Otokodlayıcı Tabanlı Boyut Azaltma ve Akıllı Saat Tabanlı Giyilebilir Hareket Algılayıcıları Kullanarak Yaşlılarda Düşme Tespiti

Abstract

Falling is a serious health risk that can even result in death, especially for the elderly. For this reason, it is crucial to prevent falls and, in cases where prevention is not possible, to detect and intervene as soon as possible. Smartwatches are an ideal tool for fall detection due to their constant presence, rich sensor resources, and communication capabilities. The aim of this study is to detect falls in elderly people with high accuracy using motion sensor data obtained from smartwatches. To achieve this, a dataset was created consisting of falls and daily activities. Then, the feature vector was extracted which has provided successful results in signal processing studies. Afterward, the dimensionality of the dataset was reduced using an autoencoder-based approach in order to decrease the workload on smartwatches and ensure more accurate and faster classification. The dataset was classified using machine learning methods including naive Bayes, logistic regression, and C4.5 decision tree, and successful results were obtained. Their performances were then compared. It was observed that reducing the dimensionality had positive effects on both the classification accuracy and the computation time.