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2023 IEEE International Opportunity Research Scholars Symposium, ORSS 2023

作者： Butler, Carolyn Shah, Iti Kish, Jacob Salman, Muhammad Kennesaw State University Robotics & Mechatronics United States Kennesaw State University Department of Mechanical Engineering United States

ISBN: (纸本)9798350337006

Currently, there is no affordable, everyday use chiropractic force sensor. Devices that are used to teach chiropractic students, such as activators and drop tables, assist students in learning but do not give adequate feedback. Our design will be implementing the cheapest materials to make the most reliable force sensing glove which chiropractors can use for their daily work needs. Materials needed to complete this project include pressure sensors, electrical wires, resistors, a breadboard, Raspberry Pi Pico W, a laptop with Thonny software installed, and an analog to digital converter. Our design of the chiropractic force sensor glove will help reduce the amount of confusion when determining the correct amount of force to apply to a chiropractor's patient. This research will also help to dramatically reduce any severe pain resulting from misplaced forces and pressure applied to chiropractic patients. © 2023 IEEE.

关键词： Pressure sensors

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CNN coal and rock recognition method based on hyperspectral data

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International Journal of Coal Science & Technology 2022年第5期9卷 59-70页

作者： Jianjian Yang Boshen Chang Yuchen Zhang Wenjie Luo Shirong Ge Miao Wu School of Mechatronics and Information Engineering China University of Mining and Technology-BeijingBeijing 100083China Key Laboratory of Intelligent Mining and Robotics Ministry of Emergency ManagementBeijingChina

Aiming at the problem of coal gangue identifcation in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identifcation method based on hyperspectral ***,coal and rock spectrum data were collected by a near-infrared spectrometer,and then four methods were used to flter 120 sets of collected data:frst-order diferential(FD),second-order diferential(SD),standard normal variable transformation(SNV),and multi-style *** coal and rock refectance spectrum data were pre-processed to enhance the intensity of spectral refectance and absorption characteristics,as well as efectively remove the spectral curve noise generated by instrument performance and environmental factors.A CNN model was constructed,and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations(i.e.,the learning rate,the number of feature extraction layers,and the dropout rate)to generate the best CNN classifer for the hyperspectral data for rock *** experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%.Verifcation of the advantages and efectiveness of the method were proposed in this article.

关键词： Hyperspectral data Data pre-processing 1D-CNN Coal gangue identifcation

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Inpatient Monitoring System: Temperature, Oxygen Saturation, Blood Pressure, Heart Rate, and Infusion Automation Based on ESP 32, IoT, and Mobile Application 7

Inpatient Monitoring System: Temperature, Oxygen Saturation,...

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7th International Conference of Computer and Informatics engineering, IC2IE 2024

作者： Joseph, Irvanda Anthony, Patrick Astuti, Winda Lie, Zener Sukra Iwan Solihin, Mahmud Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Ucsi University Department of Mechanical and Mechatronics Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798331505530

The implementation of a patient monitoring system and infusion automation which aims to ensure regular and periodic monitoring of inpatient conditions are applied in this system. This embedded system is applied in hospitals and addresses the uneven distribution of healthcare workers in Indonesia, particularly in remote areas. Currently, manual checks by nurses can lead to errors and reduced patient comfort. The improvement supervision is developed in this work. The system monitored body temperature, oxygen saturation, blood pressure, heart rate, and automated infusions. All the embedded systems are connected to Internet of Things (IoT) and a real-time database accessible through an application and web. This system allows doctors and hospital authorities to make decisions based on patient condition history. The testing phase of the system shows four data acquisition devices (temperature, oxygen saturation, blood pressure, and heart rate), an ESP 32, and Firebase as a database, have ability to capture and send real-time data every five seconds and can be accessed by doctors, patients, and hospital authorities. © 2024 IEEE.

关键词： Remote patient monitoring

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A SIMPLE EDUCATIONAL INTELLIGENT SPEED DETECTION SYSTEM FOR ROAD VEHICULES

International Journal of Mechatronics and Applied Mechanics

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International Journal of mechatronics and Applied Mechanics 2022年第12期2022卷 6-11页

作者： Ciobanu, Alexandru Costin Alionte, Andreea Dana Ungureanu, Liviu Marian Alionte, Cristian Gabriel Munteanu, Iulian Sorin Badea, Sorin Ionuț Faculty of Industrial Engineering and Robotics University Politehnica of Bucharest Romania National Institute of Research and Development in Mechatronics and Measurement Technique-INCDMTM Romania

This paper presents an original detection system for measuring the speed of vehicles on a road used in the laboratory for educational and testing purposes. Although the maximum speed limit is specified for each type of road, accidents still occur due to unjustified exceeding of speed limits. Therefore, it was found that these problems could be solved with the help of electronic circuits. Thus, the paper presents the development of an original educational system for detecting the speed of a road vehicle, which consists of an Arduino UNO Rev3 microcontroller, two infrared sensors, a 1602A LCD and an electronic system specially used for the intelligent braking of the vehicle, using for this a wireless communication system. The entire original system will be integrated into an autonomous safety vehicle system. © 2022, Cefin Publishing House. All rights reserved.

关键词： Speed

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Advancement in Smart Cane Technology: Enhancing Mobility for the Visually Impaired Using ROS and LiDAR 6

Advancement in Smart Cane Technology: Enhancing Mobility for...

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6th International Conference on robotics and Automation in Industry, ICRAI 2024

作者： Zafar, Saqib Maqbool, Hafiz Farhan Ahmad, Naveed Ali, Arham Moeizz, Abdul Ali, Faizan Taborri, Juri Rossi, Stefano University of Tuscia Department of Economics Engineering Society and Business Organization VT Viterbo01100 Italy University of Engineering and Technology Lahore Department of Mechanical Mechatronics and Manufacturing Engineering Faisalabad Campus Lahore38000 Pakistan National Center of Robotics and Automation Human-Centred Robotics Lab Pakistan

ISBN: (纸本)9798350355703

Visual impairment affects the ability to navigate independently in an unknown environment due to reliance on human/dog assistance, potential miscommunication, and discomfort during mobility. To make the visually impaired (VI) individuals self-reliant and confident in their mobility the proposed system has been developed that contains a light detection and ranging (LiDAR) sensor for obstacle detection and two omni-wheels coupled with the DC gear encoder motors for precise movement in any direction without lifting the cane. Hector Simultaneous Localization and Mapping (SLAM), A, and steering wheel algorithms have been implemented in the robot operating system (ROS) for real-time environment mapping, efficient path planning, and navigating in an unknown indoor static environment, respectively. The Raspberry Pi 3B+ and Arduino Mega 2560 are used in the master-slave configuration for decision-making on path planning and controlling the maneuverability of the system in real-time. The proposed system in the paper is successfully tested on one subject in two indoor environments. The results demonstrate that the system serves as an assistive mobile robot, helping the VI individual to navigate confidently in an unknown environment and reach the destination without assistance. © 2024 IEEE.

关键词： Motion planning

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Gait dynamic stability evaluation on inclined surfaces for inverted or everted feet walking

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Fuss und Sprunggelenk 2024年第4期22卷 254-268页

作者： Raza, Anam Mahmood, Imran Sultana, Tayyaba Sultana, Salma Dehghani-Sanij, Abbas A. Department of Zoology Government College University Faisalabad Pakistan Mechatronics and Control Engineering Department Faisalabad Campus UET Lahore Pakistan Institute of Design Robotics and Optimisation School of Mechanical Engineering University of Leeds United Kingdom

Background: This study investigates gait dynamic stability on inclined surfaces while walking with foot rotational impairments such as everted and inverted feet. Walking stability is assessed during gait transitional periods i.e. weight loading and unloading phases. Methods: Foot rotational impairments (eversion and inversion) are imitated using wedged foot insoles. A total of ten healthy subjects performed five trials for each of the inverted, everted, and normal feet conditions. Experimental data was collected using a twelve-camera motion capture system and a force plate installed on an inclined platform. Lower limb joint kinematics were computed in Visual-3D motion analysis software to evaluate the degree of foot rotational impairments. Gait dynamic stability was assessed by analysing the centre of mass and centre of pressure signals as neuromechanical input and output responses respectively. Both signals were modelled in time and frequency domains and stability margins were quantified by applying Nyquist and Bode (N&B) methods from control engineering theory. Results: Stability margins quantified from N&B methods illustrated the loading phase as stable and the unloading phase as unstable gait transitions. During the loading phase, both rotational impairments illustrated a decrease in stability (p < 0,05) for the ramp ascent walk and an increase in stability (p < 0,05) for the ramp descent walk. However, there was no difference observed for the respective unloading phases. Results from neuromotor outputs (CoP-velocity) illustrated a strong interlimb correlation (p < 0,001) during gait transitional periods, implying a compensatory stability interaction exists between opposite limbs. Further, stabilities quantified from outputs were found greater in magnitudes than the instability quantified from inputs illustrating the robustness of neuromotor balance control. Conclusions: Results support the hypothesis that gait stability gets affected while walking with rotation

关键词： Balance control Foot Gait stability Inclined walk Rotational impairments

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Magnetically Actuated Capsule with Multiple Sampling Capability for Gut Microbiome Research

Magnetically Actuated Capsule with Multiple Sampling Capabil...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Sanghyeon Park Sukho Park Department of Robotics and Mechatronics Engineering DGIST Daegu Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Understanding the relationship between gut microbiota and gastrointestinal (GI) diseases through longitudinal analysis is vital for advancing diagnosis and treatment methods. To achieve this, ingestible devices capable of multipoint gut microbiota sampling are necessary. Conventional devices move passively through the GI tract, relying on physiological factors and are unable to perform multipoint sampling. This study introduces a novel magnetically actuated capsule robot designed to collect gut microbiota from various GI tract locations while minimizing cross-contamination. The capsule includes a body, a driving unit, six sampling tools, a central rod, and two heads. It uses electromagnetic field control for precise orientation and position of the capsule, ensuring the sampling channel faces downward for accurate collection. The capsule can collect six distinct microbiota samples, protecting the tools from contamination throughout the process. Its active locomotion and sampling capabilities were tested through ex-vivo tests. The feasibility of clinical application was demonstrated in preclinical trials using a porcine model, confirming the capsule's potential for integration and use in medical settings.

关键词： Wireless communication Performance evaluation Microorganisms Magnetic heads Physiology Magnetic fields Robots Surface treatment Medical diagnostic imaging Diseases

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SLIP Embodied Robust Quadruped Robot Control

SLIP Embodied Robust Quadruped Robot Control

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jinsong Hong Changmin Yeo Sangjin Bae Jeongwoo Hong Sehoon Oh Department of Robotics and Mechatronics Engineering DGIST Daegu Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Recent research on quadruped robots has been achieving high-performance motion control based on optimization and reinforcement learning (RL). However, there is still ongoing research aimed at demonstrating that implementing high-performance motion based on simple and dominant dynamic principles is possible. In this paper, we proposed a novel control approach that projects Spring-Loaded Inverted Pendulum (SLIP) dynamics to articulated legs, utilizing admittance control based force observer within a rotating workspace (RWFOB). Unlike other legged robots that depend on sensor-based estimation of external forces, the proposed method presents an alternative approach that reduces the reliance on sensors. Additionally, we introduce a comprehensive control framework for quadruped robot motion control, establishing the connection between trunk and SLIP-realized leg movements using Jacobian. Through comparative analysis with Virtual Model Control (VMC) in simulations, we illustrate the effectiveness of the proposed framework as a robust and reliable trunk feedback controller.

关键词： Legged locomotion Jacobian matrices Robot motion Analytical models Dynamics Robot sensing systems Quadrupedal robots Reliability Motion control Admittance control

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SLIP Nature Embodied Robust Quadruped Robot Control

SLIP Nature Embodied Robust Quadruped Robot Control

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Jin Song Hong Changmin Yeo Sangjin Bae Jeongwoo Hong Sehoon Oh Department of Robotics and Mechatronics Engineering DGIST Daegu Korea

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Recent research on quadruped robots has been achieving high-performance motion control based on optimization and reinforcement learning. However, there is still ongoing research aimed at demonstrating high-performance motion based on simple and dominant dynamic principles. In this paper, we proposed a novel control approach that projects Spring-Loaded Inverted Pendulum (SLIP) dynamics to articulated legs, utilizing admittance control based force observer within a rotating workspace (RWFOB). Unlike other legged robots that depend on sensor-based estimation of external forces, the proposed method presents an alternative approach that reduces the reliance on sensors. Additionally, we introduce a comprehensive control framework for quadruped robot motion control, establishing the connection between trunk and SLIP-realized leg movements using Jacobian. The effectiveness of the proposed framework as a robust and reliable trunk feedback controller is validated through simulation and experiments.

关键词： Legged locomotion Jacobian matrices Robot motion Dynamics Reinforcement learning Robot sensing systems Quadrupedal robots

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