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Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test

机译:在五次站立测试中使用三轴加速度计进行跌倒分类

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摘要

The five-times-sit-to-stand test (FTSS) is an established assessment of lower limb strength, balance dysfunction and falls risk. Clinically, the time taken to complete the task is recorded with longer times indicating increased falls risk. Quantifying the movement using tri-axial accelerometers may provide a more objective and potentially more accurate falls risk estimate. 39 older adults, 19 with a history of falls, performed four repetitions of the FTSS in their homes. A tri-axial accelerometer was attached to the lateral thigh and used to identify each sit-stand-sit phase and sit-stand and stand-sit transitions. A second tri-axial accelerometer, attached to the sternum, captured torso acceleration. The mean and variation of the root-mean-squared amplitude, jerk and spectral edge frequency of the acceleration during each section of the assessment were examined. The test-retest reliability of each feature was examined using intra-class correlation analysis, ICC(2,. k). A model was developed to classify participants according to falls status. Only features with ICC. >. 0.7 were considered during feature selection. Sequential forward feature selection within leave-one-out cross-validation resulted in a model including four reliable accelerometer-derived features, providing 74.4% classification accuracy, 80.0% specificity and 68.7% sensitivity. An alternative model using FTSS time alone resulted in significantly reduced classification performance. Results suggest that the described methodology could provide a robust and accurate falls risk assessment.
机译:五次站立测试(FTSS)是对下肢力量,平衡功能障碍和跌倒风险的既定评估。临床上,记录完成任务所花费的时间,更长的时间表明跌倒风险增加。使用三轴加速度计对运动进行量化可能会提供更客观且可能更准确的跌倒风险估算。 39名老年人(其中19人有跌倒的历史)在家中进行了4次FTSS的重复。三轴加速度计连接到大腿外侧,用于识别每个坐立-坐立阶段以及坐立-站立和坐立过渡。附在胸骨上的第二个三轴加速度计捕获了躯干加速度。在评估的每个部分中,检查了加速度的均方根振幅,抖动和频谱边缘频率的均值和变化。使用类内相关分析ICC(2,.k)检查每个功能的重测可靠性。开发了一个模型以根据跌倒状态对参与者进行分类。仅具有ICC的功能。 >。特征选择期间考虑了0.7。留一法交叉验证中的顺序正向特征选择产生了一个模型,该模型包括四个可靠的加速度计衍生特征,可提供74.4%的分类精度,80.0%的特异性和68.7%的灵敏度。仅使用FTSS时间的替代模型会导致分类性能大大降低。结果表明,所描述的方法可以提供可靠且准确的跌倒风险评估。

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