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Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer

机译:pD患者的姿势转换识别通过基于sVm的技术和单个腰部加速度计

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摘要

Identification of activities of daily living is essential in order to evaluate the quality of life both in the elderly and patients with mobility problems.\udPosture transitions (PT) are one of the most\udmechanically demanding activities in daily life and,thus, they can lead to falls in patients with mobility problems. This paper deals with PT recognition in Parkinson’s Disease (PD) patients by means of a triaxial accelerometer situated between the anterior and the left lateral part of the waist. Since sensor’s orientation is susceptible to change during long monitoring periods, a\udhierarchical structure of classifiers is proposed in order to identify PT while allowing such orientation changes. Results are presented based on signals obtained from 20 PD patients and 67 healthy people who wore an inertial sensor on different positions among the anterior and the\udleft lateral part of the waist. The algorithm has been compared to a previous approach in which only the anterior-lateral location was analyzed improving the sensitivity while preserving specificity. Moreover, different supervised machine l\udearning techniques have been evaluated in\uddistinguishing PT. Results show that the location of the sensor slightly affects method’s performance and, furthermore, PD motor state does not alter its accuracy.
机译:识别日常生活活动对于评估老年人和行动不便的患者的生活质量至关重要。姿势过渡(PT)是日常生活中对机械要求最高的活动之一,因此,他们可以导致行动不便的患者跌倒。本文通过位于腰部前部和左侧部之间的三轴加速度计,探讨了帕金森氏病(PD)患者的PT识别。由于传感器的方向很容易在较长的监视时间内发生变化,因此提出了分类器的\ uhierarchical结构,以便在允许此类方向变化的同时识别PT。结果是根据从20名PD患者和67名健康人身上获得的信号给出的,这些人在腰部的前部和腰部的不同位置之间戴着惯性传感器。该算法已与以前的方法进行了比较,在以前的方法中,仅分析了前外侧位置,从而提高了灵敏度,同时保留了特异性。此外,已经在区分PT中评估了不同的监督机器学习技术。结果表明,传感器的位置会稍微影响方法的性能,此外,PD电机状态不会改变其准确性。

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