首页|Studies in the Area of Robotics Reported from University of Shanghai for Science and Technology (Research on multi-dimensional intelligent quantitative assessme nt of upper limb function based on kinematic parameters)
Studies in the Area of Robotics Reported from University of Shanghai for Science and Technology (Research on multi-dimensional intelligent quantitative assessme nt of upper limb function based on kinematic parameters)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in robotics. According to news originating from Shanghai, People’s Republic of Chin a, by NewsRx correspondents, research stated, “Rehabilitation assessment is a cr itical component of rehabilitation treatment. This study focuses on a comprehens ive analysis of patients’ movement performance using the upper limb rehabilitati on robot.” Our news reporters obtained a quote from the research from University of Shangha i for Science and Technology: “It quantitatively assessed patients’ motor contro l ability and constructed an intelligent grading model of functional impairments . These findings contribute to a deeper understanding of patients’ motor ability and provide valuable insights for personalized rehabilitation interventions. Pa tients at different Brunnstrom stages underwent rehabilitation training using th e upper limb rehabilitation robot, and data on the distal movement positions of the patients’ upper limbs were collected. A total of 22 assessment metrics relat ed to movement efficiency, smoothness, and accuracy were extracted. The performa nce of these assessment metrics was measured using the Mann-Whitney U test and P earson correlation analysis. Due to the issue of imbalanced sample categories, d ata augmentation was performed using the Synthetic Minority Over-sampling Techni que (SMOTE) algorithm based on weighted sampling, and an intelligent grading mod el of functional impairment based on the Extreme Gradient Boosting Tree (XGBoost ) algorithm was constructed. Sixteen assessment metrics were screened. These met rics were effectively normalized to their maximum values, enabling the derivatio n of quantitative assessment scores for motor control ability across the three d imensions through a weighted fusion approach. Notably, when applied to the dataenhanced dataset, the intelligent grading model exhibited remarkable improvement , achieving an accuracy rate exceeding 0.98. Moreover, significant enhancements were observed in terms of precision, recall, and f1-score.”
University of Shanghai for Science and T echnologyShanghaiPeople’s Republic of ChinaAsiaEmerging TechnologiesMa chine LearningRobotRobotics
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