4. Technical Implementation

Structured by coursework requirements: System Architecture, High-Fi Prototype, Individual Contributions.

System Architecture

Input Layer: mood check-in, run preference, optional context tags.

Logic Layer: rule-based adaptation engine selects run mode and prompt strategy.

Interaction Layer: guided run screen, adaptive prompts, post-run reflection card.

Data Layer: stores session state, mood trend, and feedback notes.

Action: insert architecture diagram image in this section.

Data Handling Flow

  • User input is validated on the client side before state update.
  • Mood + context values map to predefined adaptive recommendation rules.
  • Session outcomes are recorded for next-run personalization.
  • No sensitive identity data is required for the core prototype flow.

High-Fi Prototype

Hosted system URL and repository references.

Implemented Core Features

  • Feature 1: Mood check-in and adaptive run mode generation.
  • Feature 2: In-run prompt adaptation by user state.
  • Feature 3: Post-run reflection and suggestion output.

Individual Contributions

Clear table of what each team member built.

Member Role Main Contribution Evidence
Member A Research & Requirement User interviews, journey mapping, requirement drafting Interview notes, requirement docs
Member B Interaction Design Low-fi and high-fi screens, interaction flows Figma links, design exports
Member C Frontend Development Implemented UI pages and adaptive feature logic Commits, feature demos
Member D Testing & Reflection Usability testing, issue tracking, final reflection Test sheets, before/after updates