Salarian Arash, Russmann Heike, Vingerhoets François J G, Burkhard Pierre R, Aminian Kamiar
Laboratory of Movement Analysis and Measurement (LMAM), Ecole Polytechnique Fédérale de Lausanne (EPFL), STI-LMAM, ELG 239, Lausanne Vaud 1024, Switzerland.
IEEE Trans Biomed Eng. 2007 Dec;54(12):2296-9. doi: 10.1109/tbme.2007.896591.
A new ambulatory method of monitoring physical activities in Parkinson's disease (PD) patients is proposed based on a portable data-logger with three body-fixed inertial sensors. A group of ten PD patients treated with subthalamic nucleus deep brain stimulation (STN-DBS) and ten normal control subjects followed a protocol of typical daily activities and the whole period of the measurement was recorded by video. Walking periods were recognized using two sensors on shanks and lying periods were detected using a sensor on trunk. By calculating kinematics features of the trunk movements during the transitions between sitting and standing postures and using a statistical classifier, sit-to-stand (SiSt) and stand-to-sit (StSi) transitions were detected and separated from other body movements. Finally, a fuzzy classifier used this information to detect periods of sitting and standing. The proposed method showed a high sensitivity and specificity for the detection of basic body postures allocations: sitting, standing, lying, and walking periods, both in PD patients and healthy subjects. We found significant differences in parameters related to SiSt and StSi transitions between PD patients and controls and also between PD patients with and without STN-DBS turned on. We concluded that our method provides a simple, accurate, and effective means to objectively quantify physical activities in both normal and PD patients and may prove useful to assess the level of motor functions in the latter.
基于一款带有三个固定在身体上的惯性传感器的便携式数据记录器，提出了一种监测帕金森病（PD）患者身体活动的新动态方法。一组接受丘脑底核深部脑刺激（STN-DBS）治疗的10名PD患者和10名正常对照受试者遵循典型日常活动方案，并通过视频记录整个测量周期。使用小腿上的两个传感器识别步行阶段，使用躯干上的传感器检测躺卧阶段。通过计算坐姿和站姿转换期间躯干运动的运动学特征，并使用统计分类器，检测坐立（SiSt）和站立坐立（StSi）转换，并将其与其他身体运动区分开来。最后，一个模糊分类器利用这些信息检测坐立和站立阶段。所提出的方法在检测基本身体姿势分配（即坐、站、躺和步行阶段）方面，对PD患者和健康受试者均显示出高灵敏度和特异性。我们发现，PD患者与对照组之间以及STN-DBS开启和未开启的PD患者之间，与SiSt和StSi转换相关的参数存在显著差异。我们得出结论，我们的方法提供了一种简单、准确且有效的手段，可客观量化正常人和PD患者的身体活动，并且可能被证明对评估后者的运动功能水平有用。
