HydroWatch

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  hydrowatch

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Inspiration

Hydration is a basic human need, yet many elderly, bedridden, Parkinson’s, and differently abled individuals struggle to drink water independently. Delayed assistance can lead to dehydration, kidney complications, infections, and reduced quality of life. We were inspired to create HydroWatch to promote dignity, independence, and proactive healthcare through AI-powered hydration assistance.

What it does

HydroWatch is an AI-powered smart hydration assistant that automatically helps users drink water safely. Using sensors, servo automation, and a smart straw, the system detects user presence and drinking intent, delivers water when needed, records hydration events, predicts dehydration risk, and alerts caregivers through a mobile application.

How we built it

The hardware prototype uses an ESP32 microcontroller, HC-SR04 ultrasonic sensor, capacitive touch sensor, MG996R servo motor, PCA9685 servo driver, L298N motor driver, and a 5V submersible water pump. Hydration data is transmitted via Wi-Fi and stored in Firebase Realtime Database as JSON records. Python, TensorFlow, and Scikit-learn are used to analyze hydration patterns, classify dehydration risk, and generate personalized recommendations. A Flutter mobile application provides caregiver alerts, monitoring, and hydration history.

Challenges we ran into

Developing reliable drinking-event detection without false triggers was challenging. We also faced difficulties integrating hardware, cloud connectivity, and AI analytics into a single platform. Creating meaningful hydration predictions from limited datasets and ensuring the system remained safe for vulnerable users required extensive testing and refinement.

Accomplishments that we're proud of

We successfully developed a working AI-enabled hydration assistance prototype that combines IoT hardware, cloud connectivity, machine learning, and mobile monitoring. The system not only automates water delivery but also learns individual hydration habits, predicts dehydration risks, and provides personalized support. We are proud that HydroWatch addresses a real healthcare challenge while remaining affordable and scalable.

What we learned

Through this project, we gained practical experience in embedded systems, sensor integration, cloud databases, mobile application development, machine learning, and responsible AI design. We also learned the importance of human oversight in healthcare AI systems and how data-driven insights can improve patient well-being.

What's next for HydroWatch

Future development will include larger clinical datasets, improved AI prediction accuracy, voice-assisted interaction, integration with wearable health sensors, multilingual caregiver support, and deployment in hospitals, rehabilitation centers, and elderly care facilities. We also plan to incorporate advanced analytics for personalized hydration planning and long-term health monitoring.

Built With

• c/c++-|-frameworks:-tensorflow
• capacitive-touch-sensor
• dart
• flutter-|-database:-firebase-realtime-database-|-cloud-service:-firebase-|-apis:-firebase-admin-sdk
• language:-python
• machine-learning
• rest-api-|-technologies:-esp32
• scikit-learn
• ultrasonic-sensor