Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living - Igiri, CG; Taylor, OE; Friday, Orji (International Journal of Computer Sciences and Engineering, 2021)
With the rapid growth in the elderly population, conventional health care system is no longer sufficient to provide personalized healthcare services for the elderly and healthcare givers are looking for a technological based solution. Ambient Assisted Living(AAL) is such a solution and at the heart of AAL is human activity recognition. Modern smartphone embedded with a lot of sensors has become an integral part of our life and is a vital option for collecting data for activity recognition. In this paper we looked at the use of smartphone accelerometer with supervised machine learning algorithm in WEKA framework for monitoring Activity of Daily Living (ADL): standing, walking, lying, walking upstairs and walking down stairs. Sitting, for the elderly in their environment of choice. We examined two common classification algorithms: Random Forest (RF), instance-based learning (KNN), RF gave us the highest accuracy of 94.4% which is considered adequate for activity recognition.