School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
Service of Geriatric Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1066, Epalinges, Switzerland.
Med Biol Eng Comput. 2018 Aug;56(8):1403-1412. doi: 10.1007/s11517-017-1778-8. Epub 2018 Jan 12.
The frequency and quality of sit-to-stand and stand-to-sit postural transitions decrease with age and are highly relevant for fall risk assessment. Accurate classification and characterization of these transitions in daily life of older adults are therefore needed. In this study, we propose to use instrumented shoes for postural transition classification as well as transition duration estimation from insole force signals. In the first part, data were collected with 10 older adults and 10 young participants performing transitions in the laboratory while wearing the instrumented shoes, without arm assistance. A wavelet approach was used to transform the insole force data, and candidate events were selected for transition duration estimation. Transition durations were then validated against a model based on force plate reference. Vertical force estimation was also compared to force plate measurement. In the second part, postural transitions were classified in daily life using the instrumented shoes and validated against a highly accurate wearable system. Transition duration was estimated with an error ranging from 10 to 20% while the error for vertical force estimation was 7%. Postural transition classification was achieved with excellent sensitivity and precision exceeding 90%. In conclusion, the instrumented shoes are suitable for classifying and characterizing postural transitions in daily life conditions of healthy older adults. Graphical abstract "Experimental setup showing instrumented shoes, reference force plate, as well as IMUs used for postural transition classification and duration estimation comparison".
坐-站和站-坐姿势转换的频率和质量随着年龄的增长而降低,与跌倒风险评估高度相关。因此,需要在老年人的日常生活中准确地对这些转换进行分类和描述。在这项研究中,我们提出使用仪器化的鞋子来进行姿势转换分类,以及从鞋垫力信号中估计转换的持续时间。在第一部分,数据是通过 10 名老年人和 10 名年轻参与者在实验室中穿着仪器化的鞋子、没有手臂辅助的情况下进行转换收集的。使用小波方法对鞋垫力数据进行转换,并选择候选事件进行转换持续时间估计。然后,将转换持续时间与基于力板参考的模型进行验证。还比较了垂直力估计与力板测量。在第二部分,使用仪器化的鞋子在日常生活中对姿势转换进行分类,并与高精度的可穿戴系统进行验证。转换持续时间的估计误差在 10%到 20%之间,而垂直力估计的误差为 7%。姿势转换分类的灵敏度和精度均超过 90%,达到了优秀水平。总之,仪器化的鞋子适用于对健康老年人日常生活中的姿势转换进行分类和描述。