Research on Theory and a Performance Analysis of an Innovative Rehabilitation Robot

Wu, Junyu; Liu, Yubin; Zhao, Jie; Zang, Xizhe; Guan, Yingzi

doi:10.3390/s22103929

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Based on the above bibliometric results, combined with the authors’ understanding, we identified research hotspots for AI research in spinal cord neural injury and restoration: Intelligent robots and limb exoskeletons for assisted rehabilitation: there is no doubt that these physical branches of AI are the most significant research hotspot of AI in the field of spinal cord neural injury and restoration, which is multiply verified from the keyword analysis, literature co-citation analysis, and the hotspot analysis of highly co-cited literature. Rehabilitation robots are interactive motorized devices that allow fine limb movement and precise measurements ( 27 ). They are typically divided into exoskeletons that assist in limb movement by controlling the displacement of each segment and end-effector devices capable of mobilizing the limb from a distal point of application.…”

Section: Discussionmentioning

confidence: 99%

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration—a bibliometrics and visualization analysis

Tao,

Yang,

et al. 2024

Front. Neurol.

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BackgroundArtificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research’s progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.MethodsWe used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.ResultsA total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.ConclusionWe pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.

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Section: Discussionmentioning

confidence: 99%

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration—a bibliometrics and visualization analysis

Tao,

Yang,

et al. 2024

Front. Neurol.

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“…The main function of the robot is to help patients train by simulating normal activities. It can also be worn on the patient to force them to perform various rehabilitation exercises, continuously stimulating their brains, improving the ability of their motor organs, and achieving an early recovery [ 97 ]. Robot-assisted rehabilitation systems mostly monitor hand extension, flexion, and wrist movements in the field of stroke rehabilitation, and there are few studies on fine movements, such as finger coordination activities.…”

Section: Discussionmentioning

confidence: 99%

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

Wei,

2023

Sensors

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The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

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“…One-dimensional sliding in the horizontal direction is achieved through these linear pairs. The motion and power of these linear pairs are transmitted to the vestibular end platform through the connecting rods and the spherical wrists [14]. The end platform has 6 DOFs, and the mechanical interface on this platform is the installation benchmark of the proprioception platform.…”

Section: A Robot Structurementioning

confidence: 99%

Research on a New Rehabilitation Robot for Balance Disorders

Wu,

Liu,

Zhao

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The treatment of patients with balance disorders is an urgent problem to be solved by the medical community. The causes of balance disorders are diverse. An aging population, traffic accidents, stroke, genetic diseases and so on are all possible factors. It has brought great pain and inconvenience to patients and their families. At present, there are two main types of assisted rehabilitation training robots for patients with balance disorders: exoskeleton robots and end robots. The exoskeleton robot is generally installed on the outside of the patient's body to follow their movement, which can support the weight of the body and provide power support to help the patient train and recover lower limb ability. The use of end robots is usually to secure the patient's foot to the motion platform and control the pedal to drive the lower limbs to conduct gait training. Such passive training is more suitable for patients with severe disorders. The patient has low awareness of active participation. This paper focuses on research on end rehabilitation training robots for balance disorders. In this paper, a robotic system for rehabilitation training of patients with balance disorders is invented. The robot body is a 9 degree of freedom (DOF) redundant series-parallel hybrid motion platform. Two sets of motion platforms with symmetrical mirror images are used together to simulate different motion modes of the human body and drive the human body to move. Each set of motion platforms is composed of a 6-DOF vestibular parallel device and a 3-DOF proprioception parallel device. It has the advantages of DOF decoupling and fast response, proposing a new structural form for the design of proprioceptive and vestibular simulation platforms. The robot's functional level can be divided into a vestibular sense module and a proprioception module according to the structure. The two modules can work independently to achieve different functions or work together to achieve complex motion and multisensory fusion. This robot is a redundant mechanism device with 9 DOFs. Through a reasonable distribution of DOF and motion, the robot's working space can be increased, and the robot's flexibility and motion performance can be improved. In this paper, a trajectory tracking control

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Research on Theory and a Performance Analysis of an Innovative Rehabilitation Robot

Cited by 5 publications

References 11 publications

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration—a bibliometrics and visualization analysis

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration—a bibliometrics and visualization analysis

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

Research on a New Rehabilitation Robot for Balance Disorders

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