Body Pose Detection App using Google ML-Kit Flutter

A Flutter app that detects body posture using Google ML Kit. It tracks 33 skeletal points on the face, hands and feet from the camera, working for full or partial body views, which suits fitness and posture correction apps.

The user installs the app and signs in; credentials are checked against Firebase Authentication or the app's own API. Screens built in XML (or Jetpack Compose) collect the user's input and show results in lists and cards. Data is stored in Firebase Realtime Database / Firestore or a local SQLite database and synced when the phone is online. Notifications and background services alert the user to updates, and an admin side manages the records.

An Activity moves through onCreate, onStart, onResume (running), onPause, onStop and onDestroy. Saving state in onPause or onSaveInstanceState prevents data loss when the user switches apps or rotates the screen.

Firebase gives a hosted real-time database, authentication and push notifications without running our own server. SQLite stores data locally on the phone and works offline. We chose based on whether data must be shared between users.

We compared a few suitable algorithms (for example logistic regression, decision tree, random forest and SVM) on the same train-test split and chose the one with the best balance of accuracy, recall and speed for this data.

Accuracy is correct predictions over all predictions. Precision is how many predicted positives are truly positive. Recall is how many actual positives were found. When classes are imbalanced, precision and recall matter more than accuracy.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.