A computer vision system that detects yoga poses and evaluates posture correctness in real time to support safe, effective practice.
Incorrect yoga postures reduce exercise effectiveness and can lead to muscle strain or injury, and beginners and remote learners often practice without expert supervision or real-time corrective feedback.
MoreYeahs built an AI-powered yoga pose detection and posture evaluation system for fitness and wellness organizations, giving learners automated, camera-based feedback that scales beyond what manual coaching can reach.
Without a scalable feedback mechanism, practitioners were left to self-correct with limited guidance:
No Real-Time Correction: remote and beginner learners practiced without feedback on alignment errors as they happened.
Injury Risk: incorrect postures increased the risk of muscle strain and reduced the effectiveness of practice.
Manual Coaching Doesn't Scale: expert supervision is not always available or affordable for every learner.
Inconsistent Self-Assessment: without objective measurement, learners had no reliable way to judge their own form.
MoreYeahs built a computer vision–based yoga analysis system that captures live video of a practitioner and evaluates pose correctness in real time.
Pose Estimation: models extract body keypoints and compute joint angles, limb alignment, and body symmetry metrics.
Pose Classification: extracted features are compared against pose templates or a trained classifier to identify the performed yoga pose.
Correctness Scoring: the system evaluates posture correctness and highlights misaligned joints and limbs.
Real-Time Feedback: visual and textual feedback is displayed live, suggesting specific corrections.
The system runs continuously through a practice session, from keypoint extraction to logged pose summaries.
Continuous Keypoint Extraction: video frames are captured continuously, with pose estimation extracting body keypoints each frame.
Joint Angle Computation: alignment metrics are computed and compared against pose templates or the trained model.
Session Logging: pose results and correctness scores are logged for a session summary.
Graceful Handling: partial visibility prompts the user to adjust position, and transition states between poses temporarily pause feedback.
The solution runs on a standard RGB camera paired with pose estimation and a joint-angle computation module.
In validation testing, the system met its target benchmarks for classification accuracy and feedback speed.
Automated posture feedback extends expert-level correction to learners practicing on their own.
Safer Practice: real-time correction reduces the risk of strain from sustained incorrect postures.
Scalable Coaching: feedback quality no longer depends on live instructor availability.
Better Learning Outcomes: objective scoring helps learners track and improve their form over time.
Foundation for Digital Fitness Products: the pipeline can extend into broader fitness and wellness applications beyond yoga.
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