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