Our stack roughly divides into three major components, the front-end, back-end and the data storage.

For the front-end, we have decided to go with React Native via Expo. This allows us to target both Android and iOS with a single codebase. Expo provides "managed workflows" and an SDK that will simplify development and deployment.

For the back-end, we have decided to use Python. Python is the language of choice for machine learning (ML). It has extensive support for traditional ML algorithms (e.g. random forests) via Scikit-Learn and the SciPy ecosystem. On top of this, our industry partner has provided us their current solution written in Python. We decided to expose the back-end as a REST API using FastAPI. This allows us to nicely separate concerns from the rest of the codebase. FastAPIs use of static type hints, validation with Pydantic, and automated documentation allows us to build better APIs faster.

For data storage we decided to use a MongoDB Atlas, a NoSQL database. We decided to use a NoSQL database because we need to store large amounts of data (e.g data from the wearable IMUs). Moreover, due to the ever changing nature of a startup we require flexibility. NoSQL databases are schema-free which enables us to modify our schema as we see fit.

We plan on using GitHub Actions (GA) to orchestrate our CI/CD. Given GAs broad support of languages and workflows, it's hard to go wrong with this decision. We will also be using GitHub for version control and project management, so having everything in one place is convenient.

The major components of our CI/CD for the backend will consist of black for autoformatting, flake8 for linting, pytest for unit-testing, and mypy for static type checking and codecov for coverage reporting. We plan to use separate Docker containers to package the back-end and front-end components and use Docker Compose to launch the app. This allows us to better separate concerns, manage dependencies, and ensure our app is deployable anywhere.