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Development of Gait Classification System Using Six-Axis Inertial Sensors by CNN-LSTM

Development of Gait Classification System Using Six-Axis Ine...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Shotaro Ohno Kouki Nagamune Graduated School of Engineering University of Fukui Fukui Japan Graduated School of Engineering University of Hyogo Hyogo Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Restoration of walking function is the most important goal of rehabilitation. However, the evaluation of post-stroke rehabilitation lacks quantitative elements. In this study, we used an IMU to acquire the angles of three axes (rotation of x-axis, rotation of y-axis, and rotation of z-axis) of the knee during walking motion as gait parameters. Using this data, machine learning analysis is performed. In this article, we discuss the gait parameters, the angular data of the three axes of the knee, and examine which of the three angular parameters increases or de-creases the generalization ability of the model. As a result, we identify the gait parameters to be used as a dataset. Time series data of the knee angle of one step in the gait measurement was used as the dataset. CNN - LSTM algorithm was used to construct the model. Five subjects were measured with the knee joint motion measurement device attached to both knees. A total of four types of gait movements, including normal gait and three types of gait movements that reproduce hemiplegia, were meas-ured for analysis. Out of one hundred thirty-six steps of data from five subjects, four subjects were used as training data and one unknown subject's data, which was not used during training, was applied to the test data. The results of the prediction of the estimated data show that rotation of z-axis was a parameter that reduced the generalization ability of the model, while rotation of y-axis was a parameter that increased the generalization ability of the model. Prediction results for the rotation of x-axis data alone showed poor accuracy, but when combined with the rotation of y-axis data, the generalization ability was improved.

关键词： Knee Legged locomotion Training Inertial sensors Time series analysis Training data Predictive models Data models pattern recognition Motion measurement

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Compiler Design for recognizing different Programming Languages

Compiler Design for recognizing different Programming Langua...

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international conference on computing and Networking Technology (ICCNT)

作者： Ch Rahul A N Sharma Bedadala Mohaneeswar Reddy Dutta Swetchana Niharika Panda Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Compiler design for programming language recognition is a tedious process with crucial phases. These phases include lexical analysis, syntax parsing, semantic validation, intermediate code generation, optimization, and final code generation. The initial step involves linguistic analysis, breaking source code into elemental components like keywords, identifiers, literals, operators, etc. This step is tailored to each programming language, considering the specific constructs and syntax rules of that particular language. Leveraging the Python-Lex-Yacc library, this research seeks to design a compiler to identify multiple programming languages, primarily focusing on the abovementioned lexical analysis phase. The proposed compiler is engineered to recognize various programming languages, including C++, Java, and Python. The need for a compiler to recognize multiple programming languages concurrently arises from the evolving land of software development. With modern applications often written in various languages, a unified compiler capable of recognizing multiple languages is essential. This research aims to tackle this challenge by proposing a compiler that identifies multiple programming languages concurrently.

关键词： Java Program processors Codes Source coding Semantics Syntactics Tokenization pattern recognition Python software development management

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pattern recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features

Pattern Recognition in Lung Disease Classification: Leveragi...

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international conference on computing and Networking Technology (ICCNT)

作者： M Ramya Sree Mettu Siddhartha Poli Vamsi Vardhan Reddy Tripty Singh Rekha R Nair Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction.

关键词： Visualization Accuracy Quantization (signal) Pneumonia Lungs Computational modeling Feature extraction Convolutional neural networks X-ray imaging Medical diagnostic imaging

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Estimating Human Strategies by Collision Avoidance Experiments with Meta-strategy Agents

Estimating Human Strategies by Collision Avoidance Experimen...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Norifumi Watanabe Kensuke Miyamoto Graduate School of Data Science Musashino University Tokyo JAPAN Graduate School of Media and Governance Keio University Kanagawa JAPAN

In human cooperative behavior, there are some strategies: a passive behavioral strategy based on others’ behaviors and an active behavioral strategy based on the objective-first. However, it is unclear how to acquire a meta-strategy to switch those strategies. In this study, we conduct a collision-avoidance experiment with agents taking multiple strategies in a grid-like corridor to see whether the subject’s behavior changes when the agent’s strategy changes. Furthermore, we compare the behavior selected by the subjects with the behavior of the agents acquired by reinforcement learning. The experimental results show that subjects can read the change in strategy from the behavior of the opposing agent.

关键词： Switches Reinforcement learning Behavioral sciences pattern recognition Collision avoidance Robots

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Can Threshold Memory Switching Memristor be Effectively Utilized for the Hardware Implementation of Brain-Inspired Neuromorphic Systems?

Can Threshold Memory Switching Memristor be Effectively Util...

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international conference on Semiconductor

作者： Mani Shankar Yadav Brajesh Rawat Department of Electrical Engineering Indian Institute of Technology Ropar Rupnagar Punjab India

ISBN: (数字)9798350352078

ISBN: (纸本)9798350352085

Integration of the threshold-type selector layer with the memristor device (TS-MS) has emerged as a promising candidate for suppressing the undesired sneak path current within crossbar memory structures. However, their potential advantages for neuromorphic computing architecture remain unexplored. In this context, we systematically explore the device-to-crossbar level performance of NbO 2 -HfO x -based TS-MS cell with multi-state resistive switching behavior in a layer neural network for pattern recognition. The NbO 2 -HfO x -based TS-MS device demonstrates the existence of distinct four-level resistance states during the reset process, which facilitates electronic synapses with a high resistance state modulation within the range of 6.5 KΩ to 25.71 KΩ and an on-off resistance ratio of 12. The crossbar simulation reveals that TS-MS could allow recognition accuracy with around 62% when the noise level increases around 27%. Our findings highlight that TS-MS devices have the potential to offer noise immunity operations, coupled with energy efficiency, which positions them as strong candidates for future hardware implementations of brain-inspired neuromorphic systems.

关键词： Resistance Performance evaluation Accuracy Noise Memristors Switches Hardware Energy efficiency pattern recognition Synapses

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Research on Multi-level Attention-based Human Pose Estimation 2

Research on Multi-level Attention-based Human Pose Estimatio...

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2nd international conference on Algorithms, High Performance computing and Artificial Intelligence, AHPCAI 2022

作者： Gao, Jun Huang, Hua Li, Qishen Xia, Shaoqi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network. © 2022 IEEE.

关键词： attention mechanism Human Pose Estimation Keypoint detection

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Fault pattern recognition Method for DC-DC Power by Using Output Voltage Waveform Analysis 4

Fault Pattern Recognition Method for DC-DC Power by Using Ou...

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4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Yuan, Wei Gao, Huizhuang Li, Wei Wang, Changxin The Fourth Military Representative Office Beijing Army Armament Department Beijing China China Aerospace Science & Industry Crop Defense Technology R&T Center Beijing China Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

DC-DC power module is the power supply for electronic system. With its wide range of applications in engineering equipment, the degradation and fault conditions have received increasing attention. Many researches on DCDC power tend to take some of their internal devices out of the test and analysis, and study the corresponding test methods, which are rapidly developing in single fault diagnosis. In this paper, a method is proposed for identifying fault patterns of DC-DC power. The fault characteristics of the internal components in the power module are determined by PSpice circuit simulation. The test signals are the output current pulse signal and the normal input voltage signal. After inputting the test signal, the fault diagnosis of optocoupler and MOSFET tube is realized by analyzing the output waveform. The result shows that the optocoupler has a great influence on the adjustment ability of the circuit anti-jamming. MOSFET tube has a great influence on the output voltage level. © 2022 IEEE.

关键词： Signal analysis

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Computer Vision for Economic Activity Monitoring

Computer Vision for Economic Activity Monitoring

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： K. Santhanalaxmi Ryam Majed Khazal Nada Qasim Mohammed Diana Erudiyanathan Hameed Hassan Khalaf D Rajkumar Pillay School of Management Sastra Deemed to be University Thanjavur Department of Pharmacy Mazaya University College Iraq Department of Technology Al-Nisour University College Nisour Seq. Karkh Baghdad Iraq Department of Management Kristu Jayanti College (Autonomous) Bengaluru Karnataka Department of Management AI-Manara College for Medical Sciences (Maysan) Iraq Department of Management Operations and Analytic Institute of Public Enterprise Hyderabad

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

One of the most creative methods for real-time economic pattern analysis and interpretation is the use of computer vision in economic activity monitoring. The purpose of this study is to investigate computer vision technologies and assess their potential for automating the collection and analysis of visual data. If they did, it would allow for quicker and more accurate evaluations of economic activity. This study demonstrates how computer vision may be used to track several economic indicators, such as retail sales, the growth of infrastructure, and agricultural productivity. This is achieved through the use of complex algorithms for image recognition, pattern analysis, and object detection. The study highlights the benefits of using computer vision in place of more conventional data collection methods. Its effectiveness, scalability, and ability to provide thorough insights into the operation of the economic system are a few of these benefits. The purpose of this research is to demonstrate how computer vision can enhance decision-making processes for businesses and policymakers by giving them a better knowledge of patterns of economic activity. With the aid of case studies and experimental evaluations, this work was effectively finished. These results should be considered since they could have significant effects on how better policy planning and economic forecasting are done.

关键词： Computer vision Visualization Image recognition Economic indicators Scalability Decision making Economic forecasting Real-time systems pattern analysis Monitoring

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Data analysis of Global Perceived Stress Scores during Covid-19 4

Data analysis of Global Perceived Stress Scores during Covid...

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4th international conference on Circuits, Control, Communication and computing, I4C 2022

作者： Muttaraju, Chakita Kabber, Anusha Parimala, S. Anagani, Prajwal Srinivasa, Gowri PES University Department of Computer Science and Engineering Bengaluru India PES University PES Center for Pattern Recognition Dept. of CSE Bengaluru India

ISBN: (纸本)9798350397475

The Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. This work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. Machine Learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.

关键词： Decision trees

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Web Application System of Handwritten Text recognition 5

Web Application System of Handwritten Text Recognition

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Bodnia, Yevhen Kozulia, Mariia Natl Tech Univ Kharkiv Polytech Inst Kirpichova Str 2 Kharkiv Ukraine

The problem of text recognition is becoming increasingly important due to the active introduction of digital computing and the widespread use of word processors. pattern recognition is one of the most difficult from a mathematical point of view and one of the most popular areas of artificial intelligence programming. In the work is researched approaches and methods of solving text recognition problem, improved the performance of the available algorithms for text recognition and created algorithmic software. According to the analysis, neural networks were selected for handwriting recognition. The main advantage of using neural networks is a good generalization ability, the ability to use context analysis and recognize a symbol based on the surrounding symbols. The software implementation features of Hopfield and convolutional neural network, genetic algorithm, which were chosen as effective methods for recognizing handwritten text, were considered. Algorithmic software and web application that uses these methods for the task of handwritten text recognition is developed.

关键词： Handwritten text recognition pattern recognition recognition methods neural network genetic algorithm convolutional neural network Hopfield neural network machine learning data models

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