arXiv cs.AI· Rehan Khan, Muhammad Ashraf, Sikandar Ali, Asad Ali Khan, Muhammad Junaid Asif, Nabeel Ahmad·· 11 小时前AI 评分22
结合三轴 IMU 传感器与集成学习实现帕金森病严重程度分类
Integrating Triaxial IMU Sensors and Ensemble Learning for Effective Parkinson Disease Severity Classification
AI 导读
一项研究用三轴 IMU 采集加速度与陀螺仪信号，对比 SVM、LR、KNN、DT、XGBoost 和 LightGBM 等模型对帕金森病严重程度的分类效果。LightGBM 表现最佳，Accuracy、Precision、Recall 和 F1-score 均约 97%，KNN 约 90%，SVM 接近 94%，DT 与 XGBoost 接近 96%。
正文 · 原文
Abstract:Parkinson disease PD is a progressive neurodegenerative disease that can have a significant impact on motor performance resulting in the appearance of symptoms such as tremors rigidity postural instabilities and bradykinesia. Timely clinical treatment disease management and quality life of the patients are closely linked to early and appropriate identification of PD. Over the past few years the growth of wearable sensor technology and artificial intelligence AI have made it possible to create noninvasive and data driven disease detection methods. This paper proposes a comparative system using artificial intelligence to detect Parkinsons disease by analyzing the motion and tremor data captured by an inertial measurement unit IMU. The data comprises the signals of the acceleration and gyroscope sensors measuring movement in three directions X Y and Z. The signs and symptoms provide helpful information about subtle motor deficits associated with PD. Several classification models like Support Vector Machine SVM Logistic Regression LR KNearest Neighbors KNN Decision Tree DT Extreme Gradient Boosting XGBoost and Light Gradient Boosting Machine LightGBM were used to compare their effectiveness. The Logistic Regression model had a performance around 75 percent in all evaluation metrics and KNearest Neighbours KNN around 90 percent. The support vector machine SVM performed almost 94 percent whereas the performance of classifiers such as Decision Tree and XGBoost was close to 96 percent and overall classification efficacy respectively. LightGBM model performs consistently at the best rank among all of the evaluated methods having Accuracy, Precision, Recall and F1score of around 97 percent. The results show that the proposed machine learning approach offers an accurate and effective predictive capability in the classification of PD severity.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2608.28602 [cs.AI] |
| (or arXiv:2608.28602v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.28602 arXiv-issued DOI via DataCite |
Submission history
From: Muhammad Junaid Asif [view email]
[v1]
Tue, 30 Jun 2026 09:49:30 UTC (1,509 KB)
[v2]
Thu, 5 Nov 2026 04:30:57 UTC (1,070 KB)
来源：arXiv cs.AI · arxiv.org