Safi Khaled, Aly Wael Hosny Fouad, Kanj Hassan, Khalifa Tarek, Ghedira Mouna, Hutin Emilie
Computer Science Department, Jinan University, Tripoli P.O. Box 818, Lebanon.
College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.
Bioengineering (Basel). 2024 Jan 17;11(1):88. doi: 10.3390/bioengineering11010088.
Understanding the behavior of the human postural system has become a very attractive topic for many researchers. This system plays a crucial role in maintaining balance during both stationary and moving states. Parkinson's disease (PD) is a prevalent degenerative movement disorder that significantly impacts human stability, leading to falls and injuries. This research introduces an innovative approach that utilizes a hidden Markov model (HMM) to distinguish healthy individuals and those with PD. Interestingly, this methodology employs raw data obtained from stabilometric signals without any preprocessing. The dataset used for this study comprises 60 subjects divided into healthy and PD patients. Impressively, the proposed method achieves an accuracy rate of up to 98% in effectively differentiating healthy subjects from those with PD.
理解人体姿势系统的行为已成为许多研究人员非常感兴趣的话题。该系统在静止和运动状态下维持平衡方面起着至关重要的作用。帕金森病（PD）是一种常见的退行性运动障碍，会严重影响人体稳定性，导致跌倒和受伤。本研究引入了一种创新方法，利用隐马尔可夫模型（HMM）来区分健康个体和帕金森病患者。有趣的是，该方法使用从稳定测量信号中获取的原始数据，无需任何预处理。本研究使用的数据集包括60名受试者，分为健康组和帕金森病患者组。令人印象深刻的是，所提出的方法在有效区分健康受试者和帕金森病患者方面的准确率高达98%。
