Catheters navigating through complex vessels,such as sharp turns or multiple U-turns,remain challenging for vascular ***,we propose a novel multistage vascular embolization strategy for hard-to-reach vessels that rele...
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Catheters navigating through complex vessels,such as sharp turns or multiple U-turns,remain challenging for vascular ***,we propose a novel multistage vascular embolization strategy for hard-to-reach vessels that releases untethered swimming shape-memory magnetic microrobots(SMMs)from the prior catheter to the vessel ***,made of organo-gel with magnetic particles,ensure biocompatibility,radiopacity,thrombosis,and fast thermal and magnetic *** SMM is initially a linear shape with a 0.5-mm diameter at 20°C inserted in a *** transforms into a predetermined helix within 2 s at 38°C blood temperature after being pushed out of the catheter into the *** enable agile swimming in confined and tortuous vessels and can swim upstream using helical propulsion with rotating magnetic ***,we validated this multistage vascular embolization in living rabbits,completing 100-cm travel and renal artery embolization in 2 *** 4 weeks,the SMMs maintained the embolic position,and the kidney volume decreased by 36%.
Designed to provide rehabilitation or the assistance of walking for individuals who are with lower limb muscle injuries or spinal during their activities, self-balancing lower limb exoskeletons have been developed. Th...
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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 ...
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Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychol...
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The Brain-Computer Interface (BCI) technology has emerged as an innovative tool in the field of rehabilitation, aimed at restoring and improving patient motor and sensory functions. The motivation for using BCI in reh...
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ISBN:
(数字)9798331529734
ISBN:
(纸本)9798331529741
The Brain-Computer Interface (BCI) technology has emerged as an innovative tool in the field of rehabilitation, aimed at restoring and improving patient motor and sensory functions. The motivation for using BCI in rehabilitation is primarily based on the ability of these systems to establish a direct correlation between brain activity and external devices, such as motor prostheses or assistive robots. This technology aids patients with spinal cord injuries, strokes, or neurological diseases in regaining lost functionality by directly controlling assistive devices. The primary objective of research in this area is to enhance the quality of life for individuals with motor impairments and to improve the efficiency of rehabilitation methods, potentially leading to the development of models for automation in clinics and rehabilitation centers. In this study, to utilize the BCI as an automated system for controlling rehabilitation devices or functional prostheses, a series of experiments were designed and conducted to record electroencephalography (EEG) data from healthy individuals during a variety of hand motions, while simultaneously the motion is captured with two webcams. The initial data was collected and analyzed, and the results indicate that increasing the number of test subjects and employing more advanced equipment for signal recording could lead to more accurate results and the development of a more comprehensive model. Obtained model can then be used to command a rehabilitation arm according to the patient brain signals.
Terrain classification and force assistance strategies in complex environments have always piqued the interest of many *** wearable soft exosuits,inaccurate terrain recognition can easily introduce undesired assist fo...
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Terrain classification and force assistance strategies in complex environments have always piqued the interest of many *** wearable soft exosuits,inaccurate terrain recognition can easily introduce undesired assist forces that can easily injure the *** of these problems,we introduced a depth camera into the exosuit system,perform classification of terrain based on a Vision Transformer(ViT),and optimized the control algorithm,which is known as a ViT-Based Terrain Recognition System(TTRS).First,we used the Transformer algorithm to achieve a considerable classification effect in terrain *** also introduced terrain recognition as prior knowledge into the force assistance strategy of the exosuit,providing different force assistance to the exosuit in different ***,we performed human experiments with seven able-bodied people(six males and one female).The promising results demonstrate that our classification accuracy can reach 99.2%under six different terrains and that it can smoothly switch the force–assist curve in different terrains to better adapt to the complex terrain and improve the walking *** aforementioned terrain recognition algorithms and force–assist strategies may positively influence the study of soft exosuit,powered prostheses,and orthotics.
This paper investigates machine learning approaches for gait mode detection in lower limb exoskeletons. Five gait modes (level walking, stair ascent, stair descent, ramp ascent, and ramp descent) are identified using ...
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ISBN:
(数字)9798331529734
ISBN:
(纸本)9798331529741
This paper investigates machine learning approaches for gait mode detection in lower limb exoskeletons. Five gait modes (level walking, stair ascent, stair descent, ramp ascent, and ramp descent) are identified using a limited set of sensor data (IMU and heel motion markers) to ensure fast and accurate detection. The performance of 31 classification models trained on two different datasets is studied and four of the best models in terms of accuracy are demonstrated and discussed. Results show that a Bilayered Neural Network (BNN) trained on equalized data achieves the best performance with a success rate of 97.57% in real-time simulation. The BNN outperforms a similar Multi-Layer Perceptron Neural Network (MLPNN) model used in previous research, demonstrating its potential for robust gait mode detection in wearable robots.
Nuclear energy is presently the clean energy source most likely to massively displace fossil energy, and the secondary side of the steam generator is an important part of a nuclear power plant. Due to the small space ...
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This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image cl...
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In various fields such as robotics, rehabilitation, biomechanics, human-machine interfaces, and clinical research, human motion prediction has extensive applications. A hybrid networks model based on Convolutional Neu...
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