Analyzing Wearables Dataset to Predict ADLs and Falls: A Pilot Study
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.