Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults.
Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults.
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DOI：
10.3390/s22093334
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发表时间：
2022-04-27
期刊：
影响因子：
3.9
通讯作者：
Bhatt, Tanvi
中科院分区：
文献类型：
--
作者：
Wang, Shuaijie;Miranda, Fabio;Wang, Yiru;Rasheed, Rahiya;Bhatt, Tanvi
滑倒导致的跌倒是老年人日益关注的健康问题，而险些摔倒的事件与跌倒的风险增加有关。为了检测与跌倒相关的高风险老年人，本研究旨在开发基于身体固定传感器收集的加速度计数据的近距离坠落事件检测模型。包括34名健康的老年人，他们经历了24次实验室诱导的滑倒。首先确定滑脱结果为失去平衡(LOB)和无平衡(NLOB)，然后对这两种结果的运动学测量进行比较。接下来，所有的SLIP试验被分成样本水平的训练集(90%)和测试集(10%)。训练集被用来训练机器学习模型(n=2)和深度学习模型(n=2)，测试集被用来评估每个模型的性能。结果表明，深度学习模型对LOB(>；%)和NLOB(>；90%)分类的准确率均高于机器学习模型。在所有模型中，初始模型的分类准确率最高(87.5%)，接收器工作特征曲线(AUC)下面积最大，表明该模型是一种有效的近距离坠落(LOB)检测方法。我们的方法有助于在经历真正的跌倒之前识别有滑倒相关跌倒风险的个人。
Slip-induced falls are a growing health concern for older adults, and near-fall events are associated with an increased risk of falling. To detect older adults at a high risk of slip-related falls, this study aimed to develop models for near-fall event detection based on accelerometry data collected by body-fixed sensors. Thirty-four healthy older adults who experienced 24 laboratory-induced slips were included. The slip outcomes were first identified as loss of balance (LOB) and no LOB (NLOB), and then the kinematic measures were compared between these two outcomes. Next, all the slip trials were split into a training set (90%) and a test set (10%) at sample level. The training set was used to train both machine learning models (n = 2) and deep learning models (n = 2), and the test set was used to evaluate the performance of each model. Our results indicated that the deep learning models showed higher accuracy for both LOB (>64%) and NLOB (>90%) classifications than the machine learning models. Among all the models, the Inception model showed the highest classification accuracy (87.5%) and the largest area under the receiver operating characteristic curve (AUC), indicating that the model is an effective method for near-fall (LOB) detection. Our approach can be helpful in identifying individuals at the risk of slip-related falls before they experience an actual fall.
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基于加速度计的秋季检测算法评估现实世界跌倒。
影响因子：
3.7
作者：
Bagalà F;Becker C;Cappello A;Chiari L;Aminian K;Hausdorff JM;Zijlstra W;Klenk J
通讯作者：
Klenk J
DOI：
10.3390/s18041275
发表时间：
2018-04-21
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Howcroft J;Lemaire ED;Kofman J;McIlroy WE
通讯作者：
McIlroy WE
影响因子：
2
作者：
Bhatt, T;Wening, JD;Pai, YC
通讯作者：
Pai, YC
影响因子：
8.1
作者：
Deng, Houtao;Runger, George;Vladimir, Martyanov
通讯作者：
Vladimir, Martyanov
影响因子：
4.6
作者：
Wang S;Varas-Diaz G;Bhatt T
通讯作者：
Bhatt T