- Objective: To simplify the evaluation of lung capacity or respiration rate for medical specialists
- Inhaling and Exhaling Rates are recorded using air pressure sensors and uploaded to a database using Wifi module in realtime, data is fetched, visualized and clustered (K Means), using unsupervised machine learning algorithms. 84 percent accuracy was achieved
- Designed and implemented a backend system for processing data from IoT devices, ensuring real-time data ingestion, storage, and transformation. The model is trained using various respiration types and rates to predict a positive outcome
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