An Analysis of Current Fall Detection Systems and the Role of Smart Devices and Machine Learning in Future Systems

Fall detection and prevention is a critical area of research especially as senior populations grow around the globe. This paper explores current fall detection systems, and proposes the integration of smart technology into existing fall detection systems via smartphones or smartwatches to provide a broad spectrum of opportunities for data collection and analysis. We created and evaluated three ML classifiers for fall detection, namely, k-NN, SVM, and DNN using an open-source fall dataset. The DNN performed the best with an accuracy of 92.591%. Recommendations are also included that illustrate the limitations of current systems, and suggest how new systems could be designed to improve the accuracy of detecting and preventing falls.