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RSI/ISM International Conference on robotics and mechatronics (ICROM)

作者： Sepideh Etaati Fatemeh Javaherat Mehdi Delrobaei Department of Mechatronics Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Advanced Robotics and Automated Systems lab. Faculty of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Ahstract- This study focuses on developing a gait asymmetry index and applying image processing techniques to cyclogram analysis. We evaluated gait asymmetry under different walking conditions, specifically examining the effects of wearing a knee brace and holding a weight in hand. Using inertial measurement units (IMUs), we collected gait data to analyze knee, hip, and ankle angles in the sagittal plane. The joint kinematics data were assessed using our proposed asymmetry index, which combines image processing techniques and linear regression models. The proposed asymmetry index incorporates cyclogram area, trend symmetry, and the longest length of the cyclogram shape, effectively capturing variations in symmetry and providing a comprehensive measure. The results demonstrate that the proposed asymmetry index can effectively distinguish between different gait conditions. The findings potentially offer insights for researchers and clinicians in human biomechanics, aiding in the planning of interventions and the assessment of motion patterns, as well as for developers of bipedal walking robots in the field of robotic locomotion, enhancing their ability to design hardware and evaluate motion patterns.

关键词： Legged locomotion Knee Biomechanics Limbs Shape Image processing Market research Indexes Robots Hip

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Lateral gait analysis of a crawling robot by means of controlling the lengths of links and friction in the supports

Lateral gait analysis of a crawling robot by means of contro...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Vorochaeva, Lyudmila Savin, Sergei Hamed, Hany Southwest State University Dept. of Mechanics Mechatronics and Robotics Kursk Russia Innopolis University Lab. of Mechatronics Control and Prototyping Innopolis Russia

ISBN: (纸本)9781728187631

The present paper describes three developed designs of multi-link crawling robots intended for movement on rough surfaces with obstacles. The characteristic feature of the first design is the ability to control the friction coefficients in the supports of the central link, the second design is supplemented by the ability to lift off the side links in order to "step over"obstacles. The third design is distinguished by the ability to control the friction coefficients in the supports of all links, as well as the lengths of the links in addition to the implementation of the function "stepping over"obstacles. A comparative description of the implementation of the lateral gait by the robots of all the designs is presented. The operation of the control system for the lengths of the robot's links is described, which is based on a sequential algorithm for changing the length of the first central link and then the side links when the central one reaches its shortest length. © 2020 IEEE.

关键词： Machine design

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Autonomous cricothyroid membrane detection using neural networks for first-aid surgical airway management

Autonomous cricothyroid membrane detection using neural netw...

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ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020

作者： Han, Xiaoxue Ren, Hailin Ben-Tzvi, Pinhas Robotics and Mechatronics Lab Department of Mechanical Engineering Virginia Tech BlacksburgVA24060 United States

ISBN: (纸本)9780791883990

Airway management is one of the most important priorities when dealing with patients with severe injuries, but knowledge of the important anatomy and physiology is needed for providers to perform a successful surgery . This paper provides a solution for the precise cricothyroid membrane detection problem for real-time surgical airway management applications. With a commercial compact and portable cricothyrotomy kit, the proposed method will enable providers with general knowledge to perform successful first-aid airway management. In this paper, we propose a Hybrid Neural Network (HNNet), consisting of two parallel computing ensembles. The first ensemble takes as an input a low-resolution global image and outputs the Region-of-Interest (ROI) from the predefined grids. The high-resolution image is then cropped according to the ROI, and fed into the second ensemble to achieve precise keypoint detection. Global features and their spatial information from the first ensemble are also fed into the second ensemble to improve the precision. A dataset that consists of over 16,000 images from 13 subjects is built, and the location of the cricothyroid membrane in each image is precisely labeled by medical experts. The training results are presented to show both the efficiency and improved performance of our proposed method compared to existing ones. Copyright © 2020 ASME

关键词： Image segmentation

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Sensor based target tracking with application to autonomous docking and self-reconfigurability

Sensor based target tracking with application to autonomous ...

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ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020

作者： Sohal, Shubhdildeep S. Ben-Tzvi, Pinhas Robotics and Mechatronics Lab Mechanical Engineering Dept. Virginia Tech BlacksburgVA United States

ISBN: (纸本)9780791883990

This paper presents a target detection technique, which combines a supervised learning model with sensor data to eliminate false positives for a given input image frame. Such a technique aids with selective docking procedures where multiple robots are present in the environment. Hence the sensor data provides additional information for this decision making process. Senor accuracy plays a crucial role when the motion of the robot is defined by the use of data recorded by its sensors. The uncertainties in the sensory data can cause misalignments due to poor calibration of the sensor, which can result in poor positioning of the robot relative to its target. Such misalignments can play a significant role where certain accuracy is desired. Therefore, it is necessary to minimize such misalignments to achieve certainty for the robot interaction with its target. The work proposed in this paper allows achieving such accuracy using a vision-based approach by eliminating all false occurrences leading to selective interactions with the target. The proposed methodology is validated using a self-reconfigurable mobile robot capable of hybrid Wheeled-Tracked mobility, as an application towards autonomous docking of mobile robotic modules. Copyright © 2020 ASME

关键词： Target tracking

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Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

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Computers, Materials & Continua 2022年第1期70卷 535-555页

作者： Arshia Arif M.Jawad Khan Kashif Javed Hasan Sajid Saddaf Rubab Noman Naseer Talha Irfan Khan National University of Sciences and Technology(NUST) IslamabadPakistan Intelligent Robotics Lab National Center of Artificial IntelligenceNational University of Sciences and Technology(NUST)IslamabadPakistan Department of Mechatronics Engineering Air UniversityIslamabadPakistan Institute of Space Technology IslamabadPakistan

For BCI systems,it is important to have an accurate and less complex architecture to control a device with enhanced *** this paper,a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface(BCI).An integrated classifier has been developed for achieving better classification accuracy using two *** integrated EEG-fNIRS-based vector-phase analysis(VPA)has been *** open-source dataset collected at the TechnischeUniversit鋞Berlin,including simultaneous electroencephalography(EEG)and functional near-infrared spectroscopy(fNIRS)signals of 26 healthy participants during n-back tests,has been used for this *** and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both *** resting state threshold circle,VPA has been used to detect a hemodynamic response in fNIRS signals,whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each *** threshold circles are drawn in the vector plane,where each circle is drawn after task completion in each trial of EEG ***,both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for *** of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy,that is 91.35%,as compared to other classifiers such as support vector machine(SVM),convolutional neural networks(CNN),deep neural networks(DNN)and VPA(with dual-threshold circles)with classification accuracies 82%,89%,87%and 86%*** of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.

关键词： EEG-fNIRS hybrid BCI vector-phase analysis hemodynamic response detection

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Classification of hand-related real and imaginary motor activity with fNIRS 4

Classification of hand-related real and imaginary motor acti...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Hramov, Alexander Grubov, Vadim Badarin, Artem Center for Technologies in Robotics and Mechatronics Components Innopolis University Neuroscience and Cognitive Technology Lab Innopolis Russia

ISBN: (纸本)9781728172866

In the present work we studied blood oxygenation/deoxygenation spatial dynamics related to real and imaginary motor activity using functional near-infrared spectroscopy (fNIRS). We revealed biomarkers based on pronounced hemispheric lateralization of hemodynamical response in the motor cortex during motor activity. We used these markers to design a sensing method for classification of movement's type. The accuracy of real movements classification was close to 100%, while for imaginary movements it was lower but still quite high (about 90%). The proposed system can find application, for example, in neurorehabilitation after severe brain injuries, including traumas and strokes. © 2020 IEEE.

关键词： Infrared devices

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Detecting inter-areal functional connectivity using artificial neural network 4

Detecting inter-areal functional connectivity using artifici...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Pitsik, Elena Frolov, Nikita Innopolis University Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (数字)9781728172866

ISBN: (纸本)9781728172866

In present paper we introduce an extended machine-learning-based approach to detect inter-areal functional connectivity based on artificial neural network (ANN). We prove the efficiency of the proposed method by applying multi-layer perceptron to find functional relations between the cortex and thalamus of the pathological WAG/Rij rats during the epileptic seizure based on the multivariate electrocorticography (ECoG) recordings. We show that the proposed algorithm is able to reconstruct the increased coupling within a thalamo-cortical network during the seizure versus a baseline activity. © 2020 IEEE.

关键词： Multilayer neural networks

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Development of the approach to collaborative BCI 4

Development of the approach to collaborative BCI

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Grubov, Vadim Maksimenko, Vladimir Nedaivozov, Vladimir Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728172866

In the present study we aim to find specific characteristic based on brain activity, that can be used to evaluate attention and, thus, can be used in brain-computer interface. We propose an approach to collaborative BCI aimed to enhance human-to-human interaction while performing shared visual task. We also describe the setup for such BCI and its possible application in long task of classifying ambiguous visual stimuli with varying degrees of ambiguity by a group of people. © 2020 IEEE.

关键词： Brain

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Estimating elementary cognitive functions based on EEG signals analysis 4

Estimating elementary cognitive functions based on EEG signa...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Kuc, Alexander Grubov, Vadim Maksimenko, Vladimir Makarov, Vladimir Center for Technologies in Robotics and Mechatronics Components Innopolis University Neuroscience and Cognitive Technology Lab Innopolis Russia

ISBN: (纸本)9781728172866

Human performance in complex mental tasks relies on elementary cognitive abilities, e.g., information processing speed and working memory capacity. These elementary functions can be measured on a behavioral level using elementary tasks, e.g., reaction time and memory scanning tasks. Cognitive and behavioral neuroscience aims to reveal cortical activity features underlying human performance in the elementary cognitive tasks. In this work, we designed a reaction time task based on the processing of ambiguous sensory stimuli. We found a correlation between the task performance on the behavioral level and the sensor-level cortical activity. © 2020 IEEE.

关键词： Signal analysis

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Design and Validation of Tunable Stiffness Actuator using Soft-Rigid Combined Layer Jamming Mechanism

Design and Validation of Tunable Stiffness Actuator using So...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Seoyeon Ham Brian Byunghyun Kang Kevin Abishek Hyunglae Lee Wansoo Kim Department of Interdisciplinary Robot Engineering Systems Hanyang University Gyeonggi-do Republic of Korea Neuromuscular Control and Human Robotics Lab. Arizona States University AZ USA Department of Intelligent Mechatronics Engineering Sejong University Seoul Republic of Korea Robotics Department Ansan-si Gyeonggi-do Republic of Korea

This paper presents a tunable stiffness actuator with a soft-rigid combined layer jamming mechanism. The tunable stiffness actuator is aimed to be integrated into an exosuit to prevent ankle sprain and avoid or mitigate the development of chronic ankle instability. The main purpose of the soft-rigid layer jamming mechanism is to produce large stiffness with a small volume and achieve a linear stiffness characteristic. To this end, the actuator is designed to include rigid retainer pieces within the soft silicone layers, and each soft-rigid layer is jammed to induce stiffness changes. To validate the stiffness characteristics of the proposed soft-rigid actuator, a series of experiments were performed and stiffness changes were investigated for varying jamming states from unjammed to fully jammed states. Increasing the number of jamming layer effectively increased the actuator stiffness, which was consistent with expected results from the analytical model. Soft-rigid actuator's stiffness at the fully jammed state was 212.1% and 123.1% higher than the unjammed state for one-side and both sides anchored conditions, respectively. Compared to the soft actuator without the rigid retainer, the soft-rigid actuator exhibited a more linear characteristic (Pearson correlation coefficient = 0.990 and 0.997 for one-side and both sides anchored conditions, respectively). Moreover, the soft-rigid actuator achieved significantly higher stiffness than the soft actuator in all jamming states (at least 41.3% increase in each jamming state). The results suggest a potential use of the tunable stiffness actuator to develop a soft ankle exosuit with highly variable but linear stiffness characteristics.

关键词： Correlation coefficient Actuators Analytical models Composite materials Mechanical cables Soft robotics Jamming

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