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soft Exoskeleton Glove for Hand Assistance Based on Human-machine Interaction and Machine Learning 1

Soft Exoskeleton Glove for Hand Assistance Based on Human-ma...

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1st IEEE international conference on Human-Machine Systems (ICHMS)

作者： Chen, Xiaoshi Gong, Li Zheng, Lirong Zou, Zhuo Fudan Univ State Key Lab ASIC & Syst Shanghai Peoples R China

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158716

This paper proposes a human machine interaction system in the field of stroke rehabilitation, based on the concept of mirror therapy (MT). It aims to improve the hand motor function of stroke patients, enabling a true synchronization between the affected hand and non-affected hand (healthy hand) for the stroke patient. It consists of a soft exoskeleton glove, a surface electromyography (sEMG) signal collecting armband and machine learning (ML) algorithms. The glove is developed by integrating low-power motors to provide force strength for the hand movement. Unlike the rigid exoskeleton devices, the glove is comfortable to wear and lightweight, so it is more suitable for rehabilitation training of stroke patients in daily life. The armband collects the sEMG signals for pattern recognition by the ML algorithms. In the experiment, four subjects perform 10 hand gestures to collect data for model training. A comparison of data preprocessing is conducted to find the optimal data segmentation method and feature vector sets. A series of pattern recognition algorithms are developed and assessed in different aspects, including prediction accuracy, training time and predicting time. All 10 gestures can be recognized in offline mode with an accuracy up to 99.4%. The control of soft exoskeleton glove in real-time manner is also carried out, and the accuracy is 82.2%. The experiment result demonstrates the feasibility of the proposed system. The innovations and limitations of the work are discussed at the end of the paper.

关键词： soft exoskeleton glove surface electromyography (sEMG) mirror therapy machine learning

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Fruit recognition based on near-infrared spectroscopy using deep neural networks 21

Fruit recognition based on near-infrared spectroscopy using ...

引用

5th international conference on Machine Learning and soft computing, ICMLSC 2021

作者： Ninh, Duy Khanh Doan, Thi-Ngoc-Canh Ninh, Chi Khanh Nguyen-Thi, Thanh Xuan Thanh, Nhan Le The University of Danang University of Science and Technology Danang Viet Nam The University of Danang University of Economics Viet Nam The University of Danang Vietnam-Korea University of Information and Communication Technology Danang Viet Nam The University of Danang Danang International Institute of Technology Danang Viet Nam

ISBN: (纸本)9781450387613

Near-infrared (NIR) spectroscopy has been widely used to determine the varieties and chemical properties of agricultural and food products. The major advantage of NIR spectroscopy is that the analysis is carried out in a simple, fast, and non-destructive manner, making it suitable for food applications. As the first step in applying NIR spectroscopy for fruit recognition and analysis in Vietnam, this paper presents deep neural networks (DNNs) based solutions for automatic recognition of several kinds of fruits. We compared two proposed DNN architectures based on Convolutional Neural Network (CNN) and Residual Network (ResNet). Additionally, we proposed feature extraction methods using the first and second derivatives of the Savitzky-Golay (SG) filtered normalized NIR data. Experimental results show that the deep learning approach combined with reasonable feature extraction process can achive the accuracy of approximately 99% for the task of classifying five types of fruits including Apple, Avocado, Dragon Fruit, Guava, and Mango. The ResNet-based model is more compact and has slightly better recognition performance than the CNN-based one. The inclusion of the first and second derivatives of SG-smoothed normalized spectra improves the recognition accuracy of the proposed DNN models by more than 8%. Moreover, the recognition performance of the proposed DNN models surpasses that of traditional classifiers, including k-nearest neighbors, Naive Bayes, and support vector machine. Our proposed methods were proved to be robust against the freshness of fruits, the NIR device's calibration parameters, and the measurement position on the body of fruits. © 2021 ACM.

关键词： Deep neural networks

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Prediction of Prediabetes, No Diabetes and Diabetes Mellitus-2 Using pattern recognition 1st

Prediction of Prediabetes, No Diabetes and Diabetes Mellitus...

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1st FICR international conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020

作者： Kamble, Anupriya Hannan, Shaikh Abdul Jain, Anubha Manza, Ramesh Department of Computer Science and Information Technology Dr. Babasaheb Ambedkar Marathwada University AurangabadMaharashtra India Department of Computer Science Al-Baha University Albaha Saudi Arabia Jaipur India

ISBN: (纸本)9789811560132

As of now everyone knows that the glucose or the sugar level when increases or decreases in the blood it leads to diabetes disorder. The doctors do have a need of an automated system to detect diabetes disorder by which their time for writing down the prescription will be minimized. The automated detection of the disorder will help doctors to reduce the time spent on the test and also it will be helpful for the patient to take precautions much early if in case a subject is suffering from the disorder. The present analysis concludes that if a subject is possessing PD, NODB or DM2 and if it has to be detected then NaiveBayesMultinominalText classification can be used if and only if it has to be used in comparison with CVParameterSelection or Stacking classification as NaiveBayesMultinominalText gave highest result 48.837% compared to CVParameterSelection and Stacking. © 2021, Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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BeAwareOfYourAct: A Framework for Behavioural Action Detection in Workplace through Deep Learning Analysis and Augmented Action pattern recognition

BeAwareOfYourAct: A Framework for Behavioural Action Detecti...

引用

Information and computing Research (iCORE), international conference in

作者： Edward Humphrey C. Isles Francis F. Balahadia College of Computer Studies Laguna State Polytechnic University

The purpose of this study is to propose a system framework that addresses the problem in monitoring malicious behavior of an individual towards someone in the same vicinity through multi-person detection video processing. The real-time video rendering analysis and behavioral action detection is an efficient method in line with pervasive or any event related atrocious behavior towards an individual. In the development of the system several algorithms will be utilized such as object detection, action recognition algorithms and techniques (e.g. Human Activity recognition, Supervised Learning Data Representation, etc.) which will heavily rely on Regions with Convolutional Neural Network (RCNN). The capability of Artificial Intelligence (A.I.) to analyze real-time video rendering through the training data to be supplied as a point of reference in deep learning analysis, any act that might cause discomfort to the victim can be detected to be reported immediately to the Officer-in-charge preventing the maltreatment to further escalate. In order to accomplish the said study that will serve as a pillar for the system development. It is concluded that this system framework of real-time monitoring is a great approach in preventing certain abuse from starting and will make the conduct of investigation more precise and efficient should a harassment come into place. Through this framework it can make the vicinity more conducive to healthy communication and prevent intimidating words to be casually mentioned.

关键词： Deep learning Supervised learning Training data Object detection Streaming media Rendering (computer graphics) Real-time systems

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A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images 1

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2021

作者： An, Chengyang Chen, Huai Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

Radiomics analysis can help patients suffered from head and neck (H&N) cancer customize tailoring treatments. It requires a large number of segmentation of the H&N tumor area in PET and CT images. However, the cost of manual segmentation is extremely high. In this paper, we propose a coarse-to-fine framework to segment the H&N tumor automatically in FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) images. Specifically, we trained three 3D-UNets with residual blocks to make coarse stage, fine stage and refined stage predictions respectively. Experiments show that such a training framework can improve the segmentation quality step by step. We evaluated our framework with Dice Similarity Coefficient (DSC) and Hausdorff Distance at 95% (HD95) of 0.7733 and 3.0882 respectively in the task 1 of the HEad and neCK TumOR segmentation and outcome prediction in PET/CT images (HECKTOR2021) Challenge and ranked second. © 2022, Springer Nature Switzerland AG.

关键词： Positron emission tomography

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Big Data Clustering Algorithms: Improving Efficiency and Scalability for Massive Data Mining and pattern recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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Pervasive Computational Technologies (ICPCT), international conference on

作者： Shanmugam Muthu G Ramesh Kalyan V. Samuthira Pandi Shobana D Lakshmi Priya J Jaishika S Shipt Inc Birmingham USA Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamilnadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai Department of ECE Saveetha Engineering College Chennai Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study.

关键词： Dimensionality reduction Machine learning algorithms Accuracy Social networking (online) Scalability Heuristic algorithms Clustering algorithms Adaptive algorithms Big Data Computational efficiency

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A Survey of Person Re-identification Based on Deep Learning 10

A Survey of Person Re-identification Based on Deep Learning

引用

10th international conference on computing and pattern recognition, ICCPR 2021

作者： Tian, Zimin Chen, Si Wang, Da-Han Lu, Junwen School of Computer and Information Engineering Xiamen University of Technology China Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450390439

Person re-identification (Re-ID) has been a popular research topic in computer vision in recent years, and it has important application value in numerous fields, such as intelligent security. The person Re-ID task is to identify whether the pedestrians appearing under different cameras are the same person. The traditional person Re-ID methods mainly rely on the characteristics of manual design, and it has difficulty in solving the problems of person occlusion, posture change, and illumination variation. With the wide application of deep learning, the person Re-ID based on deep learning has brought new ideas for solving these problems, and has been widely concerned by scholars. This paper summarizes and analyzes the latest research trends of person Re-ID based on deep learning. In our work, the recent research works of person Re-ID are coarsely categorized into the supervised learning methods and the unsupervised learning methods according to whether the pedestrian images in the training set have real labels. We then describe the representative datasets used in the person Re-ID task. Finally, we conclude and discuss the future directions of the person Re-ID based on deep learning. © 2021 ACM.

关键词： Unsupervised learning

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Learning Dynamics for Video Facial Expression recognition 21

Learning Dynamics for Video Facial Expression Recognition

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Proceedings of the 2021 4th international conference on Algorithms, computing and Artificial Intelligence

作者： Yu Wang XinMin Xu Yao Zhuang Zhejiang University China Alibaba Group China

ISBN: (纸本)9781450385053

Video-based facial expression recognition has always been a focus of attention in the computer vision community for decades. It aims to automatically identify one of several emotions represented by the video according to the input audio or visual information. Capturing the dynamics, namely motion pattern plays an important role in video-based facial expression recognition. In this paper, we explore an effective and efficient motion pattern to model temporal relationships called Diff-based Canny Operator (DCO) to guide the inter-frame aggregation and generate a novel feature modality. Our proposed DCO add rare computational consumption and can be easily inserted into any frameworks, so we incorporate it with exist networks to form a unified structure for video-based facial expression recognition task, which enable the network to Ideally extract temporal information. With extensive experiments on CK+ and AFEW dataset, our proposed method shows its superiority with better or comparable performance compared to the state-of-the-art approaches at low FLOPs.

关键词： Diff-based Canny Operator dynamics video-based facial expression recognition motion pattern

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Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

引用

3rd international conference on Innovative computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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Attention based Writer Independent Verification 17

Attention based Writer Independent Verification

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17th international conference on Frontiers in Handwriting recognition (ICFHR)

作者： Shaikh, Mohammad Abuzar Duan, Tiehang Chauhan, Mihir Srihari, Sargur N. SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14214 USA

ISBN: (纸本)9781728199665

The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not. Such a task calls for the neural network to make it's outcome interpretable, i.e. provide a view into the network's decision making process. We implement and integrate cross-attention and soft-attention mechanisms to capture the highly correlated and salient points in feature space of 2D inputs. The attention maps serve as an explanation premise for the network's output likelihood score. The attention mechanism also allows the network to focus more on relevant areas of the input, thus improving the classification performance. Our proposed approach achieves a precision of 86% for detecting intra-writer cases in CEDAR cursive "AND" dataset. Furthermore, we generate meaningful explanations for the provided decision by extracting attention maps from multiple levels of the network.

关键词： 2D-Cross-Attention 2D-soft-Attention Attention attention-map convolutional-neural-network deep-learning Explanability Handwriting-Verification Interpretability pattern-recognition Siamese-Netowrks Transformer Verification writer-verification

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