L. Fiorini, G. D’Onofrio, E. Rovini, Alessandra Sorrentino, Luigi Coviello, Raffaele Limosani, Daniele Sancarlo, F. Cavallo
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引用次数: 4
Abstract
Aging society is characterized by a high prevalence of sarcopenia, which is considered one of the most common health problems of the elderly population. Sarcopenia is due to the age-related loss of muscle mass and muscle strength. Recent literature findings highlight that the Tinetti Balance Assessment (TBA) scale is used to assess the sarcopenia in elderly people. In this context, this article proposes a model for sarcopenia assessment that is able to provide a quantitative assessment of TBA-gait motor parameters by means of a cloud robotics approach. The proposed system is composed of cloud resources, an assistive robot namely ASTRO and two inertial wearable sensors. Particularly, data from two inertial sensors (i.e., accelerometers and gyroscopes), placed on the patient’s feet, and data from ASTRO laser sensor (position in the environment) were analyzed and combined to propose a set of motor features correspondent to the TBA gait domains. The system was preliminarily tested at the hospital of “Fondazione Casa Sollievo della Sofferenza” in Italy. The preliminary results suggest that the extracted set of features is able to describe the motor performance. In the future, these parameters could be used to support the clinicians in the assessment of sarcopenia, to monitoring the motor parameters over time and to propose personalized care-plan.
老龄化社会的特点是肌肉减少症的高发,这被认为是老年人最常见的健康问题之一。肌肉减少症是由于与年龄有关的肌肉质量和肌肉力量的损失。最近的文献发现强调了Tinetti平衡评估(TBA)量表用于评估老年人肌肉减少症。在此背景下,本文提出了一种肌肉减少症评估模型,该模型能够通过云机器人方法提供tba -步态运动参数的定量评估。该系统由云资源、辅助机器人ASTRO和两个惯性可穿戴传感器组成。特别地,对放置在患者脚上的两个惯性传感器(即加速度计和陀螺仪)的数据和ASTRO激光传感器(在环境中的位置)的数据进行分析和组合,提出了一组与TBA步态域相对应的运动特征。该系统在意大利“Sollievo della soffferenza基金会”医院进行了初步测试。初步结果表明,提取的特征集能够描述运动性能。在未来,这些参数可用于支持临床医生评估肌肉减少症,监测运动参数随时间的变化,并提出个性化的护理计划。