Daines Kyle J F, Baddour Natalie, Burger Helena, Bavec Andrej, Lemaire Edward D
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4175-4178. doi: 10.1109/EMBC44109.2020.9176624.
Identifying people at risk of falling can prevent life altering injury. Existing research has demonstrated fall-risk classifier effectiveness in older adults from accelerometer-based data. The amputee population should similarly benefit from these classification techniques; however, validation is still required. 83 individuals with varying levels of lower limb amputation performed a six-minute walk test while wearing an Android smartphone on their posterior belt, with TOHRC Walk Test app to capture accelerometer and gyroscope data. A random forest classifier was applied to feature subsets found using three feature selection techniques. The feature subset with the greatest accuracy (78.3%), sensitivity (62.1%), and Matthews Correlation Coefficient (0.51) was selected by Correlation-based Feature Selection. The peak distinction feature was chosen by all feature selectors. Classification outcomes with this lower extremity amputee group were similar to results from elderly faller classification research. The 62.1% sensitivity and 87.0% specificity would make this approach viable in practice, but further research is needed to improve faller classification results.
识别有跌倒风险的人群可以预防改变生活的伤害。现有研究已证明基于加速度计数据的跌倒风险分类器对老年人有效。截肢人群同样应能从这些分类技术中受益；然而，仍需进行验证。83名下肢截肢程度不同的个体在腰部后方佩戴安卓智能手机进行了六分钟步行测试，使用TOHRC步行测试应用程序来采集加速度计和陀螺仪数据。将随机森林分类器应用于使用三种特征选择技术找到的特征子集。通过基于相关性的特征选择选出了准确率最高（78.3%）、灵敏度最高（62.1%）和马修斯相关系数最高（0.51）的特征子集。所有特征选择器都选择了峰值差异特征。该下肢截肢人群的分类结果与老年跌倒者分类研究的结果相似。62.1%的灵敏度和87.0%的特异性将使这种方法在实际中可行，但仍需进一步研究以改善跌倒者分类结果。
