MicroHabit Coach is a mobile-native habit companion that suggests, tracks, and auto-completes micro‑habits using:
- Live content streams from the web (Hacker News + News API + OpenWeather)
- On-device intelligence (classification + FitScore calculation)
- Android sensors & services (activity recognition, geofencing, background workers, notifications)
The app now implements two fully functional Stream screens:
- Today Stream – your personalized habit dashboard with streaks, auto-completion, and real-time updates.
- Explore Stream – a smart suggestions feed built from public APIs, scored against your current context.
This top section describes the final shipped state of the project (Milestone 2 / Unit 9). All original Unit 7/8 planning content remains below for reference.
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Core Streams
- Today Screen (Stream #1)
- View all habits scheduled for today, grouped by completion status.
- One‑tap completion with visual feedback and streak updates.
- Auto‑completion from motion and geofence triggers.
- Summary banner: “X of Y habits completed today”.
- Explore Screen (Stream #2)
- Scrollable stream of suggestions built from Hacker News and News API content.
- Each suggestion shows title, source, FitScore (0–100), category, and context indicators.
- “Turn into Habit” converts any card into a pre‑filled habit in one tap.
- Today Screen (Stream #1)
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Habit Lifecycle
- Create Time, Motion, or Location based habits with a dynamic form.
- Edit and delete habits; all database records, notifications, and geofences stay in sync.
- Detailed Habit Detail screen with streaks, calendar history, and trends.
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Context-Aware Coaching
- Activity Recognition auto‑completes motion-based habits when walking/running thresholds are met.
- Geofenced Nudges trigger notifications when entering user-defined locations (e.g., gym, home).
- Dynamic notifications warn when streaks are at risk or when the user has been inactive for too long.
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Analytics & Settings
- Stats screen shows longest streaks, completion percentages, “best day of week,” and category breakdowns.
- Settings manages notifications, Quiet Hours, permissions, and data management (clear history / reset streaks).
- As a new user, I can go through onboarding, grant permissions, and land on a populated Today dashboard.
- As a user, I can create time, movement, or location‑based habits and see them on the Today screen.
- As a user, I can convert a suggestion from the Explore stream into a habit with one tap and minimal typing.
- As a user, I get notifications when:
- It’s time for a scheduled habit,
- I’ve been inactive for a long period, or
- I enter a geofenced location tied to a habit.
- As a user, I can see detailed history, streaks, and completion trends for each habit.
- As a user, I can change notification preferences, Quiet Hours, and clear my history from Settings.
- Architecture Pattern: MVVM with Room, ViewModel, LiveData, and Repository layers.
- Persistence
- Entities:
Habit,Completion,ApiSuggestion,SavedArticle,UserPreferences. - DAOs:
HabitDao,CompletionDao,ApiSuggestionDao,SavedArticleDao,UserPreferencesDao. AppDatabaseandDatabaseModulewire these together.
- Entities:
- Repositories
HabitRepository– source of truth for habits and completions; coordinates geofences and reminders when habits change.ApiRepository– orchestrates Hacker News + News API, caching suggestions in Room.WeatherRepository– wraps OpenWeather API and exposes current conditions for scoring.PreferencesRepository– stores user category preferences, Quiet Hours, and other settings.
- UI Layers
today/–TodayFragment,HabitItemAdapter,TodayViewModel.explore/–ExploreFragment,SuggestionAdapter,ExploreViewModel,ArticleDetailFragment.habit/–AddEditHabitFragment,AddEditHabitViewModel,HabitFormValidator.habitdetail/–HabitDetailFragment,HabitAnalytics,CalendarDayAdapter,CompletionHistoryAdapter.stats/–StatsFragment,StatsViewModel,ProfileStatsViewModel,StatsAnalytics.settings/–SettingsFragment,SettingsViewModel.onboarding/–OnboardingFragment,OnboardingAdapter,OnboardingStep.MainActivity– NavHost + app‑level initialization (activity recognition + geofence sync).
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Hacker News API (
HackerNewsApi.kt)- Fetches top story IDs and individual items.
- Filters posts by habit‑relevant keywords (e.g., workout, exercise, health, productivity).
- Maps results to
ApiSuggestionentities stored locally.
-
News API (
NewsApi.kt)- Optional backup provider for health/fitness/productivity articles.
- Normalized into the same
ApiSuggestionmodel so the Explore stream is backend‑agnostic. - Used conservatively to respect free‑tier rate limits.
-
OpenWeather API (
WeatherApi.kt)- Provides current weather for the user’s approximate location.
- Consumed by
WeatherRepositoryand injected into the FitScore calculation.
-
Classification & FitScore
HabitClassifierassigns categories: Fitness, Wellness, Productivity, Learning, or General using keyword rules only (no ML).FitScoreCalculatorcombines:- User category preferences (
UserPreferences/UserContext), - Time of day,
- Weather conditions from OpenWeather,
- Proximity to important locations,
- Recent motion state (from activity recognition).
- User category preferences (
- Explore cards display the resulting score and are sorted by FitScore so the most context‑appropriate ideas surface first.
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Activity Recognition & Auto-Completion
- Implemented with
ActivityRecognitionService,ActivityTransitionReceiver,ActivityDurationTracker, andActivityRecognitionWorker. - Detects walking/running/stationary states using the ActivityTransition API.
- Motion-based habits are automatically marked complete when configured duration thresholds are met, updating the Today stream in real time.
- Implemented with
-
Geofencing
- Core logic in
GeofenceServiceandGeofenceBroadcastReceiver, with user‑visible notifications viaGeofenceNotificationHandler. - Integrated across the habit lifecycle:
AddEditHabitViewModel– creates/updates/removes geofences when saving or editing location habits or changing type.TodayViewModel– removes geofences when deleting location habits from Today.MainActivity– calls a sync routine on startup to ensure Android’s geofence registry exactly matches active location habits in Room.
- Handles permission failures gracefully and respects Android’s geofence count limits.
- Core logic in
-
Notifications & Background Work
- Workers:
HabitReminderWorker,StreakCountdownWorker,InactivityDetectionWorker. - Coordination layer:
NotificationManager,NotificationService,ReminderScheduler,NotificationActionReceiver. - Supports:
- Time‑based reminders for scheduled habits via WorkManager.
- Streak‑saving countdown notifications late in the day.
- Inactivity nudges after prolonged stillness.
- Geofence‑triggered notifications when entering defined locations.
- Action buttons (Mark Done, Snooze) that update Room directly without forcing users into the app.
- Workers:
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Streams & Screens
- Both Streams (Today and Explore) are implemented and driven by real data.
- All specified archetypes exist: Onboarding, Today, Explore, Habit Detail, Add/Edit, Profile/Stats, Settings.
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Required Features (Product Spec)
- Implemented: Today Stream, Explore Stream, classification + FitScore, full habit CRUD, detail analytics, stats, settings, activity recognition auto‑completion.
- Geofencing: infrastructure plus full lifecycle integration (create/edit/delete/startup sync) are complete and demo‑ready.
- Notifications: time, streak, inactivity, and geofence notifications wired through WorkManager and notification actions.
-
Optional / Stretch
- Weather-aware scoring via OpenWeather is implemented.
- Deep social features and full cloud sync are intentionally deferred beyond this milestone.
For a detailed spec‑vs‑implementation matrix and geofencing deep dive, see context/IMPLEMENTATION_STATUS.md and context/GEOFENCING_INTEGRATION.md.
-
Geofencing Lifecycle
- Challenge: keeping Android’s geofence registry exactly in sync with Room as habits change.
- Decision: centralize geofence operations in
GeofenceServiceand call it only fromAddEditHabitViewModel,TodayViewModel, andMainActivity, ensuring a single source of truth.
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Battery vs. Context Awareness
- Challenge: using motion and location data aggressively without draining the battery.
- Decision: rely on the ActivityTransition API for coarse transitions, track durations only while “in motion,” and use low‑power geofences with a one‑time sync on startup instead of constant churn.
-
API Reliability & Limits
- Hacker News is free/unlimited but noisy; News API and OpenWeather are structured but rate‑limited.
- Decision: use HN as the primary stream, persist suggestions locally, and treat News API and OpenWeather as context enhancers rather than hard dependencies.
-
Authentication Scope
- The original spec included login; for this course milestone, we focused engineering effort on the Streams, automation, and sensors.
- The current build uses a lightweight onboarding-first flow; full auth can be added in a future iteration without changing the core architecture.
-
Prerequisites
- Android Studio (Giraffe or newer recommended).
- Android SDK 26+ with Google Play Services.
- Physical Android device strongly recommended to demo geofencing and motion detection reliably.
-
Clone & Open
- Open the
MicroHabitCoachproject in Android Studio. - Allow Gradle to sync and download all dependencies.
- Open the
-
API Keys
- Add your keys in
local.properties(or the configured Gradle location) for:- News API (optional)
- OpenWeather API
- If keys are missing, the app still functions with Hacker News only; weather‑based scoring simply degrades gracefully.
- Add your keys in
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Running the App
- Build and install the debug build on your device/emulator.
- On first launch:
- Complete onboarding and grant Location, Activity Recognition, and Notification permissions.
- Create at least one time, motion, and location habit.
- Optionally walk around or move into a configured geofence to see auto‑completion and geofence notifications in action.
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Demo Video & GIFs
- The “App Demo Video” and “Current Progress GIFs” sections later in this README contain links showing:
- Today stream usage and habit completion,
- Explore stream suggestions and “Turn into Habit” flow,
- Stats and Settings interactions,
- (Where possible) examples of motion and geofence‑driven completions.
- The “App Demo Video” and “Current Progress GIFs” sections later in this README contain links showing:
-
Project Management
- Screenshots of the GitHub Project board and sprint breakdowns are included in the Milestone 2/3 sections below.
- They document how user stories moved from TODO → In Progress → Done across Units 8 and 9.
Everything below this line is the original milestone planning and specification content, preserved for grading and historical context.
MicroHabit Coach is a mobile-native behavior engine that solves two critical problems that cause habit-tracking apps to fail:
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Decision Fatigue: Users don't know what habits to create or how to start. Most apps present a blank slate, forcing users to invent habits from scratch—a cognitive burden that leads to abandonment.
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Lack of Context-Aware Suggestions: Traditional habit trackers are static. They remind you at fixed times regardless of your location, activity level, or current state. This creates friction when the reminder doesn't match reality (e.g., "Go for a walk" when you're already walking, or "Drink water" when you're nowhere near water).
Our Solution: MicroHabit Coach is a mobile-native behavior engine that:
- Continuously suggests context-aware habits from live API feeds (Hacker News/News API), eliminating decision fatigue
- Auto-completes habits when your phone detects you've already done the work (motion detection, geofencing)
- Scores suggestions in real-time based on your location, weather, time of day, and movement state
- Converts any suggestion into a trackable habit with one tap
The app doesn't just track habits—it actively generates, scores, and verifies them using your phone's sensors and contextual data. This is impossible on desktop and requires true mobile-native capabilities.
Category: Productivity / Health & Fitness
Mobile: ⭐⭐⭐⭐⭐ Very High
More than a glorified website: The app fundamentally relies on mobile sensors and background services that cannot exist on desktop.
Two or more mobile-specific features (we have 4+):
-
Activity Recognition / Motion Detection (Android ActivityRecognitionClient)
- Detects walking, running, stationary states
- Auto-completes movement-based habits when thresholds are met
- Continuously monitors in background via WorkManager
-
Geofencing / Location Services (FusedLocationProviderClient)
- Place-based habit triggers (e.g., "Hydrate at Gym" activates when entering gym geofence)
- Location-aware FitScore calculation for API suggestions
- Low-power background monitoring
-
Push Notifications / Real-time Alerts (WorkManager + NotificationManager)
- Dynamic streak countdown notifications ("2 hours left to save your streak")
- Contextual nudges based on inactivity ("You've been still for 2 hours")
- Geofence-triggered reminders
- Actionable notification buttons (Mark Done, Snooze)
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Real-time Data Updates (LiveData + Room + Background Services)
- Continuous sensor data processing
- Real-time habit completion updates
- Background API fetching and scoring
- Widget updates without app launch
Story: ⭐⭐⭐⭐ High
The Pitch: "MicroHabit Coach is the only habit app that suggests habits based on your phone's context and auto-completes them when your device detects you've done the work. When you run out of ideas, the app pulls real exercise discussions from Hacker News, analyzes them locally using your location and movement data, and turns them into one-tap habits that sync with your motion and geofence triggers."
Why it's compelling:
- Solves a real, relatable problem (decision fatigue + context mismatch)
- Demonstrates clear Android-native intelligence (not just a database)
- Shows technical sophistication (API integration, local classification, sensor fusion)
- Has a clear demo narrative: "Watch as the app suggests a workout, you tap to create it, and it auto-completes when you start walking"
Market: ⭐⭐⭐⭐ Large
Target Audience:
- Primary: Students and professionals (18-35) seeking structured habit reinforcement
- Secondary: Fitness enthusiasts and athletes who want context-aware workout suggestions
- Tertiary: Anyone struggling with habit consistency due to decision fatigue
Market Size: The productivity/health app market is massive (millions of users), but most apps are generic trackers. Our context-aware, suggestion-driven approach addresses an underserved niche.
Unique Value: Unlike generic habit trackers (Habitica, Streaks), we combine:
- External content discovery (API feeds)
- Local intelligence (classification, scoring)
- Sensor-based verification (auto-completion)
- Context-aware suggestions (location, weather, time, motion)
Habit: ⭐⭐⭐⭐ High
Habit-forming mechanisms:
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Positive Reinforcement:
- Streak counters and visual progress indicators
- Completion animations (confetti, color transitions)
- Analytics showing consistency metrics
- "Best Day of the Week" insights
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Negative Reinforcement (Urgency):
- Streak countdown timers ("2 hours left to save your 7-day streak")
- Home widget showing incomplete habits
- Push notifications with actionable buttons
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Reduced Friction:
- One-tap completion
- Auto-completion via sensors (no manual logging needed)
- Pre-filled habit creation from API suggestions
- Context-aware reminders (right time, right place)
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Continuous Engagement:
- Daily habit list (Today screen)
- Fresh suggestions from API feed (Explore screen)
- Real-time updates as habits complete
- Weekly/monthly analytics review
Average user opens app: Daily (to check Today screen, mark completions, browse suggestions)
Average user creates content: Regularly (converts API suggestions to habits, creates custom habits)
Scope: ⭐⭐⭐⭐⭐ Excellent
Appropriately technically challenging:
- Room database (CRUD operations)
- Retrofit for API calls (Hacker News/News API)
- Activity Recognition API integration
- Geofencing with FusedLocationProviderClient
- WorkManager for background tasks
- LiveData/ViewModel architecture
- Navigation Component
- Notification system
- Optional: AppWidget (stretch goal)
Stripped-down version still interesting:
- MVP: Today screen (habits list), Add/Edit habit, basic notifications, simple API feed
- Even without auto-completion, the suggestion feed + manual tracking is valuable
- Core value proposition (decision fatigue solution) remains intact
Product is clearly defined:
- Two Stream screens (Today = habits, Explore = API suggestions)
- Clear navigation flows
- Well-defined user features (required vs optional)
- Technical implementation path is clear
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Account & Onboarding
- Create account; choose from smart templates (e.g., Hydrate at Gym, Move Break, Walk 10 min, Read 10 pages) or create a custom habit.
- No hard limit on habits; soft guidance to 3–6 for clarity (UI stays performant).
- The onboarding process guides users through permission setup for motion and location. It also introduces the app’s core features and explains how automated completion works.
- User data and settings are stored locally via Room for speed and privacy, with future compatibility for cloud sync.
- Contextual setup ensures habits are intelligently categorized (time, movement, or location), reducing user confusion during initial configuration.
-
Today Screen (Stream #1 - Habits List)
- Displays all habits scheduled or active for the current day, grouped by completion state.
- Scrollable list of discrete habit items (Stream archetype).
- Each habit card shows:
- Habit name and icon
- Progress bar/ring
- Streak count
- Next reminder time
- Completion status (checked/unchecked)
- Users can complete any habit with a single tap, triggering immediate visual feedback such as confetti or color transitions.
- Summary banner at top: "X of Y habits completed today"
- Real-time updates as sensor-triggered completions occur (LiveData).
-
Explore Screen (Stream #2 - API Suggestions Feed)
- Scrollable stream of habit suggestions from Hacker News API (primary) or News API (backup).
- Each suggestion card displays:
- Post title and snippet
- Source badge (Hacker News/News API)
- FitScore (0-100) with visual indicator
- Category badge (Fitness, Wellness, Productivity, Learning)
- Context indicators (time-appropriate, weather-appropriate, location-appropriate icons)
- "Turn into Habit" button
- Stream sorted by FitScore (highest relevance first).
- Pull-to-refresh to fetch new suggestions.
- Tap "Turn into Habit" → opens Creation screen pre-filled with:
- Habit name (extracted from post title)
- Category (from classification)
- Suggested type (Time-based, Motion-based, or Location-based)
- Suggested parameters (duration, location, etc.)
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Habit Classification & FitScore System
- Local classification (no AI/ML, simple keyword matching in Kotlin):
- Parses post title and content for keywords
- Categories: Fitness, Wellness, Productivity, Learning, General
- Sub-classifications: Indoor/Outdoor, Short/Long duration
- FitScore calculation (0-100):
- Base score: 50
- +20 if matches user's preferred categories
- +15 if time-appropriate (e.g., "morning workout" at 7am)
- +10 if weather-appropriate (e.g., "outdoor run" when sunny)
- +5 if location-appropriate (e.g., "gym workout" near gym geofence)
- +10 if motion-state matches (e.g., "walk" when user is walking)
- Runs entirely on-device (no cloud processing).
- Local classification (no AI/ML, simple keyword matching in Kotlin):
-
Scheduled & Dynamic Notifications
- Time-based reminders use WorkManager for reliable background execution, even in doze mode.
- Dynamic notifications provide streak-saving alerts (“You have 2 hours to save your streak”) and adapt based on user behavior or inactivity.
- Each reminder can trigger actionable buttons (e.g., “Mark Done” or “Snooze”) to minimize app entry friction.
- Duplicate or redundant notifications are automatically filtered to maintain a clean user experience.
-
Activity Recognition Assist (base)
- Integrates Android’s ActivityRecognitionClient to detect transitions such as walking, running, or being stationary.
- Movement-type habits like Walk 10 min or Move Break can be automatically marked complete once motion duration or intensity meets the threshold.
- Sensor sampling is optimized to conserve battery by reducing polling intervals when inactive.
- Logs each recognized activity event for analytics and future machine learning recommendations.
-
Geofenced Nudges (base)
- Implements FusedLocationProviderClient for low-power geofencing.
- Place-based habits such as Hydrate at Gym or Stretch at Home activate a notification when the user enters a predefined location radius.
- Users can define radius sensitivity to minimize false triggers.
- Permission fallback gracefully disables this feature and reverts to time-based reminders with clear in-app messaging.
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Habit Detail & History
- Provides detailed streak tracking, performance summaries, and trend visualization.
- Calendar and list modes offer both high-level and granular views of progress.
- Each completion entry is logged in Room and rendered dynamically through LiveData for instant updates.
- History analytics display seven-day and thirty-day completion percentages, helping users monitor adherence and growth.
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Create/Edit/Delete Habit
- Users can create new habits or edit existing ones with a guided form interface that adapts to habit type.
- Options include naming, target count, time scheduling, and optional motion or location triggers.
- Smart validation prevents setting conflicting reminders and limits excessive notification frequency.
- Deleting a habit removes its associated triggers, database entries, and notification schedules cleanly.
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Settings
- Users can toggle notifications, configure Quiet Hours, manage permissions, and enable battery optimization.
- Includes access to a “Data Management” section where users can clear history or reset streaks.
- Battery-friendly mode automatically adjusts sensor update intervals and background job frequency.
- Provides direct navigation to Android’s system settings for location or motion permissions.
-
Home-Screen Widget
- Displays remaining habits for the day with completion shortcuts.
- Widget updates dynamically as habits are completed or auto-marked through sensors.
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Lite Social Accountability
- Allows users to share habit streaks or completion milestones with friends for external motivation.
- Designed as a read-only sharing mode to maintain privacy and simplicity.
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Cloud Backup/Sync
- Optional Firebase integration for cross-device data storage.
- Synchronizes habit configurations, completion history, and streaks to maintain continuity when reinstalling or switching devices.
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Multi-place Geofences & Advanced Rules
- Allows linking multiple geofences to one habit (e.g., “Any Gym” or “Home or Park”).
- Users can apply frequency conditions such as “only trigger once every 6 hours.”
- Adds scheduling hierarchy for smarter trigger prioritization.
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Login / Onboarding
- Users authenticate through email or Google Sign-In.
- Smart templates displayed as visual cards to help first-time users select practical examples quickly.
- Permissions for motion and location are requested contextually with justifications for each use.
- Includes micro-animations and clear progress indicators to maintain engagement during setup.
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Today (Stream #1)
- Displays an adaptive list of active habits prioritized by urgency and streak risk.
- Scrollable list of discrete habit items (Stream archetype).
- Uses color-coded progress rings and completion toggles for quick scanning.
- A summary banner at the top shows total habits completed and time remaining to preserve streaks.
- Background service continuously updates this list based on motion and geofence triggers.
- Real-time updates via LiveData as sensor-triggered completions occur.
-
Explore (Stream #2)
- Scrollable stream of API suggestions (Stream archetype).
- Each suggestion is a discrete item with title, FitScore, category, and action button.
- Sorted by FitScore (highest relevance first).
- Pull-to-refresh to fetch new suggestions from Hacker News/News API.
- Mobile-native: Uses location, weather, time, and motion state to calculate FitScore in real-time.
- "Turn into Habit" button opens Creation screen with pre-filled data.
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Add / Edit Habit (Creation)
- Includes type selector: Time-based, Movement-based, or Location-based.
- Dynamic forms automatically expand or hide fields based on habit type.
- Built-in data validation ensures that entered times, motion thresholds, and locations are realistic.
- Each habit preview updates in real time to show how it will appear on the Today screen.
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Habit Detail
- Displays a detailed breakdown of streak length, best streak, completion percentage, and trend over time.
- Users can toggle between list and calendar history views.
- Edit and delete options remain accessible at the top for immediate changes.
- Designed for responsive performance even with extensive historical data.
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Profile / Stats
- Summarizes user performance through aggregate statistics.
- Highlights longest streak, completion rate, and consistency metrics.
- Provides motivational insights such as “Most Consistent Habit” and “Best Day of the Week.”
- Built using chart components for visual feedback on habit adherence.
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Settings
- Consolidates global preferences, notifications, permissions, and backup options.
- Quiet Hours configuration uses sliders for intuitive selection.
- Visual permission status indicators (granted, denied, pending) improve transparency.
- Includes system shortcuts for motion and location permission management.
- Today → Today Screen (Stream #1 - Habits List)
- Explore → Explore Screen (Stream #2 - API Suggestions Feed)
- Stats → Profile/Stats Screen
- Settings → Settings Screen
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Login/Onboarding → Today
Directs new users to their first-day dashboard after initial setup. -
Today → Habit Detail
Opens detailed progress view of a selected habit (tap on habit card). -
Today → Add/Edit Habit
Launches creation flow via FAB or "Add Habit" button; returning automatically updates the Today list. -
Today → Explore
Navigate via tab navigation to browse API suggestions. -
Explore → Add/Edit Habit
Tap "Turn into Habit" button on any suggestion card; opens Creation screen with pre-filled data (name, category, type, parameters). -
Explore → Today
Navigate via tab navigation to return to habits list. -
Habit Detail → Add/Edit Habit
Tap Edit button; preserves data context for immediate modifications. -
Habit Detail → Today
Back button or navigation returns to Today screen. -
Add/Edit Habit → Today
After saving, returns to Today screen and updates the list. -
Add/Edit Habit → Habit Detail
If editing existing habit, can navigate to detail view. -
Profile/Stats → Habit Detail
Tap on any habit metric to see detailed analytics for that habit. -
Profile/Stats → Settings
Navigate via tab navigation or overflow menu. -
Settings → System Permission Screens
Opens Android system dialogs for adjusting key permissions (Notifications, Activity Recognition, Location).
Navigation emphasizes logical flow with minimal friction. Transitions between screens use smooth animations to maintain focus and continuity. The design prioritizes clarity, ensuring that users always understand where they are and how to return to the main dashboard.
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Onboarding
Introduces templates, permission requests, and contextual guidance for first-time setup.
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Today (list + big “Mark Done”)
Central dashboard displaying all active habits and real-time progress.
-
Add/Edit (type selector → dynamic form)
Context-sensitive form layout adjusting dynamically to chosen habit type.
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Habit Detail (streak + history)
Data visualization of performance trends with quick edit access.
-
Stats (completion %, longest streak)
Presents user analytics using charts and summaries.
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Settings (quiet hours, permissions)
Displays notification control, Quiet Hours scheduling, and permission overview.
Demo walkthrough
Demonstrates complete user flow: onboarding, creating a habit, triggering via motion/location, marking completion, and viewing analytics.
This Unit's Progress
Sprints
Goal: Build the base architecture, database, and main user flow (CRUD + Today screen).
Due: End of Unit 8
- Initialize Android Studio project (
com.microhabitcoach) - Set up GitHub repo and README (Milestone 1 complete)
- Create GitHub Project Board (To Do / In Progress / Done)
- Create three Sprint Milestones (for build weeks)
- Define coding conventions and branching strategy
- Implement Room Database (
Habit,CompletionLogentities + DAO) - Create Repository Layer connecting DAO ↔ ViewModel
- Build Habit ViewModel and LiveData for UI binding
- Verify database CRUD operations (unit tests or logs)
- Build Today Dashboard UI – list of habits (grouped by completion state)
- Add Mark Complete button (one-tap completion with visual feedback)
- Create Add/Edit Habit screen with dynamic form for type selection
- Add Delete Habit function (removes triggers and database entries)
- Implement Navigation Component (Today ↔ Add/Edit ↔ Detail)
- Create placeholder Habit Detail screen (streak and history mock)
- Create basic onboarding screens (Google Sign-In mock optional)
- Add permission prompt stubs for motion and location (contextual UI only)
- Test Add → Display → Complete → Delete flow
- Update README with com
Milestone 3 - Build Sprint 2 (Unit 9)
GitHub Project board
Completed User Stories (Sprint 1 - Milestone 2)
- Project Setup & Architecture - Android Studio project with dependencies and MVVM structure
- Room Database Setup - All entities, DAOs, and database class implemented
- Repository Layer - HabitRepository and ApiRepository with LiveData observables
- Navigation Setup - Navigation Component with all screen destinations and bottom navigation
- Login & Onboarding UI - Login and onboarding screens with permission requests
- Today Screen UI - Scrollable habit list with summary banner and empty states
- Add/Edit Habit UI - Dynamic form based on habit type (Time/Motion/Location)
- ViewModels - TodayViewModel and AddEditHabitViewModel with reactive LiveData
- Basic CRUD Operations - Create, Read, Update, Delete habits with persistence
- One-Tap Completion - Habit completion with visual feedback (confetti, animations) Total: 10/10 Sprint 1 issues completed
App Demo Video Video
Current Progress GIFs