As we grow older, one of the most concerning aspects of our lives becomes increasingly challenging to manage our health. Fall is a leading cause of health problems or death in the elderly population. Using a wearable sensors device, this research presents a strategy for identifying and distinguishing fall activities from activities of daily living (ADL) in older persons. The conventional Machin learning method was applied by extracting features from telemetry data after pre-processing, and feature extraction. It is then compared to non-coding Automated Machine Learning (AutoML) method, where all the selected classifiers get automatically optimized. Furthermore, machine learning algorithms such as Support Vector Machine, K-Nearest Neighbor, Random Forest tree, and Artificial Neural Network are used to categorize acceleration signals as falling or regular activity. The test results indicate that AutoML can predict exceptionally accurate results in binary classifications with 99.9% accuracy on three of the four machine learning techniques it was tested against.
|الصفحات (من إلى)||16-23|
|دورية||Procedia Computer Science|
|المعرِّفات الرقمية للأشياء|
|حالة النشر||Published - 2022|
|الحدث||17th International Conference on Future Networks and Communications / 19th International Conference on Mobile Systems and Pervasive Computing / 12th International Conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022 - Niagara Falls, Canada|
المدة: أغسطس ٩ ٢٠٢٢ → أغسطس ١١ ٢٠٢٢
ASJC Scopus subject areas