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1. ML-based Smart Wearable Device for Alzheimer's Patients Abhishek B Abhishek MS Adarsh AS Aromal S

2. CONTENTS Introduction Existing System Gap analysis Problem Statement Conclusion Reference

3. Introduction Alzheimer’s disease causes progressive memory loss and cognitive decline affecting millions globally.
Patients often face challenges like disorientation, wandering, and difficulty in performing daily tasks.
Smart wearable devices can continuously monitor patients’ physiological and behavioural data.
Machine Learning (ML) algorithms analyse this data to detect patterns and predict potential risks.
Early alerts for issues like wandering or sudden health changes can be generated, improving patient safety.
The ML-based system adapts to individual patient needs, offering personalized care and support.

4. Existing Systems Wearables Current wearable devices track activity, location, and vitals, but lack personalized assistance and data-driven insights. Smart Home Home automation systems can monitor environments, but do not directly engage with or assist Alzheimer's patients. Caregiver Apps Apps provide information sharing, but don't integrate with wearables or the home to deliver real-time support.

5. Gap Analysis 1 Personalized Assistance Existing solutions lack the ability to provide customized, in-the-moment support for Alzheimer's patients. 2 Comprehensive Monitoring Current systems do not seamlessly integrate data from wearables, the home, and caregivers to enable proactive interventions. 3 Data-Driven Insights There is a need for an intelligent system that can analyze data to provide meaningful recommendations for caregivers and healthcare providers.

6. Problem Statement Enhance Safety Develop a smart wearable system that can monitor Alzheimer's patients and intervene to prevent dangerous situations. Increase Independence Enable Alzheimer's patients to maintain their independence and routines for longer by providing personalized assistance. Improve Caregiving Empower caregivers and healthcare providers with data-driven insights to make more informed decisions.

7. Improved Safety and Independence 1 Monitoring Detect changes in behavior, location, and vital signs to identify potential safety risks. 2 Personalized Assistance Provide real-time prompts, reminders, and guidance to help patients navigate their environment and daily routines. 3 Emergency Response Automatically alert caregivers or emergency services if the patient is in danger, enabling rapid intervention.

8. Real-Time Monitoring and Personalized Assistance Vital Signs Continuous monitoring of heart rate, blood pressure, and other health indicators. Location Tracking GPS and indoor positioning to ensure patients stay within safe, designated areas. Routine Support Personalized reminders and guidance to help patients maintain their daily routines. Real-Time Aid Immediate, context-aware interventions to prevent dangerous situations or provide helpful information.

9. Data-Driven Insights for Caregivers and Providers Data Collection Aggregate data from wearables, home sensors, and caregiver inputs. Pattern Analysis Utilize machine learning to identify trends and anomalies in patient behavior and health. Personalized Insights Generate customized reports and recommendations to support caregiving and treatment decisions.

10. Conclusion and Future Developments 1 Transformative Impact ML-based smart devices can revolutionize Alzheimer's care, enhancing safety, independence, and quality of life. 2 Ongoing Improvements Continuous advancements in sensors, AI, and user experience will further refine the system's capabilities. 3 Expanding Applications The technology can be adapted to support other cognitive impairments and aging-related conditions.

11. References Breijyeh , Z.; Karaman , R. Comprehensive Review on Alzheimer’s Disease: Causes and Treatment. Molecules 2020, 25, 5789, https://doi.org/10.3390/molecules252 Devi, S.K.; Amirthavarshini , D.; Anbukani , R.; Ranjanni , S.B. Personal Assistance for Alzheimer’s Patient. In proceedings of the 2020 4 th International Conference on Computer, Communication and Signal Processing (ICCCSP), Chennai, India, 28–29 September 2020, https://doi.org/10.1109/ICCCSP49186.2020.9315250 Lu, D.; Yan, L. Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor. J. Sensors 2021, 2021, 1–16, https://doi.org/10.1155/2021/4796768