Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255820
T. Carvalho, C. Costa, J. Mombach, Cristiane B. R. Ferreira, D. Fernandes, Fabrízzio Soares
In order to provide good musical performance to the piano, the wrist movement is very important during the performance of experienced players. However, to acquire proficiency in this technique is not a simple task since it is not simple to find formal representation, such as sheet music, to provide information about body movements. Moreover, piano teachers have to spend lots of effort to demonstrate an exercise to a single student, while many times they have to deal with many students in a classroom. Therefore, learning this approach requires not only observing experienced players but also, try to reproduce their execution to get practice. In this work, we propose PETA-System: a computer aid tool for teaching wrist movements on piano playing. The tool provides an interactive interface on which a tutor can record video musical excerpts to be performed by a learner. To evaluate the system, we carried out tests with actual learners to verify the improvement of the learning experience of wrist movement. A result has shown that the tool provided a stimulating learning environment for the student while reducing teacher efforts since they can share a time monitoring many students at the same time rather than a single one.
{"title":"PETA-System – A Piano Expression Teaching Aid System","authors":"T. Carvalho, C. Costa, J. Mombach, Cristiane B. R. Ferreira, D. Fernandes, Fabrízzio Soares","doi":"10.1109/CCECE47787.2020.9255820","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255820","url":null,"abstract":"In order to provide good musical performance to the piano, the wrist movement is very important during the performance of experienced players. However, to acquire proficiency in this technique is not a simple task since it is not simple to find formal representation, such as sheet music, to provide information about body movements. Moreover, piano teachers have to spend lots of effort to demonstrate an exercise to a single student, while many times they have to deal with many students in a classroom. Therefore, learning this approach requires not only observing experienced players but also, try to reproduce their execution to get practice. In this work, we propose PETA-System: a computer aid tool for teaching wrist movements on piano playing. The tool provides an interactive interface on which a tutor can record video musical excerpts to be performed by a learner. To evaluate the system, we carried out tests with actual learners to verify the improvement of the learning experience of wrist movement. A result has shown that the tool provided a stimulating learning environment for the student while reducing teacher efforts since they can share a time monitoring many students at the same time rather than a single one.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130096063","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255734
M. Al-Fayyad, K. Abugharbieh
An ultra-low power 7T -based SRAM system is proposed. The seven-transistor cells are used with write and read wordlines boost assist circuits: WWLB and RWLB. A low power switching PMOS sense amplifier (SPSA) is also presented. The read and write assist circuits utilize charge pumps that generate voltages above VDD and below ground to improve speed of operation. The proposed system works properly at a very low supply voltage equal to 0.3 V. For a 32 Kb system, typical power and energy consumption are 0.147 mW and 3.82 pJ, respectively. The operating frequency is 15.6 MHz and the static noise margin, SNM, is 55mV. All circuits were simulated in Hspice using 28nm CMOS technology devices.
{"title":"A 0.3V 15.6MHz 7T SRAM with Boosted Write and Read Worldlines","authors":"M. Al-Fayyad, K. Abugharbieh","doi":"10.1109/CCECE47787.2020.9255734","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255734","url":null,"abstract":"An ultra-low power 7T -based SRAM system is proposed. The seven-transistor cells are used with write and read wordlines boost assist circuits: WWLB and RWLB. A low power switching PMOS sense amplifier (SPSA) is also presented. The read and write assist circuits utilize charge pumps that generate voltages above VDD and below ground to improve speed of operation. The proposed system works properly at a very low supply voltage equal to 0.3 V. For a 32 Kb system, typical power and energy consumption are 0.147 mW and 3.82 pJ, respectively. The operating frequency is 15.6 MHz and the static noise margin, SNM, is 55mV. All circuits were simulated in Hspice using 28nm CMOS technology devices.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125985794","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255829
Soumava Bhattacharjee, Sukanta Halder, Animesh Kundu, K. Iyer, N. Kar
Wide–bandgap (WBG) device based high–frequency inverters using Gallium Nitride (GaN) switches are gaining significant research attention in the field of electric vehicles (EVs) due to their potential to operate at higher switching frequencies with improved efficiency as compared to the available power devices. However, the computation time of the control algorithm plays a significant role in the effective control and operation of such high–frequency converters. This paper presents an advanced artificial neural network (ANN) based improved space vector pulse width modulation (SVPWM) control for Gallium Nitride based inverter in EV application. The proposed neural–network (NN) based control technique has two core objectives: the first objective is to overcome the processing speed of the complex algorithm during high switching frequency operation and hence reduce the computation time which is the major challenge in WBG device–based inverter control. The second objective is to minimize the GaN inverter switching losses and to improve the overall performance of the inverter. The NN based SVPWM is trained using the reference voltage to get the modulated signal for pulse generation, thereby reducing the computation time and improving the performance of the inverter. The proposed ANN–based improved switching strategy has been validated experimentally using a GaN inverter and a comparative performance analysis with a conventional SVPWM technique is presented in this paper.
{"title":"Artificial Neural Network Based Improved Modulation Strategy for GaN–based Inverter in EV","authors":"Soumava Bhattacharjee, Sukanta Halder, Animesh Kundu, K. Iyer, N. Kar","doi":"10.1109/CCECE47787.2020.9255829","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255829","url":null,"abstract":"Wide–bandgap (WBG) device based high–frequency inverters using Gallium Nitride (GaN) switches are gaining significant research attention in the field of electric vehicles (EVs) due to their potential to operate at higher switching frequencies with improved efficiency as compared to the available power devices. However, the computation time of the control algorithm plays a significant role in the effective control and operation of such high–frequency converters. This paper presents an advanced artificial neural network (ANN) based improved space vector pulse width modulation (SVPWM) control for Gallium Nitride based inverter in EV application. The proposed neural–network (NN) based control technique has two core objectives: the first objective is to overcome the processing speed of the complex algorithm during high switching frequency operation and hence reduce the computation time which is the major challenge in WBG device–based inverter control. The second objective is to minimize the GaN inverter switching losses and to improve the overall performance of the inverter. The NN based SVPWM is trained using the reference voltage to get the modulated signal for pulse generation, thereby reducing the computation time and improving the performance of the inverter. The proposed ANN–based improved switching strategy has been validated experimentally using a GaN inverter and a comparative performance analysis with a conventional SVPWM technique is presented in this paper.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127747632","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255683
N. Reginald, Jaho Seo, Abdullah Rasul
This study proposes an effective control strategy for autonomous excavation under complex ground conditions, by integrating position, contour, and force control that are mutually associated factors. For the position control strategy, a non-linear PI controller was devised to control the stroke of each hydraulic cylinder, and therefore the bucket tip's position. To compensate for the ground resistive forces in contact space, an impedance controller was designed. Finally, contour compensation was considered to generate an optimal path of the bucket tip for ground leveling tasks. The performance of developed control algorithms was evaluated in the case of ground leveling task through co-simulation in multi-physics domains. Simulation results show that the designed control scheme provides good results in terms of transient response and tracking accuracy by dealing with all the aspects of force, position, and contour compensation.
{"title":"Tracking Control of Force, Position, and Contour for an Excavator with Co-simulation","authors":"N. Reginald, Jaho Seo, Abdullah Rasul","doi":"10.1109/CCECE47787.2020.9255683","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255683","url":null,"abstract":"This study proposes an effective control strategy for autonomous excavation under complex ground conditions, by integrating position, contour, and force control that are mutually associated factors. For the position control strategy, a non-linear PI controller was devised to control the stroke of each hydraulic cylinder, and therefore the bucket tip's position. To compensate for the ground resistive forces in contact space, an impedance controller was designed. Finally, contour compensation was considered to generate an optimal path of the bucket tip for ground leveling tasks. The performance of developed control algorithms was evaluated in the case of ground leveling task through co-simulation in multi-physics domains. Simulation results show that the designed control scheme provides good results in terms of transient response and tracking accuracy by dealing with all the aspects of force, position, and contour compensation.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"98 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016035","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255826
Haijun Gao, Japjot Singh Bawa, R. Paranjape
Network densification is an important topic which has been studied during the past decades in the 4G heterogeneous networks (HetNets). Deployment of small cells and cell-splitting technique are aimed to increase network capacity, cell coverage, and total cell throughput in HetNets. However, most published literature is about theoretical analysis. In this paper, extensive measurements are conducted in a real-world LTE-A HetNet environment. The cell-splitting strategy is applied in a real-world LTE-A HetNet. Four directional antennas operate as one cell and two cells respectively in an indoor gymnasium in the University of Regina. Optimization techniques such as ABS (Almost Blank Subframe) are utilized to mitigate interference and increase UE (user equipment) SINR inside the gymnasium. Users' (both static users and moving users) average SINR and system cell throughput are used to evaluate the performance of the tests. Our results show that operating the small cells from one cell to three cells for the whole building, the SINR inside the gymnasium decreased from 29 dB to 5 dB, and cell throughput decreased from 140 Mbps to 88Mbps. Even though the throughput performance of cells inside the gymnasium is slightly lowered, the overall network capacity of the building is enhanced.
{"title":"Measurement and Analysis of Small Cell Splitting in a Real-world LTE-A HetNet","authors":"Haijun Gao, Japjot Singh Bawa, R. Paranjape","doi":"10.1109/CCECE47787.2020.9255826","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255826","url":null,"abstract":"Network densification is an important topic which has been studied during the past decades in the 4G heterogeneous networks (HetNets). Deployment of small cells and cell-splitting technique are aimed to increase network capacity, cell coverage, and total cell throughput in HetNets. However, most published literature is about theoretical analysis. In this paper, extensive measurements are conducted in a real-world LTE-A HetNet environment. The cell-splitting strategy is applied in a real-world LTE-A HetNet. Four directional antennas operate as one cell and two cells respectively in an indoor gymnasium in the University of Regina. Optimization techniques such as ABS (Almost Blank Subframe) are utilized to mitigate interference and increase UE (user equipment) SINR inside the gymnasium. Users' (both static users and moving users) average SINR and system cell throughput are used to evaluate the performance of the tests. Our results show that operating the small cells from one cell to three cells for the whole building, the SINR inside the gymnasium decreased from 29 dB to 5 dB, and cell throughput decreased from 140 Mbps to 88Mbps. Even though the throughput performance of cells inside the gymnasium is slightly lowered, the overall network capacity of the building is enhanced.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123427802","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255701
B. M. Rocha, G. S. Vieira, Afonso U. Fonseca, H. Pedrini, N. M. Sousa, Fabrízzio Soares
Sugarcane is one of the main crops in the world due to the economic value it promotes by selling its derivatives. A diversity of technologies has been developed to optimize agricultural activities and maximize the productivity of sugarcane crops. In this sense, our primary goal is to contribute to this research area by detecting planting lines and measuring their faults, including the evaluation of curved lines that substantially limit numerous solutions in practical applications. An automatic method that identifies and measures sugarcane planting lines through digital image processing techniques and machine learning algorithms is presented. The proposal is evaluated using a database of real scene images, which were classified by K-Nearest Neighbors (KNN) and prepared with the support of a small unmanned aerial vehicle (UAV). Experimental tests show a low relative error of approximately 1.65% compared to manual mapping in the planting regions. It means that our proposal can identify and measure planting lines accurately, which enables automated inspections with high precision measurements.
{"title":"Evaluation and Detection of Gaps in Curved Sugarcane Planting Lines in Aerial Images","authors":"B. M. Rocha, G. S. Vieira, Afonso U. Fonseca, H. Pedrini, N. M. Sousa, Fabrízzio Soares","doi":"10.1109/CCECE47787.2020.9255701","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255701","url":null,"abstract":"Sugarcane is one of the main crops in the world due to the economic value it promotes by selling its derivatives. A diversity of technologies has been developed to optimize agricultural activities and maximize the productivity of sugarcane crops. In this sense, our primary goal is to contribute to this research area by detecting planting lines and measuring their faults, including the evaluation of curved lines that substantially limit numerous solutions in practical applications. An automatic method that identifies and measures sugarcane planting lines through digital image processing techniques and machine learning algorithms is presented. The proposal is evaluated using a database of real scene images, which were classified by K-Nearest Neighbors (KNN) and prepared with the support of a small unmanned aerial vehicle (UAV). Experimental tests show a low relative error of approximately 1.65% compared to manual mapping in the planting regions. It means that our proposal can identify and measure planting lines accurately, which enables automated inspections with high precision measurements.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127682867","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 : 2020-08-30DOI: 10.1109/ccece47787.2020.9255740
{"title":"Index","authors":"","doi":"10.1109/ccece47787.2020.9255740","DOIUrl":"https://doi.org/10.1109/ccece47787.2020.9255740","url":null,"abstract":"","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117353430","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255714
Thangarajah Akilan, E. Johnson, Gaurav Taluja, Japneet Sandhu, Ritika Chadha
Simultaneous Localization And Mapping (SLAM) is used to predict the trajectory by the Autonomous Navigation Robots (ANR), for instance Self-Driving Cars (SDC). It computes the trajectory through sensing the surroundings, like a visual perception of the environment. This work focuses on the performance improvements of a SLAM model using multimodal learning: (i), early fusion via layer weight enhancement of feature extractors, and (ii), late fusion via score refinement of the trajectory (pose) regressor. The comparative analysis on Apolloscape dataset shows that the proposed fusion strategies improve localization performance significantly. This work also evaluates applicability of various Deep Convolutional Neural Networks (DCNNs) for SLAM.
{"title":"Multimodality Weight and Score Fusion for SLAM","authors":"Thangarajah Akilan, E. Johnson, Gaurav Taluja, Japneet Sandhu, Ritika Chadha","doi":"10.1109/CCECE47787.2020.9255714","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255714","url":null,"abstract":"Simultaneous Localization And Mapping (SLAM) is used to predict the trajectory by the Autonomous Navigation Robots (ANR), for instance Self-Driving Cars (SDC). It computes the trajectory through sensing the surroundings, like a visual perception of the environment. This work focuses on the performance improvements of a SLAM model using multimodal learning: (i), early fusion via layer weight enhancement of feature extractors, and (ii), late fusion via score refinement of the trajectory (pose) regressor. The comparative analysis on Apolloscape dataset shows that the proposed fusion strategies improve localization performance significantly. This work also evaluates applicability of various Deep Convolutional Neural Networks (DCNNs) for SLAM.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965568","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255809
Osama Bondoq, K. Abugharbieh, Abdullah Hasan
This work proposes a novel master-slave latch D-type Flip-Flop. It consists of a reset-set slave latch and an asymmetrical single data input master latch. By reducing the number of stages and removing signal conditioning circuitry in the master latch, setup time has been significantly reduced and power consumption has improved. The proposed flip-flop is competitive to other state of the art low power flip-flops in addition to the conventional Transmission Gate Flip-flop (TGFF) in terms of performance, power consumption and area. In simulations, the proposed flip-flop has achieved up to 71.5% improvement in setup time, 36.5% improvement in D-Q delay time and up to 56.5% less power delay product (PDP) with 10% data activity compared to Topologically Compressed Flip-Flop (TCFF), which is a low power flip-flop. Further, it has achieved 11% smaller circuit area compared with TGFF. This work includes the proposed flip-flop's circuit schematic, layout design and simulations using Hspice tool with 28nm CMOS technology and a 1V supply voltage at 1 GHz clock (CLK).
{"title":"A D-Type Flip-Flop with Enhanced Timing Using Low Supply Voltage","authors":"Osama Bondoq, K. Abugharbieh, Abdullah Hasan","doi":"10.1109/CCECE47787.2020.9255809","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255809","url":null,"abstract":"This work proposes a novel master-slave latch D-type Flip-Flop. It consists of a reset-set slave latch and an asymmetrical single data input master latch. By reducing the number of stages and removing signal conditioning circuitry in the master latch, setup time has been significantly reduced and power consumption has improved. The proposed flip-flop is competitive to other state of the art low power flip-flops in addition to the conventional Transmission Gate Flip-flop (TGFF) in terms of performance, power consumption and area. In simulations, the proposed flip-flop has achieved up to 71.5% improvement in setup time, 36.5% improvement in D-Q delay time and up to 56.5% less power delay product (PDP) with 10% data activity compared to Topologically Compressed Flip-Flop (TCFF), which is a low power flip-flop. Further, it has achieved 11% smaller circuit area compared with TGFF. This work includes the proposed flip-flop's circuit schematic, layout design and simulations using Hspice tool with 28nm CMOS technology and a 1V supply voltage at 1 GHz clock (CLK).","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127931545","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255672
Anas Ibrahim, Yue Zhou, M. Jenkins, M. Naish, A. L. Trejos
The study of the characteristics and behaviour of tremor for people suffering from Parkinson's disease (PD) is an important first step in developing a new method to predict future tremor signals, their onset and the active tremor instances. The current approaches to detect tremor are limited to tremor estimators that rely on simple tremor models, or on deep brain probing that is invasive in nature. Thus, a new method that is noninvasive and that can capture tremor complexity to predict when tremor is active is needed. In this work, a new approach is presented using neural networks (NNs) and data from inertial measurement units (IMUs) to predict tremor onset and classify the active tremor instances in the wrist and metacarpophalangeal (MCP) joints of the index finger and thumb. The developed model showed an accuracy of 92.9% in predicting and detecting tremor onset, and therefore can be considered a reliable tool that has the potential to be integrated with wearable assistive devices for suppressing tremor.
{"title":"Parkinson's Tremor Onset Detection and Active Tremor Classification Using a Multilayer Perceptron","authors":"Anas Ibrahim, Yue Zhou, M. Jenkins, M. Naish, A. L. Trejos","doi":"10.1109/CCECE47787.2020.9255672","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255672","url":null,"abstract":"The study of the characteristics and behaviour of tremor for people suffering from Parkinson's disease (PD) is an important first step in developing a new method to predict future tremor signals, their onset and the active tremor instances. The current approaches to detect tremor are limited to tremor estimators that rely on simple tremor models, or on deep brain probing that is invasive in nature. Thus, a new method that is noninvasive and that can capture tremor complexity to predict when tremor is active is needed. In this work, a new approach is presented using neural networks (NNs) and data from inertial measurement units (IMUs) to predict tremor onset and classify the active tremor instances in the wrist and metacarpophalangeal (MCP) joints of the index finger and thumb. The developed model showed an accuracy of 92.9% in predicting and detecting tremor onset, and therefore can be considered a reliable tool that has the potential to be integrated with wearable assistive devices for suppressing tremor.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131522172","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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