Purdue-Artificial-Intelligence-in-Music/Evaluator-code
Sep 2025 – Oct 2025
2 Months active
Languages Used
KotlinJava
Technical Skills
Android DevelopmentComputer VisionMobile DevelopmentCameraXData AnalysisFile I/O
Michael Zhang
PROFILE
Michael Zhang
Michael Zhang contributed to the Purdue-Artificial-Intelligence-in-Music/Evaluator-code repository by developing features that enhanced gesture recognition and session analytics for mobile AI music evaluation. He implemented handedness-based hand landmark processing in Kotlin, isolating the target hand to reduce cross-hand noise and improve gesture accuracy. Michael also delivered session data capture and analytics using Android CameraX, introducing periodic data saving and per-user profiling with JSON serialization to track posture and hand metrics. Additionally, he fixed camera orientation logic to ensure reliable landmark tracking across front and back cameras. His work demonstrated depth in computer vision, data analysis, and robust mobile development.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
889
Activity Months2
Your Network
6 people
Work History
October 2025
3 Commits • 1 Features
Oct 1, 2025Concise monthly summary focused on business value and technical achievements for 2025-10 with emphasis on key features delivered, major bug fixes, impact, and skills demonstrated.
3 Commits • 1 Features
Oct 1, 2025Concise monthly summary focused on business value and technical achievements for 2025-10 with emphasis on key features delivered, major bug fixes, impact, and skills demonstrated.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code: Implemented handedness-based hand landmark processing to isolate the target hand and reduce cross-hand noise, enhancing gesture recognition reliability in evaluation workflows. This feature was delivered via targeted updates to HandLandmarkerHelper.kt across two commits, enabling a controlled switch to a specific handedness (Right, then Left) for more robust performance. Impact highlights: improved gesture recognition accuracy, cleaner evaluation inputs, and more dependable metrics for music AI gesture analysis. Skills demonstrated include Kotlin-based utility development, hand landmark processing, and disciplined version control. Business value: Higher reliability in gesture evaluation translates to more trustworthy AI music assessments and improved downstream results for research and product decisions.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code: Implemented handedness-based hand landmark processing to isolate the target hand and reduce cross-hand noise, enhancing gesture recognition reliability in evaluation workflows. This feature was delivered via targeted updates to HandLandmarkerHelper.kt across two commits, enabling a controlled switch to a specific handedness (Right, then Left) for more robust performance. Impact highlights: improved gesture recognition accuracy, cleaner evaluation inputs, and more dependable metrics for music AI gesture analysis. Skills demonstrated include Kotlin-based utility development, hand landmark processing, and disciplined version control. Business value: Higher reliability in gesture evaluation translates to more trustworthy AI music assessments and improved downstream results for research and product decisions.
Activity
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Quality Metrics
Correctness86.0%
Maintainability84.0%
Architecture84.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaKotlin
Technical Skills
Android DevelopmentCameraXComputer VisionData AnalysisFile I/OJSON SerializationKotlinMachine LearningMediaPipeMobile Development
Repositories Contributed To
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