🏃 RunTrail

The application prioritizes signal integrity, uninterrupted tracking, and minimal UI noise. Complex telemetry is reduced into a clean, high-contrast interface designed for outdoor visibility and immediate readability.

• RunTrail is a precision-focused GPS running tracker engineered for reliability under real-world constraints.
• It records routes, pace, distance, and elevation while maintaining consistent background execution even under aggressive system limitations.

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Features

1. Tracking

• Continuous GPS route recording with live polyline rendering
• Real-time pace, distance, and elapsed time calculation
• Elevation-aware tracking (device dependent)

2. Reliability

• Foreground service resistant to background termination
• Mid-run persistence to prevent data loss on crashes
• GPS signal filtering to reduce drift and spikes

3. History & Insights

• Persistent run history with detailed summaries
• Aggregated metrics across sessions
• Map-based replay of recorded routes

4. UI/UX

• Minimalist, distraction-free interface
• High-contrast design optimized for outdoor usage
• Large typography for real-time metrics

5. Advanced

• Location smoothing via filtering logic
• Audio coaching via Text-to-Speech (distance milestones)
• Custom Canvas charts for weekly distance visualization

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Tech Stack

Core

• Language: Kotlin
• UI: Jetpack Compose
• Architecture: Clean Architecture + MVVM

Data & State

• Local Storage: Room Database
• Async: Coroutines + Flow
• State Management: StateFlow

System & Services

• Location: FusedLocationProviderClient
• Maps: Google Maps SDK
• Background Execution: Foreground Service + NotificationManager

Dependency Injection

• Hilt

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Screenshots

Home	History	StartRun

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Architecture & Logic Overview

RunTrail follows strict separation of concerns using Clean Architecture mapped to MVVM.

FusedLocationProviderClient
        ↓
Repository (Flow)
        ↓
UseCases (Domain Layer)
        ↓
ViewModel (StateFlow)
        ↓
Jetpack Compose UI

Layers

Presentation

• Compose UI
• ViewModels
• Reactive state via StateFlow

Domain

• Business logic (UseCases)
• Pure Kotlin, no framework dependencies

Data

• Repository implementations
• Room database
• Location data sources

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Performance Considerations

• Batched database inserts during active tracking
• Controlled recomposition in Compose
• Efficient polyline rendering for large datasets

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Security & Privacy

• Fully offline architecture
• No external APIs or data transmission
• Location permissions handled with explicit user consent