Obeservational handicapping Posted on April 23, 2025 at 11:22:29 AM by Craig G
In theory a great observer would have a solid edge over people who rely solely on stats.
To that end, we ask DeepSeek, Do you think that AI will ever be able to detect changes in daily athletic form of humans through frame by frame video analysis? Factors might include respiration, posture, and walking speed.
the main point of this question is that I already know what I believe, and what I look for (when attending live) but I was curious to what extent, if any, an AI would independently ID and/or validate them.
The full response:
Yes, AI will likely be able to detect subtle changes in daily athletic form through frame-by-frame video analysis in the near future—if it isn't already doing so in some applications. Advances in computer vision, deep learning, and biomechanical modeling are making it increasingly feasible to track and analyze human movement with high precision. Here’s how AI could assess key factors like respiration, posture, and walking speed:
1. Respiration Rate Detection
Visual Cues: AI can estimate breathing patterns by analyzing subtle chest and abdominal movements, rib cage expansion, or even nostril flaring in high-resolution video.
Infrared & Depth Sensors: If additional sensors (like RGB-D cameras or thermal imaging) are available, respiration can be tracked more accurately by temperature changes or torso movement.
Neural Networks: Models like CNN-LSTM hybrids can learn temporal patterns in breathing from video data.
2. Posture & Biomechanics Analysis
Pose Estimation (2D/3D): AI tools like OpenPose, MediaPipe, or AlphaPose can track joint angles, spinal alignment, and weight distribution in real time.
Gait Analysis: By examining stride length, foot strike patterns, and hip movement, AI can detect fatigue, asymmetry, or injury risk.
Fatigue & Compensation Detection: Changes in posture (e.g., slumped shoulders, forward head tilt) could indicate fatigue or muscle imbalances.
3. Walking Speed & Movement Efficiency
Optical Flow & Object Tracking: AI can compute velocity by tracking body position changes over frames.
Stride Dynamics: Variations in cadence, step symmetry, and ground contact time can reveal performance degradation or recovery status.
Context-Aware Models: AI could correlate movement data with environmental factors (e.g., terrain, fatigue levels) for better insights.
Challenges & Considerations
Data Quality: Low-light conditions, occlusions, or low-resolution video may reduce accuracy.
Real-Time Processing: Edge AI (on-device processing) may be needed for immediate feedback in sports settings.
Privacy & Ethics: Continuous monitoring raises concerns about surveillance and data misuse.
Individual Variability: Models must adapt to personal biomechanics to avoid false positives.
Current & Emerging Applications
Sports Science: Systems like Dartfish, Kinovea, or Sparta Science already use AI-assisted motion tracking.
Healthcare: AI gait analysis helps in Parkinson’s and stroke rehabilitation.
Wearable Integration: Combining video with IMU (inertial sensor) data improves reliability.
Future Outlook
Multimodal AI: Fusion of video, audio (breathing sounds), and wearable data will enhance detection.
Predictive Analytics: AI could forecast injuries by detecting micro-changes before they become obvious.
Consumer-Level Tools: Smartphone apps might soon offer real-time form analysis for athletes.
Conclusion
Yes, AI will almost certainly reach (and may already be approaching) the capability to detect daily changes in athletic form through video analysis. The key hurdles are accuracy in real-world conditions and personalized calibration, but rapid advancements in neural networks, sensor fusion, and edge computing are closing the gap quickly. Within 5–10 years, this could become a standard tool for athletes, coaches, and physiotherapists.
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