Medical errors, defined as unintended acts either of omission or commission that cause the failure of medical actions, are the third leading cause of death in the United States. Medical errors can include communicatio...
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Medical errors, defined as unintended acts either of omission or commission that cause the failure of medical actions, are the third leading cause of death in the United States. Medical errors can include communication breakdowns, diagnostic errors, poor judgment, and inadequate skills. The application of autonomy and robotics can alleviate some causes of medical errors by improving accuracy and providing means to accurately follow planned procedures. However, for the robotic applications to improve safety, they must maintain constant operating conditions in the presence of disturbances, and provide reliable measurements, evaluation, and control for each state of the procedure. This article addresses the need for process control in medical robotic systems, and proposes a standardized design cycle toward its automation. Monitoring and controlling the changing conditions in a medical or surgical environment necessitates a clear definition of workflows and their procedural dependencies. We propose integrating process control into medical robotic workflows using hierarchical Finite State Machines (hFSM) to identify changes in states of the system and environment, and execute possible operations or transitions to new states. Therefore, the system translates clinician experiences and procedure workflows into machine-interpretable languages. The design cycle using hFSM formulation can be a deterministic process, which opens up possibilities for higher-level automation in medical robotics. Shown in our work, with a standardized design cycle and software paradigm, we pave the way toward controlled workflows that can be automatically generated. Additionally, a modular design for a robotic system architecture that integrates hFSM can provide easy software and hardware integration. This article discusses the system design, software implementation, and example application to Robot-Assisted Transcranial Magnetic Stimulation (RATMS) and robot-assisted femoroplasty. We also provide
Estimating Neural Radiance Fields (NeRFs) from images captured under optimal conditions has been extensively explored in the vision community. However, robotic applications often face challenges such as motion blur, i...
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Realistic garment simulation is critical for digital humans. However, noticeable penetrations still exist in current learning-based garment simulation techniques. To reduce penetrations in predicted garments, we resor...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Realistic garment simulation is critical for digital humans. However, noticeable penetrations still exist in current learning-based garment simulation techniques. To reduce penetrations in predicted garments, we resort to the garment geometry and neural Signed Distance Fields (SDFs) for effective collision handling. The key idea of our method is that we divide the garment into patches and model the local and global garment geometry through Intra- and Inter-Patch Correlations (IIPC), which can be easily learned through the powerful context-understanding ability of Transformers. The geometry information is then utilized to predict a per-vertex moving offset, according to which we move the penetrating vertices along the SDF’s gradient directions to solve collisions. Our module can be coupled with learning-based backbones to effectively solve penetrations while retaining real-time performance. Extensive experiments show that the proposed method excels the prior works significantly.
Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper pr...
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Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.
This study proposes an optimized design for the leg transmission structure of a quadruped robot, aiming to enhance the load-bearing capacity of the knee joint. Firstly, the dynamic simulation of the single leg's l...
This study proposes an optimized design for the leg transmission structure of a quadruped robot, aiming to enhance the load-bearing capacity of the knee joint. Firstly, the dynamic simulation of the single leg's load-lifting process in the quadruped robot is conducted, indicating higher torque demands on the knee joint during this process. Therefore, a leg transmission mechanism, combining a ball screw mechanism and a crank-slider mechanism, is designed to effectively amplify the torque of the knee joint motor. Subsequently, a virtual prototype of the quadruped robot, incorporating this design, is constructed using Adams software. The foot trajectory of the robot is pre-planned, and the joint trajectories are obtained through inverse kinematics, using them as the driving functions for the quadruped robot to perform actions such as load lifting, static standing, and diagonal walking gait. Finally, the knee joint torque and corresponding motor driving torque of the quadruped robot's legs are measured and compared. The results demonstrate that the driving torque of the knee joint motor is effectively amplified by the leg transmission mechanism, with the maximum amplification observed during the initial stage of load lifting, reaching a magnification factor of nearly 7.66 times. This verifies the effectiveness of the proposed leg transmission mechanism in enhancing the load-bearing capacity of the knee joint.
The Internet of Flying Things is one of the priority areas for robotics and automation research and applications. A promising case is an application of UAVs in a search and rescue scenario for data collection from pre...
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ISBN:
(数字)9798331517564
ISBN:
(纸本)9798331517571
The Internet of Flying Things is one of the priority areas for robotics and automation research and applications. A promising case is an application of UAVs in a search and rescue scenario for data collection from pre-positioned IoT devices, which allows assessing and monitoring a current situation in an operation area. This paper presents a concept of using a low-power LoRa protocol to transmit data from smart sensors to UAVs that deliver the data to a dedicated cloud for further processing and analysis. A mock-up of a structure, which is described in the paper, is to be realized using microcontrollers, LoRa modules and smart temperature sensors.
Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions (CLFs) and control barrier functions ...
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Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive cont...
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ISBN:
(数字)9798350352344
ISBN:
(纸本)9798350352351
Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.
Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, requires adopting more effic...
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Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, requires adopting more efficient agricultural practices. Traditional irrigation systems, often imprecise, contribute to water wastage. The use of embedded systems and machine learning offers a solution for optimizing irrigation according to local conditions and actual crop needs while contributing to food security and environmental sustainability. This study proposes an innovative approach to irrigation management, integrating real-time data and predictive models to improve irrigation efficiency. This study proposes an irrigation system based on embedded systems, using sensors and algorithms to collect and analyze data in order to optimize water management. The system adjusts irrigation levels according to specific crop needs, thus contributing to more sustainable water management. Using ML algorithms like linear regression algorithms to model the relationships between environmental factors and crop water requirements, enabling accurate prediction of required irrigation levels based on data collected by sensors. The use of embedded systems such as the ESP32, combined with temperature, humidity, and water level sensors, has enabled the development of an autonomous and efficient system for collecting data in real-time and processing it for decision-making. The proposed model has an MAE of 0.8434, an RMSE of 0.8434, and a coefficient (R2 Score) of 0.4044, offering soil moisture prediction accuracy. Furthermore, the training time of our model is 0.00253 seconds, while the prediction time is 0.00117 seconds. These results show not only the performance of the proposed model in terms of accuracy but also its computational efficiency, outperforming some of the studies mentioned. The results of the study show a significant reduction in water consumption, with a marked improvement in water
Stroke patients are unable to perform gripping actions effectively as normal individuals due to impaired hand function. Electrical stimulation is currently a relatively effective rehabilitation method, but there is st...
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ISBN:
(数字)9798350385724
ISBN:
(纸本)9798350385731
Stroke patients are unable to perform gripping actions effectively as normal individuals due to impaired hand function. Electrical stimulation is currently a relatively effective rehabilitation method, but there is still room for improvement in aspects such as grip control precision and grip posture selection. Therefore, we propose a multi-channel electrical stimulation system to achieve precise control of hand gripping in stroke patients and assist in hand function rehabilitation. The pressure-controlled constant current source electrical stimulation system is proposed and a flexible wearable 16-channel hand FPC electrical stimulation patch is designed according to the distribution of hand muscles. The MCU controls an external DAC chip to generate voltage to drive the pressure-controlled constant current source, thereby generating adjustable biphasic symmetrical stimulation pulse waveforms. The FPC transmits the generated stimulation pulses to the corresponding stimulation points to achieve precise electrical stimulation of specific points. Through three comparative experiments, the results indicate that precise control of finger movement angles has been achieved by adjusting the stimulation current and frequency. The flexible multi-channel hand electrical stimulation system offers a solution for restoring motor function in patients with hemiplegia.
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