IEEE SPS @ UF

Ora — local-first workout tracker with voice logging

A deployable app for fast workout logging, structured training data capture, and longitudinal analysis of hypertrophy, strength, and endurance trends.

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Ora project logo.

Overview

Ora is built to reduce workout logging friction while generating high-quality structured data for ML-assisted progression analysis and coaching workflows.

Ora prioritizes daily usability: local-first storage for privacy and reliability, voice-first capture for speed, and clear summaries that make progression trends easy to interpret over time.

What I built

  • A product architecture centered on local-first persistence and offline-friendly usage.
  • Voice-to-structured-log workflows that reduce interaction cost during training sessions.
  • A roadmap from MVP logging to richer trend analysis and ML-assisted coaching features.

Snapshot

Track
IEEE SPS @ UF · app systems · health analytics
Status
Active MVP development with expanding analysis features.
Focus
Local-first dataVoice loggingTraining trend analysis

Stack

  • Flutter + Riverpod
  • SQLite
  • Speech pipeline (Vosk/Gemini-ready)

Glossary

Local-first
An architecture where core app functionality and data storage work on-device by default.