A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot

Zhao, Yating; Liang, Changyong; Gu, Zuozuo; Zheng, Yunjun; Wu, Qilin

doi:10.3390/ijerph17082948

Cited by 27 publications

(18 citation statements)

References 39 publications

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“…Several biomechanical parameters have been proposed in the scientific literature to date for assessing the quality of movement in healthy and diseased subjects in different rehabilitation settings and tasks [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Moreover, several studies have aimed to exploit motion analysis data and compare different instrumentations for diagnostic purposes [32,33].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Ponsiglione

Ricciardi

Amato

et al. 2022

Sensors

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The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson’s disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson’s disease.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Ponsiglione

Ricciardi

Amato

et al. 2022

Sensors

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“…The AI-based medical devices can be helpful during patient recovery, meeting the requirements of rehabilitation and expediting the proceedings [107] . In addition, the application of AI robots also has accelerated limb rehabilitation in complex anthropopathic action guidance and helped patients to obtain a better degree of recovery [108,109] . What's more, AI technology also has been used to track progression and to monitor health, which may be beneficial for the management of discharged patients [110,111] .…”

Section: Rehabilitation Assistancementioning

confidence: 99%

Application of Artificial Intelligence in Medicine: An Overview

Liu

Zhang

et al. 2021

CURR MED SCI

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Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient’s diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

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Section: Robotics Therapy For the Upper Limbmentioning

confidence: 99%

“…Zhao et al designed a new exoskelton robot structure with 7 DOF in [49]. The system uses a multi-sensor system (force, encoding, hearing, vision, myoelectric and proximity sensors), the principles of machine learning and deep learning are used by applying optimal and robust adaptive control based on Udwdia-Kalaba theory and fuzzy logic to ensure safe human-machine interaction [49]. The prototype designed will improve patient participation in training sessions through the use of virtual reality and game-based therapy sessions, help doctors build a good rehabilitation training programme and help hospitals manage patients in real time and summarise experiences.…”

Section: Robotics Therapy For the Upper Limbmentioning

confidence: 99%

Overview of Robotic Based System for Rehabilitation and Healthcare

Chellal

Lima

Fernandes

et al. 2021

Communications in Computer and Information Science

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A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot

Cited by 27 publications

References 39 publications

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

Application of Artificial Intelligence in Medicine: An Overview

Overview of Robotic Based System for Rehabilitation and Healthcare

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