Advanced multimodal AI system for analyzing human motion, rhythm, and breathing patterns.
This project implements a multimodal artificial intelligence system designed for analyzing human movement, temporal synchronization, and physiological signals.
The system combines computer vision, audio signal processing, and breathing detection to evaluate structured movement sequences (e.g., karate kata) and explore human motion consistency, rhythm alignment, and breathing patterns.
This work extends beyond traditional computer vision tasks and moves toward integrated human behavior and biometric signal analysis.
- Motion analysis using computer vision techniques
- Audio signal processing and rhythm detection
- Breathing detection and physiological signal analysis
- Synchronization between motion, rhythm, and breathing
- Multimodal data fusion (vision + audio + physiological signals)
- Experimental evaluation of temporal alignment and consistency
The system is composed of multiple interacting components:
- Movement tracking and motion pattern extraction
- Frame-level analysis of structured sequences
- Rhythm detection
- Timing and frequency analysis
- Analysis of breathing-related signals
- Temporal pattern recognition
- Alignment of motion, audio, and breathing signals
- Temporal consistency evaluation
The project includes several experimental pipelines:
- Motion tracking from video sequences
- Audio feature extraction and rhythm modeling
- Breathing signal detection
- Multimodal synchronization analysis
Experiments were conducted to evaluate:
- temporal alignment between modalities
- consistency of motion patterns
- interaction between physiological and external signals
- Python
- OpenCV
- NumPy
- Signal processing libraries
- Audio processing tools
This project explores key directions in modern AI systems:
- Multimodal data fusion
- Temporal synchronization and sequence alignment
- Human motion analysis
- Breathing and physiological signal detection
- Interaction between visual, audio, and biometric data
- Human performance analysis
- Sports analytics
- Health monitoring and diagnostics
- Movement quality evaluation
- Multimodal AI research
- Improve breathing detection accuracy
- Apply deep learning models for sequence modeling
- Extend to real-time multimodal systems
- Integrate with wearable and health monitoring devices
Svetlana Rumyantseva
AI Systems Engineer