Detecting and Predicting Agitation Episodes in Dementia Patients Using IoT and Artificial Neural Networks

Dementia patients' agitation is a major problem for caregivers, who report greater rates of stress and costs connected to care. This research aims to determine if it can detect and predict agitation episodes in dementia patients by combining Internet of Things (IoT) devices with Artificial Neural Networks (ANNs). It continually monitors environmental and physiological variables like room temperature, noise levels, heart rate, and movement patterns by installing IoT sensors. A custom-built ANN is trained to detect patterns related to agitation episodes and feed the collected data. This method preprocesses raw sensor data using present techniques for signal processing to increase reliability and robustness. Data from multiple healthcare institutes helps the ANNs model recognize agitation. Early intervention may lessen the severity of these instances and forecast weight gain due to the model's ability to predict. The integration of IoT with ANNs may improve the quality of care for individuals with dementia and provide carers with actionable insights that enable enhanced, personalized therapy. This combination of technology has the potential to significantly enhance patient outcomes and optimize resource utilization in dementia care facilities.