论文标题
分析可穿戴设备数据集以预测ADL和FALLS：一项试点研究
Analyzing Wearables Dataset to Predict ADLs and Falls: A Pilot Study
论文作者
论文摘要
医疗保健是人类生活的重要方面。大流行后，在医疗保健中使用技术的流形增加了。文献中提出的基于物联网的系统和设备可以帮助长者，儿童和成人面临/经历健康问题。本文详尽地回顾了39个基于可穿戴的数据集，这些数据集可用于评估系统以识别日常生活和跌倒活动。使用五种机器学习方法，即逻辑回归，线性判别分析，k-nearest邻居，决策树和幼稚的贝叶斯对SIFFALL数据集进行比较分析。数据集以两种方式进行修改，首先使用数据集中存在的所有属性，并以二进制形式标记。第二，计算三个传感器值的三个轴（x，y，z）的幅度，然后用于实验标签属性。实验是对一个受试者，十个受试者和所有受试者进行的，并在准确性，精度和召回方面进行比较。从这项研究中获得的结果证明，KNN在准确性，精度和召回方面胜过其他机器学习方法。还可以得出结论，数据个性化提高了准确性。
Healthcare is an important aspect of human life. Use of technologies in healthcare has increased manifolds after the pandemic. Internet of Things based systems and devices proposed in literature can help elders, children and adults facing/experiencing health problems. This paper exhaustively reviews thirty-nine wearable based datasets which can be used for evaluating the system to recognize Activities of Daily Living and Falls. A comparative analysis on the SisFall dataset using five machine learning methods i.e., Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor, Decision Tree and Naive Bayes is performed in python. The dataset is modified in two ways, in first all the attributes present in dataset are used as it is and labelled in binary form. In second, magnitude of three axes(x,y,z) for three sensors value are computed and then used in experiment with label attribute. The experiments are performed on one subject, ten subjects and all the subjects and compared in terms of accuracy, precision and recall. The results obtained from this study proves that KNN outperforms other machine learning methods in terms of accuracy, precision and recall. It is also concluded that personalization of data improves accuracy.
