Activity Detection of Elderly People UsingSmartphone Accelerometer and Machine Learning Methods

Elderly activity detection is one of the significantapplications in machine learning. A supportive lifestyle can help older people with their daily activities tolive their lives easier.But the current system is ineffective, expensive, and impossibleto implement. Efficient and cost-effective modern systems are needed to address the problems of agedpeople and enable them to adopt effective strategies. Though smartphones are easily accessible nowadays, thus a portable and energy-efficient system can be developed using the available resources. This paper issupposedto establishelderly people's activity detection based on available resourcesin terms of robustness, privacy, and costeffectiveness. We formulateda private dataset by capturing seven activities,including working, standing, walking, and talking, etc. Furthermore,we performed various preprocessing techniques such as activity labeling, class balancing, and concerningthe number of instances. The proposed system describes how to identify and classify the daily activities of older people using a smartphone accelerometer to predict future activities. Experimental resultsindicate that the highest accuracy rate of 93.16% has been achieved by using the J48 Decision Tree algorithm.Apart from the proposed method, we analyzed the results by using various classifiers such as Naïve Bays (NB), Random Forest (RF), and Multilayer Perceptron (MLP). In the future, various other human activities likeopening and closing the door, watching TV,and sleeping can also be considered for the evaluation of the proposed model.