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Design of a wearable system for gait analysis based on IMU, sEMG and plantar load signals 12

Design of a wearable system for gait analysis based on IMU, ...

引用

12th international conference on Control Mechatronics and Automation

作者： Alegria, Mariana Gomez-Correa, Manuela Cruz-Ortiz, David Ballesteros, Mariana Inst Politecn Nacl CIDETEC Mexico City DF Mexico Inst Politecn Nacl Med Robot & Biosignals Lab Mexico City DF Mexico

ISBN: (纸本)9798331517519;9798331517526

Gait analysis is one of the most common techniques for neuromusculoskeletal assessment;as a result of this, several devices and techniques have been developed to obtain more accurate tests. Some of the developments in gait analysis are vision systems, force platforms, and biosignal acquisition devices, which often have high costs, are non-portable and need to be used in controlled environments;therefore, they are not applicable in different conditions in which the operation requirements can not be met. This work describes a portable system for gait analysis composed of modules for the acquisition of electromyography signals, inertial measurement sensors for the calculation of the angular range of the joints, instrumented insoles for the analysis of plantar load, and software for the acquisition, processing, and online visualization of the information. The gait analysis development was tested on a healthy subject using a treadmill with three different velocities. The obtained data was analyzed, and some features were compared with the reported using professional systems.

关键词： Gait analysis wearable system EMG IMU plantar pressure

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TEXT-IMAGE DE-CONTEXTUALIZATION DETECTION USING vision-LANGUAGE MODELS 47

TEXT-IMAGE DE-CONTEXTUALIZATION DETECTION USING VISION-LANGU...

引用

47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Huang, Mingzhen Jia, Shan Chang, Ming-Ching Lyu, Siwei Univ Buffalo State Univ New York Buffalo NY 14260 USA SUNY Albany Albany NY 12222 USA

ISBN: (纸本)9781665405409

Text-image de-contextualization, which uses inconsistent image-text pairs, is an emerging form of misinformation and drawing increasing attention due to the great threat to information authenticity. With real content but semantic mismatch in multiple modalities, the detection of de-contextualization is a challenging problem in media forensics. Inspired by the recent advances in vision-language models with powerful relationship learning between images and texts, we leverage the vision-language models to the media de-contextualization detection task. Two popular models, namely CLIP and VinVL, are evaluated and compared on several news and social media datasets to show their performance in detecting image-text inconsistency in de-contextualization. We also summarize interesting observations and shed lights to the use of vision-language models in de-contextualization detection.

关键词： de-contextualization online misinformation out-of-text detection text-image inconsistency

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Text-Guided Few-Shot Semantic Segmentation with Training-Free Multimodal Feature Matching

Text-Guided Few-Shot Semantic Segmentation with Training-Fre...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Buthmann, Guillaume Sakai, Tomoya Qiu, Haoxiang Katsuki, Takayuki Kimura, Daiki IBM Research Tokyo Japan Mines Paris - PSL University Paris France

ISBN: (纸本)9798350368741

This paper addresses few-shot semantic segmentation (FSS) guided by text, where we classify unseen novel classes using image and text references as in-context examples, without the need for training. We enhance the quality and stability of the segmentation masks generated by FSS by combining the capability of open-vocabulary zero-shot semantic segmentation (ZSS) based on foundation models for image and text. We propose a training-free approach using multimodal feature matching that performs segmentation by identifying regions in a target image that match the features from both the image and text references. Experimental results demonstrate that the proposed method outperforms state-of-the-art FSS and ZSS methods. © 2025 IEEE.

关键词： few-shot segmentation semantic segmentation vision-language model zero-shot segmentation

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引用

2024 international conference on Image processing

作者： Han, Chaowei Meng, Gaofeng Huo, Chunlei Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci HK Inst Sci Innovat Ctr Artificial Intelligence & Robot Beijing Peoples R China Capital Normal Univ Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Content-based video retrieval aims to retrieve near-duplicate entries from a database of a given query video. It plays an important role in combating video piracy. Robustness to video temporal dynamics is crucial for a representation model in video retrieval, as frames extracted from two copied videos are hardly temporally aligned in actual situations. However, current image retrieval datasets have difficulty in evaluating this robustness. To address this issue, we collect Similar Frame Dataset (SFD), which consists of 32,923 query-target pairs with 128,240 distraction images. The task of SFD is to retrieve the target frame from all items given a query frame. SFD is constructed by sampling frames from Kinetics-700 action classification dataset. An object detection model (Faster R-CNN) and a Multimodal Large Language Model (BLIP2) are used during sampling to select those valid frames. Besides, we propose Adjacent Frames Contrastive Learning (AFCL) framework. In AFCL, adjacent frames are sampled from unlabeled videos as positive pairs. An image representation model with robustness to changing frames can be trained under AFCL framework and achieve the state-of-the-art performance on SFD. The code will be released at https://***/Chuan-shanjia/Similar-Frame-Dataset.

关键词： Dataset Image retrieval Content-based video retrieval Contrastive learning

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T-Top, a SAR Experimental Platform 22

T-Top, a SAR Experimental Platform

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17th Annual ACM/IEEE international conference on Human-robot Interaction (HRI)

作者： Maheux, Marc-Antoine Caya, Charles Letourneau, Dominic Michaud, Francois Univ Sherbrooke Interdisciplinary Inst Technol Innovat 3IT Sherbrooke PQ Canada

ISBN: (数字)9781665407311

ISBN: (纸本)9781665407311

During these past years, Socially Assistive robots (SARs) have been used to study the benefits of their uses with elderly people and people with dementia for healthcare purposes. Yet, almost all SARs have somewhat limited perception capabilities or respond using simple pre-programmed behaviors and reactions, providing limited or repetitive interaction modalities. To overcome these limitations and take into consideration the strengths and weaknesses of SARs in healthcare settings, this paper presents T-Top, a tabletop robot designed with advanced audio and vision sensors, deep learning perceptual processing and telecommunication capabilities. Designed as a open hardware/software platform, the objective with T-Top is to provide an experimental platform that can implement richer interaction modalities and develop higher cognitive abilities from interacting with people.

关键词： Socially Assistive robot System design Open source HRI platform

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robotic machine vision system for defective screw Identification using Fisher's LDA Techniques 3

Robotic machine vision system for defective screw Identifica...

引用

3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Sahoo, Santosh Kumar Godi, Rakesh Kumar Department of Electronics and Instrumentation Engineering Hyderabad India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350320749

The research work dedicated for the object identification difficulty resolved by the techniques of principal component analysis (PCA) and linear discriminant analysis (LDA) along with robotic machine vision system. The effectiveness of the proposed model is carefully considered for a case when the pictures of an object like Screw have not initially processed but managed them into a standard form in terms of back-ground, scaling, positioning and adjustment of intensity or brightness. Similarly, when processed a huge amount of image data sets it is necessary to use PCA and LDA for optimizing the computational difficulty. Here using the LDA and PCI the effectiveness of the model is analyzed. By an introducing the robotic machine vision system the identification accuracy can be enhanced to a greater label. © 2023 IEEE.

关键词： Principal component analysis

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FACTORIZED EMBEDDING GRAPH MATCHING NETWORK FOR LEARNING LAWLER'S QUADRATIC ASSIGNMENT PROBLEM 31

FACTORIZED EMBEDDING GRAPH MATCHING NETWORK FOR LEARNING LAW...

引用

2024 international conference on Image processing

作者： Yang, Yirui Lin, Xubin He, Li Guan, Yisheng Zhang, Hong Guangdong Univ Technol Biomimet & Intelligent Robot Lab Guangzhou Peoples R China Guangdong Acad Sci Inst Intelligent Mfg Guangzhou Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Graph matching refers to establishing correspondence between two sets of point while keeping consistency between their edge sets. Recent works in learning-based graph matching have attempted to solve the problem either by linear assignment, which transfers local structure information into node embedding at individual graphs, or by quadratic assignment through vertex classification over their association graph. However, the former embedding-based pipeline methods often neglect second-order edge similarity, leading to decreased accuracy;while the latter quadratic assignment solvers consume significant memory due to huge computation on the association graph. To address these issues, our key idea is to integrate a factorized embedding module to efficiently propogate information over the association graph. To this end, we propose a novel factorized embedding-based network, namely FEGM, which takes into account the second-order edge similarity, as well as a factorization model of GCN network, so that we extend the embedding-based pipeline for learning the Lawler's QAP while reducing memory consumption. Experimental results show that FEGM achieves a competitive matching accuracy while being superior in time and space efficiency.

关键词： Graph matching Deep learning Quadratic assignment problem Graph neural networks

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Visual Positioning System of Bag Removal robot Based on Deep Learning 9

Visual Positioning System of Bag Removal Robot Based on Deep...

引用

9th international conference on Intelligent Computing and signal processing, ICSP 2024

作者： Liu, Bo Tang, Ruiyin College of electrical engineering North China University of Science and Technology Tangshan China School of electronic and control engineering North China Institute of Aerospace Engineering Langfang065000 China

ISBN: (纸本)9798350376548

In industrial production, the dumping of raw material packaging is mostly done manually, which not only affects production efficiency but also endangers the health of workers. This article proposes an improved algorithm based on YOLOV5, which uses Mobiletv3 instead of the YOLOV5 backbone network, adjusts parameters using an improved SE attention mechanism, and optimizes the model using the LeakyReLU activation function. The entire system uses an improved algorithm to detect the target bag rope, uses a depth camera to obtain the target coordinates, and finally calculates the target coordinates in the robot coordinate system. Compared with Fast RCNN and YOLOV5 algorithms, the improved YOLOV5 MobileNetV3 algorithm has a knot detection accuracy of 97.5%, a recall rate of 92.43%, and an F1 score of 94.8. The model has a small volume, fast detection speed, and a single image detection time of only 187ms, with a mapping rate of 97.5%. The error between the coordinates obtained after hand eye calibration and the actual coordinates of the robotic arm in the XYZ axis direction does not exceed 3mm, and the actual coordinates are much smaller than the target size. © 2024 IEEE.

关键词： robotic arms

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18th international Workshop on Design and Architecture for signal and Image processing, DASIP 2025

18th International Workshop on Design and Architecture for S...

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18th international Workshop on Design and Architecture for signal and Image processing, DASIP 2025

ISBN: (纸本)9783031878961

The proceedings contain 10 papers. The special focus in this conference is on Design and Architectures for signal and Image processing. The topics include: LiFT: Lightweight, FPGA-Tailored 3D Object Detection Based on LiDAR Data;A Practical HW-Aware NAS Flow for AI vision Applications on Embedded Heterogeneous SoCs;Endoscopy Image Classification for Wireless Capsules with CNNs on Microcontroller-Based Platforms;joint Underwater Depth Estimation and Dehazing from a Single Image Using Attention U-Net;KD-AHOSVD: Neural Network Compression via Knowledge Distillation and Tensor Decomposition;Novel Scheduling and Shifter Networks for 5G LDPC Decoders;Comparison Between In-Core Hardware IDS, Off-Core Hardware IDS and Software IDS;comparative Study of Memory Optimization Techniques for Dataflow-Modeled Applications.

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An Improved vision Transformer for Early Detection of Diabetic Foot Using Thermogram 8

An Improved Vision Transformer for Early Detection of Diabet...

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8th international conference on Intelligent Computing and signal processing, ICSP 2023

作者： Shao, Huasong City University of Macau Faculty of Data Science Avenida Padre Tomás Pereira Macau China

ISBN: (纸本)9798350302455

Diabetic foot is a complication of diabetes mellitus caused by prolonged hyperglycemia. It can lead to serious consequences such as ulceration, infection, and even amputation. Early detection and treatment are essential to reduce the risks of these complications and improve the quality of life of patients. Several studies have demonstrated the potential of combining foot thermogram with deep learning for early detection of diabetic foot. Based on these insights and studies, our study focused on applying state-of-the-art networks in the field of image classification, vision transformers, to this task. In this study, A learnable block was added to the original vision transformer, effectively improving the performance of the original vision transformer. Experimental results showed that in this task, compared to the vision transformers with a maximum accuracy of 90% and convolutional neural networks with a maximum accuracy of 100%, the proposed vision transformer can achieve an accuracy of 99%, demonstrating the potential of vision transformers for application on small-size medical image datasets. © 2023 IEEE.

关键词： Patient treatment

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