Intelligent Assistance: Enhanced Real-time Patient-Specific Model for Fall Detection and Evaluation

Automated caretaking systems provide constant monitoring of people without human involvement. Intelligent frameworks automatically recognize and evaluate numerous sorts of data by making use of modern technologies. Human activities are complex practices that include continual together with interleaved activities. Complex activities can be quickly processed using fully connected quantum devices that work like the human brain. Human postures have the capability to reveal various movements of the body in diverse circumstances. An optimized approach is needed to integrate different environments and to recognize and explore various aspects of activities that improve acceptance rates. The proposed system can handle a wide range of body movements, various viewpoints, environmental changes, and obscuresituations. This research aims to design a real-time activityrecognitionsystem which tracks the movements of elderly people suffering from Parkinson's Disease(PD).