Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587636
S. Bharath, C. Khusi
This paper suggests a novel idea to implement a smart traffic management system in which real-time data are processed and stored in the database. A network of ultrasonic sensors are used to track traffic congestion at intersections on the road all day long. The information on traffic density is used to determine the set time of a signal, unlike the conventional way of the predefined set time. Internet of Things (IoT) technique is used to send the data from sensors to node-red through Message Queuing Telemetry Transport (MQTT) protocol where primary decision making is done. This system can be used in four-way or two-way junctions with few code amendments. A significant amount of waiting time is saved through the model.
{"title":"IoT Based Smart Traffic System Using MQTT Protocol: Node-Red Framework","authors":"S. Bharath, C. Khusi","doi":"10.1109/GCAT52182.2021.9587636","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587636","url":null,"abstract":"This paper suggests a novel idea to implement a smart traffic management system in which real-time data are processed and stored in the database. A network of ultrasonic sensors are used to track traffic congestion at intersections on the road all day long. The information on traffic density is used to determine the set time of a signal, unlike the conventional way of the predefined set time. Internet of Things (IoT) technique is used to send the data from sensors to node-red through Message Queuing Telemetry Transport (MQTT) protocol where primary decision making is done. This system can be used in four-way or two-way junctions with few code amendments. A significant amount of waiting time is saved through the model.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125221740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587554
Awadh Kishor Singh, Bintu Kadhiwala, Rakesh Patel
Character recognition is a technology that facilitates the conversion of different types of scanned documents into searchable and editable data. Since the decade of years, many researchers work on character recognition. It can be classified into hand-written character recognition and printed character recognition. Hand-written character recognition is considered to be a demanding research area in the field of pattern recognition. In this paper, we present a comprehensive survey on existing techniques for hand-written character recognition of Hindi scripts with the help of various parameters such as techniques utilized for pre-processing, feature extraction, classification, etc. This paper aims to provide an insight to researchers working in the domain of hand-written Hindi character recognition.
{"title":"Hand-written Hindi Character Recognition - A Comprehensive Review","authors":"Awadh Kishor Singh, Bintu Kadhiwala, Rakesh Patel","doi":"10.1109/GCAT52182.2021.9587554","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587554","url":null,"abstract":"Character recognition is a technology that facilitates the conversion of different types of scanned documents into searchable and editable data. Since the decade of years, many researchers work on character recognition. It can be classified into hand-written character recognition and printed character recognition. Hand-written character recognition is considered to be a demanding research area in the field of pattern recognition. In this paper, we present a comprehensive survey on existing techniques for hand-written character recognition of Hindi scripts with the help of various parameters such as techniques utilized for pre-processing, feature extraction, classification, etc. This paper aims to provide an insight to researchers working in the domain of hand-written Hindi character recognition.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133422722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587503
S. Shrikanth Rao, M. Kolekar, R. J. Martis
Atrial fibrillation (AF) is a disorder related to the heart. Irregularity of RR intervals and lack of P wave are the two main indicators of AF. Detection of AF using Electrocardiogram (ECG) remains one of the real challenges in the field of medical science. In this paper, we propose Discrete Wavelet Transform based method coupled with Deep Learning methods such as 2 layer Long Short Term Memory (LSTM) along with Gradient Recurrent Unit (GRU), 2 layer Bidirectional Long Short Term Memory (BiLSTM) along with Gradient Recurrent Unit (GRU) are used separately to classify the ECG signal into 3 classes namely: Normal, AF and other rhythms. Physionet challenge 2017 dataset is used for the study purpose. The results of LSTM and BiLSTM are compared with Support Vector Machine (SVM). The result indicated that LSTM provided improved performance compared to BiLSTM and SVM methods. The class specific accuracy of normal, AF and other rhythm are 96.92%, 97.36% and 96.39% respectively and Area Under the Curve (AUC) is 0.982. The overall accuracy of LSTM network is obtained as 96.94%. The developed technology has immense applications in medical devices.
{"title":"A Deep Learning Based Assisted Tool for Atrial Fibrillation Detection Using Electrocardiogram","authors":"S. Shrikanth Rao, M. Kolekar, R. J. Martis","doi":"10.1109/GCAT52182.2021.9587503","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587503","url":null,"abstract":"Atrial fibrillation (AF) is a disorder related to the heart. Irregularity of RR intervals and lack of P wave are the two main indicators of AF. Detection of AF using Electrocardiogram (ECG) remains one of the real challenges in the field of medical science. In this paper, we propose Discrete Wavelet Transform based method coupled with Deep Learning methods such as 2 layer Long Short Term Memory (LSTM) along with Gradient Recurrent Unit (GRU), 2 layer Bidirectional Long Short Term Memory (BiLSTM) along with Gradient Recurrent Unit (GRU) are used separately to classify the ECG signal into 3 classes namely: Normal, AF and other rhythms. Physionet challenge 2017 dataset is used for the study purpose. The results of LSTM and BiLSTM are compared with Support Vector Machine (SVM). The result indicated that LSTM provided improved performance compared to BiLSTM and SVM methods. The class specific accuracy of normal, AF and other rhythm are 96.92%, 97.36% and 96.39% respectively and Area Under the Curve (AUC) is 0.982. The overall accuracy of LSTM network is obtained as 96.94%. The developed technology has immense applications in medical devices.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587533
Meghana A Rajeev
Cervical cancer is a cancer of the entrance to the uterus. Pap test has made cervical cancer quite possibly one of the most preventable types of cancer, which can be utilized for its initial identification. Be that as it may, the whole interaction is tedious, expensive and involves observer biases. In order to conquer existing challenges the work has developed an automated cervical cancer detection framework using the UDMHDC segmentation and MBD-RCNN classification algorithm. The proposed segmentation technique and MBDRCNN model provides accurate classification of cervical cancer along with low computational time.
{"title":"A Framework for Detecting Cervical Cancer Based on UD-MHDC Segmentation and MBD-RCNN Classification Techniques","authors":"Meghana A Rajeev","doi":"10.1109/GCAT52182.2021.9587533","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587533","url":null,"abstract":"Cervical cancer is a cancer of the entrance to the uterus. Pap test has made cervical cancer quite possibly one of the most preventable types of cancer, which can be utilized for its initial identification. Be that as it may, the whole interaction is tedious, expensive and involves observer biases. In order to conquer existing challenges the work has developed an automated cervical cancer detection framework using the UDMHDC segmentation and MBD-RCNN classification algorithm. The proposed segmentation technique and MBDRCNN model provides accurate classification of cervical cancer along with low computational time.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132699831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587496
Mayank Singhal, R. Agarwal
Generative Adversarial Networks (GANs) were first used to generate images that were similar to images in the data the model was trained on. The GANs training is based on a zero-sum game where the constituent models are adversaries. The mathematical interpretation of GAN training is the mapping of an unknown distribution to the dataset distribution. Future works in the field led to the generation of music, texts, and types of data and GANs still are being explored in scientific, entertainment, fashion, advertising, videogames, and other miscellaneous applications. This review focuses on the versatility of GANs. First, the GAN model is explored with its mathematical intuition. Then come the popular variants of GANs and their applications. Finally, the most recent applications of GANs in different fields are discussed, and the review ends with a discussion of future possible applications of GANs.
{"title":"Generative Adversarial Networks and their Miscellaneous Applications: A Review","authors":"Mayank Singhal, R. Agarwal","doi":"10.1109/GCAT52182.2021.9587496","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587496","url":null,"abstract":"Generative Adversarial Networks (GANs) were first used to generate images that were similar to images in the data the model was trained on. The GANs training is based on a zero-sum game where the constituent models are adversaries. The mathematical interpretation of GAN training is the mapping of an unknown distribution to the dataset distribution. Future works in the field led to the generation of music, texts, and types of data and GANs still are being explored in scientific, entertainment, fashion, advertising, videogames, and other miscellaneous applications. This review focuses on the versatility of GANs. First, the GAN model is explored with its mathematical intuition. Then come the popular variants of GANs and their applications. Finally, the most recent applications of GANs in different fields are discussed, and the review ends with a discussion of future possible applications of GANs.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127622721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587807
Miranji Katta, R. Sandanalakshmi
A microcantilever array chip made with Micro-Electro-Mechanical System (MEMS) technology has been demonstrated to develop as a biosensor device. This chip includes four gold-covered and embedded polysilicon wire with microfabricated Si beams. The polysilicon coat serves as a piezoresistor, and changes in resistance due to compressive and tensile forces indicate microcantilever deformation. The relationship between initial resistance and microcantilever deflection demonstrates that this device has a detection range of 0-56kΩ. The investigation of the microcantilever response to biotin immobilisation revealed that resistance change caused by Biotin absorption can be observed and reaches a degree of amount independence at Biotin concentrations higher than 80pg/ml. The results suggested that this device could be developed as a piezoresistive-based microcantilever biosensor.
{"title":"MEMS Piezoresistive Cantilever Fabrication And Characterization","authors":"Miranji Katta, R. Sandanalakshmi","doi":"10.1109/GCAT52182.2021.9587807","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587807","url":null,"abstract":"A microcantilever array chip made with Micro-Electro-Mechanical System (MEMS) technology has been demonstrated to develop as a biosensor device. This chip includes four gold-covered and embedded polysilicon wire with microfabricated Si beams. The polysilicon coat serves as a piezoresistor, and changes in resistance due to compressive and tensile forces indicate microcantilever deformation. The relationship between initial resistance and microcantilever deflection demonstrates that this device has a detection range of 0-56kΩ. The investigation of the microcantilever response to biotin immobilisation revealed that resistance change caused by Biotin absorption can be observed and reaches a degree of amount independence at Biotin concentrations higher than 80pg/ml. The results suggested that this device could be developed as a piezoresistive-based microcantilever biosensor.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"408 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115913217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587518
Vidhi Chhatbar, Mihir Gondhalekar, Shruti Pimple, R. Pawar
We come across different biomedical images. It is difficult to interpret those images as they do not have any description. Image captioning is the process of generating textual description from an image which depends on the object and action in the image. With the advancement in deep learning techniques, we will build models to generate captions for biomedical images. This model will be very useful to accelerate the diagnosis process by telling the abnormalities present in the image. The model will be based on an encoder-decoder framework along with an attention model. The encoder will be using deep CNN to extract image features and the decoder will be using transformers to generate captions. Caption generating involves different complex scenarios starting from collecting the data set, training the model, validating the model, creating trained model to test the image, detecting the image and generating the captions
{"title":"Machine Interpretation of Medical Images Using Deep Learning","authors":"Vidhi Chhatbar, Mihir Gondhalekar, Shruti Pimple, R. Pawar","doi":"10.1109/GCAT52182.2021.9587518","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587518","url":null,"abstract":"We come across different biomedical images. It is difficult to interpret those images as they do not have any description. Image captioning is the process of generating textual description from an image which depends on the object and action in the image. With the advancement in deep learning techniques, we will build models to generate captions for biomedical images. This model will be very useful to accelerate the diagnosis process by telling the abnormalities present in the image. The model will be based on an encoder-decoder framework along with an attention model. The encoder will be using deep CNN to extract image features and the decoder will be using transformers to generate captions. Caption generating involves different complex scenarios starting from collecting the data set, training the model, validating the model, creating trained model to test the image, detecting the image and generating the captions","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124151452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587529
Dilshan Singh Chadha, Kartikey Chaturvedi, M. D. Upadhayay
This work brings an innovative design of a flyswatter shaped antenna for an 8-element linear array with Butler Matrix (BM) as the beamforming network and also proposes four port cross-over. The proposed flyswatter shaped antenna resonates at a frequency of 2.4 GHz. The results of all components (such as quadrature couplers, crossovers, phase shifters) used to realize the design of beam forming network are presented. The proposed cross-over has insertion loss close to 2dB. The 8-element linear array is integrated on FR-4 substrate $left(varepsilon_{mathrm{r}}=4.3 quad text { and } quad text { height }=1.6 quad mathrm{~mm}right)$ with BM based beamforming network to produces eight different beams at $-55^{circ},-36,-21^{circ},-7^{circ}, 55^{circ}, 36,21^{circ}$, and 7°. The reflection coefficients and isolation at respective ports are less than -15 dB at the operating frequency and side lobes of radiation pattern are sufficiently low. This technique finds applications in IEEE 802.11 WLAN, lower frequency bands of 5 G and LTE, and wearable devices.
本文提出了一种以巴特勒矩阵(BM)作为波束形成网络的八元线性阵列的蜻蜓形天线的创新设计，并提出了四端口交叉。所提出的苍蝇拍形天线谐振频率为2.4 GHz。给出了用于实现波束形成网络设计的所有元件(如正交耦合器、交叉器、移相器)的结果。所提出的交叉具有接近2dB的插入损耗。将8元线性阵列集成在FR-4衬底$left(varepsilon_{mathrm{r}}=4.3 quad text { and } quad text { height }=1.6 quad mathrm{~mm}right)$上，采用基于BM的波束形成网络，在$-55^{circ},-36,-21^{circ},-7^{circ}, 55^{circ}, 36,21^{circ}$和7°处产生8种不同的波束。在工作频率下，各端口的反射系数和隔离度均小于-15 dB，辐射方向图旁瓣足够低。该技术适用于IEEE 802.11 WLAN、5g和LTE的较低频段以及可穿戴设备。
{"title":"Flyswatter Shaped Antenna for 8 Element Beam Forming Network utilizing Butler Matrix","authors":"Dilshan Singh Chadha, Kartikey Chaturvedi, M. D. Upadhayay","doi":"10.1109/GCAT52182.2021.9587529","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587529","url":null,"abstract":"This work brings an innovative design of a flyswatter shaped antenna for an 8-element linear array with Butler Matrix (BM) as the beamforming network and also proposes four port cross-over. The proposed flyswatter shaped antenna resonates at a frequency of 2.4 GHz. The results of all components (such as quadrature couplers, crossovers, phase shifters) used to realize the design of beam forming network are presented. The proposed cross-over has insertion loss close to 2dB. The 8-element linear array is integrated on FR-4 substrate $left(varepsilon_{mathrm{r}}=4.3 quad text { and } quad text { height }=1.6 quad mathrm{~mm}right)$ with BM based beamforming network to produces eight different beams at $-55^{circ},-36,-21^{circ},-7^{circ}, 55^{circ}, 36,21^{circ}$, and 7°. The reflection coefficients and isolation at respective ports are less than -15 dB at the operating frequency and side lobes of radiation pattern are sufficiently low. This technique finds applications in IEEE 802.11 WLAN, lower frequency bands of 5 G and LTE, and wearable devices.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114368111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587648
N. Kaur, Vijay KumarSinha, S. Kang
Autism is neurological disorder in which person is affected with communication and interaction abilities. Lacks of social interaction, repetitive behavior, and stable interest are indication of the autistic child. It essential to identify the autism at very is early stage. CNN plays vital role in health care which requires a process that reduces cost and time. The key objective of proposed paper is to implement convolution neural network algorithms and classify autistic and non-autistic child..In this study, CNN is applied for classification of autistic and non-autistic child. The images of children of age 4 to 11 years were used. About 400 images extracted from pre-defined datasets and were used to train the CNN algorithm using the Google colab framework via Python and Open CV libraries. Using cross validation techniques, The CNN was evaluated. In this sense, our proposed model has achieved a high accuracy rate and robustness for prediction of autistic and non-autistic child. Additionally, the proposed algorithm attains a quick response time. Therefore, we could significantly diminish the time of diagnosis by applying the proposed method and facilitate the diagnosis of ASD in lower cost.
{"title":"Early detection of ASD Traits in Children using CNN","authors":"N. Kaur, Vijay KumarSinha, S. Kang","doi":"10.1109/GCAT52182.2021.9587648","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587648","url":null,"abstract":"Autism is neurological disorder in which person is affected with communication and interaction abilities. Lacks of social interaction, repetitive behavior, and stable interest are indication of the autistic child. It essential to identify the autism at very is early stage. CNN plays vital role in health care which requires a process that reduces cost and time. The key objective of proposed paper is to implement convolution neural network algorithms and classify autistic and non-autistic child..In this study, CNN is applied for classification of autistic and non-autistic child. The images of children of age 4 to 11 years were used. About 400 images extracted from pre-defined datasets and were used to train the CNN algorithm using the Google colab framework via Python and Open CV libraries. Using cross validation techniques, The CNN was evaluated. In this sense, our proposed model has achieved a high accuracy rate and robustness for prediction of autistic and non-autistic child. Additionally, the proposed algorithm attains a quick response time. Therefore, we could significantly diminish the time of diagnosis by applying the proposed method and facilitate the diagnosis of ASD in lower cost.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114941062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587750
Andrian C. Monroy, Kurt Austin Padilla, Edwin R. Rillera, Jepthah D. Rodriguez, Kenneth Oliver Y. Tindugan, R. Tolentino
In this study, the proponents proposed a mechanism that provides abduction and adduction movements at the MCP joint of index finger and CMC joint of the thumb as well as full actuation in the movements of remaining joints whose human equivalents are capable of fully-independent movement. The system consists of two main digits: the thumb and the index finger. As a rundown, the digits are actuated by several HS35HD Micro Servo Motors, MG996R High-Torque Motor, and PQ-12R Micro Linear Servo. An aspect that can be noticed in the mechanism is the movement in the index PIP joint is actuated by a linear servo whose linear movement translated into rotational movement, with the mechanism allowing the distal phalange of the finger to move dependently of the middle phalange.A series of flex sensors attached on a glove was used to gather finger joint movement data made by the user. Mimicking happens as motors actuate according to the gathered data with the help of Arduino Mega 2560. To compare the angular positions actuated by the motors to that of the movements by the user flex sensors and potentiometer were utilized. The system’s mimicking capability is then evaluated using z-test.
在这项研究中，支持者提出了一种机制，该机制提供了食指MCP关节和拇指CMC关节的外展和内收运动，并在其他关节的运动中完全驱动，而这些关节的人体等效关节能够完全独立运动。该系统由两个主要手指组成:拇指和食指。作为概述，数字由几个HS35HD微伺服电机，MG996R高扭矩电机和PQ-12R微线性伺服驱动。在该机构中可以注意到的一个方面是指指关节的运动是由一个线性伺服驱动的，其线性运动转化为旋转运动，该机构允许手指的远端指骨依赖于中指骨运动。安装在手套上的一系列弯曲传感器用于收集用户手指关节的运动数据。在Arduino Mega 2560的帮助下，根据收集到的数据，电机会进行模拟。为了将电机驱动的角度位置与用户运动的角度位置进行比较，使用了弯曲传感器和电位器。然后使用z检验评估系统的模拟能力。
{"title":"Design and Implementation of Articulated Mimicking Robotic Finger with Abduction and Adduction Movements in the Index MCP Joint and Thumb CMC Joint","authors":"Andrian C. Monroy, Kurt Austin Padilla, Edwin R. Rillera, Jepthah D. Rodriguez, Kenneth Oliver Y. Tindugan, R. Tolentino","doi":"10.1109/GCAT52182.2021.9587750","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587750","url":null,"abstract":"In this study, the proponents proposed a mechanism that provides abduction and adduction movements at the MCP joint of index finger and CMC joint of the thumb as well as full actuation in the movements of remaining joints whose human equivalents are capable of fully-independent movement. The system consists of two main digits: the thumb and the index finger. As a rundown, the digits are actuated by several HS35HD Micro Servo Motors, MG996R High-Torque Motor, and PQ-12R Micro Linear Servo. An aspect that can be noticed in the mechanism is the movement in the index PIP joint is actuated by a linear servo whose linear movement translated into rotational movement, with the mechanism allowing the distal phalange of the finger to move dependently of the middle phalange.A series of flex sensors attached on a glove was used to gather finger joint movement data made by the user. Mimicking happens as motors actuate according to the gathered data with the help of Arduino Mega 2560. To compare the angular positions actuated by the motors to that of the movements by the user flex sensors and potentiometer were utilized. The system’s mimicking capability is then evaluated using z-test.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116011298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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