This project leverages IoT technology combined with Deep Learning to improve the management and care of patients with Alzheimer's Disease (AD). By utilizing advanced machine learning techniques, real-time monitoring, and location tracking, the system aims to provide enhanced support to patients and caregivers.
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Alzheimer's Disease Classification using Deep Learning:
- We employ MobileNetV2, a powerful deep learning model, to classify Alzheimer's disease by analyzing brain MRI scans.
- Achieved a high accuracy rate for classification.
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Real-time Monitoring with IoT Devices:
- Utilizes an IoT device to continuously measure and monitor physiological and environmental parameters such as:
- Temperature
- Humidity
- Heart rates
- SpO2 (Blood Oxygen Saturation)
- Instant notifications are sent to caregivers if any abnormal readings are detected.
- Utilizes an IoT device to continuously measure and monitor physiological and environmental parameters such as:
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Live GPS Tracking:
- The system tracks the real-time location of the patient using GPS, ensuring that the patient remains in a safe location.
- In case of an emergency or if the patient deviates from the safe zone, immediate help can be provided without delay.
- MobileNetV2 for Alzheimer’s disease classification from MRI scans.
- Real-time data collection for physiological and environmental parameters.
- GPS tracking for patient location and safety.
- Instant caregiver notification in case of abnormalities in data or patient location.