检索结果-内蒙古大学图书馆

Design and fabrication of accelerometer sensor using fused filament fabrication technique

PROGRESS IN ADDITIVE MANUFACTURING 2025年第4期10卷 2473-2486页

作者： Bhandari, Soumya Krishnanand, Soumyendu Roy, Soumyendu Singh, Alok Taufik, Mohammad Maulana Azad Natl Inst Technol Bhopal Ctr Excellence Prod Design & Smart Mfg Bhopal 462003 MP India Maulana Azad Natl Inst Technol Bhopal Dept Mech Engn Bhopal 462003 MP India Bennett Univ Dept Phys Noida 201310 UP India

This research aims to investigate the design and manufacturing possibilities of micro-electro-mechanical devices, specifically accelerometers, utilizing a fused filament fabrication (FFF) system. This study involves the design of a 3D model for an accelerometer sensor, followed by the selection of suitable materials for its fabrication. Polylactic acid (PLA) is chosen for constructing the base structure, while conductive acrylonitrile-butadiene-styrene (ABS) is utilized for the sensing layer. Three distinct sensor models are generated, each featuring different shapes of sensing layers. Subsequently, various combinations of sensor models are subjected to testing using a source measurement unit to maintain constant voltage while measuring current, enabling the calculation of resistance values. The study reveals that both the selection of materials and the design of the sensing layer exert a substantial influence on sensor performance. Through the comparison of various sensor models and configurations, the research endeavors to pinpoint the most effective sensor design, characterized by commendable sensitivity and a streamlined manufacturing process. This study makes a significant contribution to the advancement of micro-electro-mechanical device fabrication through additive manufacturing (AM) or 3D printing by exploring the design and material selection for sensors. Present study offers valuable insights that could pave the way for the development of enhanced sensitivity in sensors with low cost and the optimization of manufacturing processes.

关键词： 3D printing 3D-printed sensor Additive manufacturing accelerometer sensor

来源：评论

学校读者我要写书评

暂无评论

Automating QA analysis for a six-degree-of-freedom (6DOF) couch using image displacement and an accelerometer sensor

引用

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2022年第0期101卷 129-136页

作者： Popreeda, Tippawan Masa-nga, Wilia Deeharing, Aphisara Udee, Nuntawat Tannanonta, Chirapha Thongsawad, Sangutid Chulabhorn Royal Acad Princess Srisavangavadhana Coll Med Bangkok Thailand Chulabhorn Hosp Chulabhorn Royal Acad Dept Radiat Oncol Bangkok Thailand Naresuan Univ Fac Allied Hlth Sci Dept Radiol Technol Phitsanulok Thailand 906 Kamphangpech 6 Rd Bangkok 10210 Thailand

The purpose of this study is to develop an approach for automating quality assurance (QA) analysis for a sixdegree-of-freedom (6DOF) couch using image displacement and an accelerometer sensor. A cubic phantom was fabricated using 3D printing and the accelerometer sensor was embedded in the phantom to measure the couch in the pitch and roll directions. The accuracy and reliability of image displacement and the accelerometer sensor were investigated prior to their practical use for 6DOF couch QA. Image displacement performance had an accuracy and reliability of 0.026 +/- 0.026 mm for the translation direction and 0.021 +/- 0.016 degrees for the rotation direction. accelerometer sensor performance had an accuracy and reliability of 0.023 +/- 0.018 degrees for pitch rotation and 0.051 +/- 0.024 degrees for roll rotation. Automating QA analysis was used to perform 6DOF couch QA, and the couch position errors measured using image displacement were less than 0.99 mm, 0.91 mm, 0.82 mm for the vertical, longitudinal, lateral translation in range between +/- 20 mm, and 0.07 degrees, 0.23 degrees, and 0.2 degrees for pitch, roll, and yaw rotation in range between +/- 3 degrees whereas the couch position errors measured using the accelerometer sensor were less than 0.1 degrees and 0.19 degrees for the pitch and roll rotation in range between +/- 3 degrees.

关键词： Automating QA analysis 6DOF couch QA Image displacement accelerometer sensor

来源：评论

学校读者我要写书评

暂无评论

MARS: A Multiview Contrastive Approach to Human Activity Recognition From accelerometer sensor

IEEE SENSORS LETTERS

引用

IEEE sensorS LETTERS 2024年第3期8卷 1页

作者： Sharma, Gulshan Dhall, Abhinav Subramanian, Ramanathan Indian Inst Technol Ropar Rupnagar 140001 India Flinders Univ S Australia Adelaide SA 5042 Australia Univ Canberra Bruce ACT 2617 Australia

In this letter, we present MARS, a novel approach, which com-bines a multiview fusion technique with contrastive loss to accurately identify human activities using accelerometer sensor data. accelerometer sensor enables precise monitoring of human activities in diverse contexts. Our approach leverages both temporal and spectral views of accelerometer data, integrating them through an attention mechanism to enhance the overall understanding of human activities. To further improve the discriminative power of the learned representations corresponding to different activity classes, we apply a contrastive loss-based siamese network. Emprical findings confirm that MARS outperforms state-of-the-art on the harAGE dataset by a significant margin of 4.71 in unweighted average recall.

关键词： accelerometers sensors Human activity recognition Monitoring Legged locomotion Stairs Mars sensor applications accelerometer sensor human activity recognition (HAR) multiview fusion contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Prototype of sensor Node for Low-Cost Machine Vibration Monitoring System Using accelerometer sensor 9

Prototype of Sensor Node for Low-Cost Machine Vibration Moni...

引用

9th IEEE International Conference on Communication, Networks and Satellite (IEEE Comnetsat)

作者： Susanto, Misfa Saputra, Rizky Herlinawati Savetlana, Shirley Alam, Syaiful Univ Lampung Dept Elect Engn Bandar Lampung Indonesia Univ Lampung Dept Mech Engn Bandar Lampung Indonesia

ISBN: (纸本)9780738125176

One important aspect in industrial manufacturing processes is the maintenance of machines' conditions. Therefore, monitoring the machine healthiness conditions is a crucial action. The drawback of conventional monitoring method is inefficiencies of the method due to the need to visit every single machine. One parameter for monitoring the machine's healthiness is vibration for the machines that have the motor components. This paper proposes a real-time monitoring system for such vibration monitoring. The proposed system is based on wireless sensor network (WSN). The nodes' prototype of proposed WSN was built and consisted of three nodes i.e. two sensor nodes and one sink node. The main component of the proposed system is Arduino uno which is not expensive relatively. In order to monitor the vibration parameter, the used sensor is the ADXL345 accelerometer sensor. The system design and testing have been carried out. The testing has been done for two aspects;the evaluation of communication performances and the machine vibration field measurements. The parameter for the testing of communication aspect is the data throughput that has been collected for the network performance evaluation. For the testing in real situations, the coffee grinder machine was chosen to be applied for the vibration monitoring system using one sensor and one sink nodes. From the testing results, the system achieved 546 bps of best throughput and was able to measure on the conditions of various supply powers to the machine. It implies the feasibility of the system in monitoring the vibration of the machine.

关键词： Monitoring system wireless sensor node prototype accelerometer sensor industrial machine vibration field testing

来源：评论

学校读者我要写书评

暂无评论

UPDRS Label Assignment by Analyzing accelerometer sensor Data Collected from Conventional Smartphones 13th

UPDRS Label Assignment by Analyzing Accelerometer Sensor Dat...

引用

13th International Conference on Brain Informatics (BI)

作者： Sajal, Md Sakibur Rahman Ehsan, Md Tanvir Vaidyanathan, Ravi Wang, Shouyan Aziz, Tipu Mamun, Khondaker A. United Int Univ UIU Dhaka Bangladesh United Int Univ Inst Adv Res Adv Intelligent Multidisciplinary Syst Lab AIMS L Dhaka Bangladesh Imperial Coll London London England Fudan Univ Shanghai Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9783030592776;9783030592769

The study of the characteristics of hand tremors of the patients suffering from Parkinson's disease (PD) offers an effective way to detect and assess the stage of the disease's progression. During the semi-quantitative evaluation, neurologists label the PD patients with any of the (0-4) Unified Parkinson's Diseases Rating Scale (UPDRS) score based on the intensity and prevalence of these tremors. This score can be bolstered by some other modes of assessment as like gait analysis to increase the reliability of PD detection. With the availability of conventional smartphones with a built-in accelerometer sensor, it is possible to acquire the 3-axes tremor and gait data very easily and analyze them by a trained algorithm. Thus we can remotely examine the PD patients from their homes and connect them to trained neurologists if required. The objective of this study was to investigate the usability of smartphones for assessing motor impairments (i.e. tremors and gait) that can be analyzed from accelerometer sensor data. We obtained 98.5% detection accuracy and 91% UPDRS labeling accuracy for 52 PD patients and 20 healthy subjects. The result of this study indicates a great promise for developing a remote system to detect, monitor, and prescribe PD patients over long distances. It will be a tremendous help for the older population in developing countries where access to a trained neurologist is very limited. Also, in a pandemic situation like COVID-19, patients from developed countries can be benefited from such a home-oriented PD detection and monitoring system.

关键词： Parkinson's disease Tremor and gait analysis UPDRS label accelerometer sensor Machine learning

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial accelerometer sensor for Accurate Activity Recognition

引用

sensorS 2020年第8期20卷 2216页

作者： Javed, Abdul Rehman Sarwar, Muhammad Usman Khan, Suleman Iwendi, Celestine Mittal, Mohit Kumar, Neeraj Air Univ Natl Ctr Cyber Secur Islamabad 44000 Pakistan Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad 44000 Pakistan Cent South Univ Forestry & Technol Dept Elect BCC Changsha 410004 Peoples R China Kyoto Sangyo Univ Dept Informat Sci & Engn Kyoto 6038555 Japan Thapar Inst Engn & Technol Comp Sci & Engn Patiala 147001 Punjab India

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

关键词： activity recognition smartphone accelerometer sensor smart health

来源：评论

学校读者我要写书评

暂无评论

A Method for Predicting Human Walking Patterns using Smartphone's accelerometer sensor

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第7期11卷 384-391页

作者： Alhalhouli, Zaid T. Tafila Tech Univ TTU Comp & Informat Technol Dept Tafila 17966110 Jordan

Recently, the techniques for monitoring and recognizing human walking patterns have become one of the most important research topics, especially in health applications related to fitness and disease progression. This paper aims at combining machine learning techniques with Smartphone sensors readings (i.e. accelerometer sensor) in order to develop a smart model capable of classifying walking patterns into different categories (fast, normal, slow, very slow or very fast) along with variable of gender, male or female and sensor place, waist, hand or leg. In this paper, we use several machine learning algorithms including: Neural Network, KNN, Random forest, and Tree to train and test extracted data from Smartphone sensors. The results indicate that Smartphone sensor can be exploited in developing a reliable model for identifying the human walking patterns based on accelerometer readings. In addition, results show that Random forest is the best performing classifiers with an accuracy of (92.3%) and (91.8%) when applied on waist datasets for both males and females respectively.

关键词： Smartphone's accelerometer sensor walking patterns machine learning classifiers

来源：评论

学校读者我要写书评

暂无评论

In-Ear accelerometer-Based sensor for Gait Classification

引用

IEEE sensorS JOURNAL 2020年第21期20卷 12895-12902页

作者： Burgos, Clara Piris Gaertner, Lea Ballester, Miguel A. Gonzalez Noailly, Jerome Stoecker, Fabian Schonfelder, Martin Adams, Tim Tassani, Simone Pompeu Fabra Univ Dept Informat & Commun Technol Barcelona 08018 Spain Cosinuss GmbH D-81379 Munich Germany Tech Univ Munich Dept Sports & Hlth Sci Chair Exercise Biol D-80333 Munich Germany AKKA DSO GmbH D-80807 Munich Germany Pompeu Fabra Univ BCN MedTech DTIC Barcelona 08018 Spain ICREA Barcelona 08010 Spain Tech Univ Munich Dept Sport & Hlth Sci Teaching & Educ Lab D-80333 Munich Germany Cosinuss GmbH Software Team D-81379 Munich Germany

For several years, the detection of gait has been popularly implemented using wearable sensors, especially in the sports and medical areas. They are unobtrusive devices which allow to monitor individuals without the need of any ambulatory technology. Despite the fact, the optimal location of the sensor remains uncertain and dependent on the type of measurement. Ear-worn sensors provide a tactical position, robust against movement, that might be significant for gait classification. The purpose of this paper is to demonstrate the accuracy and reliability of in-ear accelerometer sensor to perform gait classification, between the activities walking and running. The data was collected from fourteen participants using an in-ear sensor called 'Cosinuss degrees One', which contains a three-dimensional accelerometer sensor. The main characteristics between these two activities were detected using 17 time domain features, as for instance the maximums and standard deviations of the 3-axes, and 3 different window sizes were evaluated: 3.75s, 2s and 1s. Support vector machine (SVM) and k-nearest neighbors (KNN) classifiers were implemented and later compared. The total number of features was reduced to 6 for SVM and 12 for KNN preserving the same results. An accuracy over 99% for both classifiers was achieved, outperforming most of the previous studies.

关键词： accelerometer sensor gait human activity recognition in-ear sensor k-nearest neighbors support vector machines wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Applied a multi-scale entropy algorithm to analyze dynamic COP signal via accelerometer sensor 13

Applied a multi-scale entropy algorithm to analyze dynamic C...

引用

13th IEEE Int Conf on Ubiquitous Intelligence and Comp/13th IEEE Int Conf on Adv and Trusted Comp/16th IEEE Int Conf on Scalable Comp and Commun/IEEE Int Conf on Cloud and Big Data Comp/IEEE Int Conf on Internet of People/IEEE Smart World Congress

作者： Lu, Shih-Hui Shieh, Jiann-Shing Hansen, Clint Yuan Ze Univ Ctr Big Data & Digital Convergence Dept Mech Engn & Innovat Chungli 320 Taiwan Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore

ISBN: (纸本)9781509027712

In this study, we use an accelerometer sensor to measure human postural signals during a dynamic task. In previous studies, the step stability index (SSI) has been used to assess human balance via similar sensors. However, the placement of the 3 dimensional sensor differed and placed on the lower back of the subjects. To improve the detection method, we placed the accelerometer on the shoe. The purpose of this research is to compute the SSI and to evaluate whether human balance can be objectively measured with an accelerometer. The results are compared with dynamic centre of pressure (COP) measurements obtained from a force plate that serves as the ground truth. The complexity index (CI) is calculated from dynamic COP to compare with the results of SSI.

关键词： falling noninvasive physiological signs accelerometer sensor multi-scale entropy

来源：评论

学校读者我要写书评

暂无评论

VLC-Based Positioning System for an Indoor Environment Using an Image sensor and an accelerometer sensor

引用

sensorS 2016年第6期16卷 783-783页

作者： Phat Huynh Yoo, Myungsik Soongsil Univ Sch Elect Engn Seoul 06978 South Korea

Recently, it is believed that lighting and communication technologies are being replaced by high power LEDs, which are core parts of the visible light communication (VLC) system. In this paper, by taking advantages of VLC, we propose a novel design for an indoor positioning system using LEDs, an image sensor (IS) and an accelerometer sensor (AS) from mobile devices. The proposed algorithm, which provides a high precision indoor position, consists of four LEDs mounted on the ceiling transmitting their own three-dimensional (3D) world coordinates and an IS at an unknown position receiving and demodulating the signals. Based on the 3D world coordinates and the 2D image coordinate of LEDs, the position of the mobile device is determined. Compared to existing algorithms, the proposed algorithm only requires one IS. In addition, by using an AS, the mobile device is allowed to have arbitrary orientation. Last but not least, a mechanism for reducing the image sensor noise is proposed to further improve the accuracy of the positioning algorithm. A simulation is conducted to verify the performance of the proposed algorithm.

关键词： indoor positioning visible light communication LED image sensor accelerometer sensor photodiode (PD)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：