Neural Network-Centered Intelligent Recognizing Framework for Elderly Health Activity Supervision
Nilamadhab Mishra, Saroja Kumar Rout, Amit Thakur, Meshal Alharbi · 2025
Maintaining self-regulating life in old age anticipates high risks and often leads to failure. So, the objective of our study is to inspect the implementation feasibility of an intelligent sensing framework for self-regulating Elderly Health activity supervision. The computational intelligent tools are implemented to frame the intelligent sensing framework. The patterns in context to the walk-in activity of Elderly Health are analyzed along with statistical inference to identify the abnormal living patterns. Once the framework is embedded with intelligent tools, the backpropagation neural network algorithm is used for framework training and learning through three artificial neural models, namely NN1, NN2, and NN3, and finds approximately 96% implementation accuracy in the NN3 model. The activity level analysis is important to introduce the intelligence sensing framework into the Elderly's Health life. So, analysis and discussion are made to validate the feasibility of the framework implementation for Elderly Health activity supervision.