Computer Vision & Mobile Health AI
YogAsana AI
YogAsana AI is an intelligent mobile application that provides real-time yoga posture correction, biomechanical alignment tracking, and joint-strain detection. Using edge computer vision, it guides safer independent practice without specialized hardware, external visual sensors, or cloud video streaming.
Real-Time Pose EstimationEdge Computer VisionMobile Fitness SaaS
Value Story
Real-time alignment assistance built for personal practice.
Incorrect form risks long-term musculoskeletal strain
Yoga practitioners often practice independently without expert supervision. Poor form during complex postures such as Trikonasana or Chaturanga can lead to joint misalignment, spinal stress, and acute ligament injuries.
On-device biomechanical pose estimation
The solution runs real-time skeleton tracking directly on mobile hardware, calculating joint-angle accuracy against anatomical baselines to provide immediate visual and audio alignment cues.
Accessible, private, hardware-free wellness
Edge inference on the smartphone camera feed removes the need for expensive motion-capture wearables while keeping visual data on the user's device.
My Contribution
From skeletal keypoint inference to smooth mobile UI feedback.
Engineered hybrid pose-estimation pipelines balancing MediaPipe and OpenPose topologies for accurate skeletal keypoint inference.
Built real-time angular analysis algorithms to evaluate joint alignment and movement tolerances during live poses.
Implemented local mobile edge-inference routines in Flutter to eliminate server-side video-streaming latency.
Designed low-latency visual overlays and contextual audio alerts for mid-session posture correction.
Product Capabilities
What the user actually gets
Real-time skeleton tracking
Detects 33+ anatomical keypoints with continuous tracking designed for partial occlusion and complex body positions.
Angle precision and form scoring
Compares shoulder, hip, knee, and ankle vectors with target geometric templates to calculate a live posture-alignment score.
Active injury prevention
Highlights vulnerable joints when knee deflection, lumbar extension, or another tracked angle moves outside configured safety thresholds.
Voice-guided form feedback
Context-aware audio cues explain which movement to adjust while the user remains focused on the pose.
System Architecture
Camera capture to instant pose feedback flow
Camera Stream Layer captures high-frame-rate video through Flutter camera-controller bindings.
Frame Preprocessing and ROI Layer crops, normalizes, and isolates regional bounding boxes for efficient inference.
Edge Keypoint Engine executes lightweight MediaPipe and OpenPose models on local mobile acceleration hardware.
Biomechanical Logic Layer calculates Euclidean vectors, joint-angle offsets, and posture-hold stability scores.
Visual Overlay and Feedback Layer renders skeletal guidance and delivers contextual audio and haptic cues.
Outcomes
What this proves
- Built an on-device computer vision workflow capable of real-time pose estimation at 30+ FPS on mobile hardware.
- Mitigated visual privacy risks by running inference locally without cloud video transfer.
- Delivered an end-to-end mobile architecture combining biomechanical pose calculations with smooth UI rendering.
- Created a scalable foundation for fitness, rehabilitation, and athletic movement-tracking applications.
Technology
Core stack
FlutterMediaPipeOpenPoseDartTensorFlow LiteComputer VisionMobile Edge AI
Work together
Need an AI vision workflow that turns camera feeds into real-time operational insights?
I can help define the edge computing architecture, optimize neural network inference on mobile devices, and build production-ready applications for real-world interactions.
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