Mobile Application with AWS-Powered Analytics API for Wearable Devices

A mobile application was developed to provide users with insights based on measurements collected from a wearable device. The application utilizes Bluetooth technology to establish a connection with the wearable device and fetches measurement data. The collected data is then processed and analyzed using various AWS services, including Amazon Cognito User Pools, AWS API Gateway, AWS Lambda, Amazon SQS, Amazon DynamoDB, AWS Data Pipeline, AWS S3, and AWS SageMaker. This report outlines the key features and architecture of the mobile application.

Key Features:

Seamless User Authentication: The mobile application integrates with Amazon Cognito User Pools to provide a secure and seamless user authentication process. Users can log in to the application, and upon successful authentication, a token is returned, which is used for subsequent API requests.

Bluetooth Connectivity: The application utilizes Low-Energy Bluetooth technology to establish a connection with the wearable device. It retrieves measurement data wirelessly from the wearable device, ensuring convenience and ease of use for the users.

Real-time Insights: The collected measurement data is sent to AWS API Gateway along with the authentication token. AWS API Gateway routes the request to a Lambda function, which processes the data and generates immediate insights. These insights provide users with immediate feedback on their measurements.

Metadata Storage and Processing: Along with the real-time insights, the Lambda function also extracts relevant metadata from the measurement data. This metadata is sent to an Amazon Simple Queue Service (SQS) queue for further processing and storage.

Data Storage and Transformation: Another Lambda function continuously polls the SQS queue and retrieves the metadata, and stores it in the Amazon DynamoDB, a scalable and reliable NoSQL database service provided by AWS. Additionally, AWS Data Pipeline is configured to regularly copy the entire contents of the DynamoDB table as JSON into an Amazon S3 bucket for further data analysis.

AI Model Training and Prediction: The copied data in the S3 bucket is utilized by AWS SageMaker, a fully managed machine learning service, to train an AI model. The trained model generates predictive insights based on the measurement data. The model artifact is periodically updated, and the endpoint is refreshed to ensure accurate and up-to-date predictions.