kccistc/intel-08
Sep 2025 – Sep 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
Action RecognitionComputer VisionDeep LearningDocumentationObject DetectionPose Estimation
Chanmi Kim
PROFILE
Chanmi Kim
During September 2025, Channeunim Kim developed a Fall Detection System for the kccistc/intel-08 repository, focusing on real-time safety monitoring. The system combined YOLO for person detection, SPPE for pose estimation, and ST-GCN for action recognition, forming an end-to-end pipeline that identifies individuals, estimates their poses, and classifies actions to detect falls. This approach leveraged deep learning and computer vision techniques, implemented primarily in Python with PyTorch. In addition to engineering the detection pipeline, Channeunim Kim updated project documentation in Markdown to reflect team changes. The work demonstrated depth in integrating multiple models for a practical, safety-critical application.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
5,524
Activity Months1
Your Network
27 peopleShared Repositories
27
Work History
September 2025
2 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for kccistc/intel-08. Delivered the Fall Detection System by integrating YOLO for person detection, SPPE for pose estimation, and ST-GCN for action recognition to detect falls. This end-to-end pipeline establishes components to detect individuals, estimate poses, and classify actions, enabling real-time fall detection in safety-critical contexts. Also updated project documentation to reflect team changes by adding a new member to README. Key commits are listed below for traceability.
2 Commits • 2 Features
Sep 1, 20252025-09 monthly summary for kccistc/intel-08. Delivered the Fall Detection System by integrating YOLO for person detection, SPPE for pose estimation, and ST-GCN for action recognition to detect falls. This end-to-end pipeline establishes components to detect individuals, estimate poses, and classify actions, enabling real-time fall detection in safety-critical contexts. Also updated project documentation to reflect team changes by adding a new member to README. Key commits are listed below for traceability.
September 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Action RecognitionComputer VisionDeep LearningDocumentationObject DetectionPose EstimationPyTorch
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