Gait Analysis

This advanced pose detection system leverages TensorFlow.js to perform real-time human pose estimation and gait analysis directly in the browser. It tracks 17 key body points including joints, estimates 3D positioning, and analyzes movement patterns to provide insights into walking patterns, posture, and biomechanics—all without requiring specialized hardware. Running entirely client-side, the system democratizes motion analysis technology that previously required expensive lab equipment and specialized expertise.

Real-Time Pose Tracking

The system detects and tracks multiple people simultaneously in video streams, identifying 17 anatomical keypoints per person: ankles, knees, hips, shoulders, elbows, wrists, eyes, ears, and nose. Each keypoint comes with a confidence score indicating detection reliability, allowing the system to filter unreliable data and maintain accuracy even with partial occlusions or challenging camera angles. The tracking algorithm maintains identity across frames, preventing confusion when multiple people cross paths or temporarily leave the frame.

Joint angle calculations provide precise measurements of flexion, extension, and rotation at major joints. The system computes knee angles during squats to ensure proper form, shoulder angles during overhead presses to prevent injury, and hip angles during deadlifts to maintain safe spinal positioning. These real-time measurements enable immediate feedback—critical for coaching applications where correcting form before it becomes habitual prevents injuries and accelerates skill development.

Movement velocity analysis tracks how quickly different body parts move through space, identifying compensation patterns where one side moves faster than the other, detecting explosive power output during jumps or sprints, and measuring deceleration control during landing phases. This data is invaluable for athletic training, where subtle asymmetries often predict injury risk or indicate areas needing strengthening.

Gait Analysis Capabilities

Gait analysis functionality measures stride length by tracking the distance between heel strikes, calculates cadence by counting steps per minute, identifies gait abnormalities like limping or uneven weight distribution, and detects deviations from normal walking patterns that may indicate injury or neurological issues. The system can differentiate between intentional movement variations and problematic compensations, using machine learning models trained on extensive biomechanics datasets.

For physical therapy applications, the system tracks patient recovery by comparing current gait patterns against baseline measurements taken before injury or at the start of treatment. Progress visualizations show improvements in symmetry, stride consistency, and range of motion over weeks or months. Therapists can set specific goals—like achieving 90% symmetry in step length—and the system automatically monitors progress toward those targets, providing objective data to complement clinical assessments.

The 3D pose estimation extends beyond 2D keypoint detection, inferring depth information to estimate true 3D positions of body parts. While running from a single camera (unlike expensive multi-camera motion capture systems), the system uses learned priors about human body proportions and movement constraints to reconstruct plausible 3D poses. This enables analysis of forward/backward lean, rotation around the vertical axis, and depth-based measurements that 2D systems cannot capture.

Performance Optimization

Optimized for both accuracy and performance, the system runs smoothly at 30+ FPS on modern devices while maintaining high-quality pose estimations. On high-end hardware, it can achieve 60+ FPS for ultra-smooth tracking, while on mobile devices it gracefully degrades to 15-20 FPS while maintaining accuracy. The adaptive performance scaling ensures the system remains usable across a wide range of devices, from flagship smartphones to budget laptops.

Multiple model variants offer different performance profiles: the MobileNet-based models prioritize speed for real-time applications, achieving 60 FPS on mid-range hardware with acceptable accuracy for most use cases. ResNet-based models deliver higher accuracy at the cost of performance, ideal for detailed analysis where precision matters more than frame rate. The system can dynamically switch models based on device capabilities or user preferences.

Visualization and Analysis Tools

The system includes comprehensive visualization tools that overlay skeletal structures on video feeds, showing detected keypoints as colored dots, connecting them with lines to form a stick figure representation, and highlighting joints with angle measurements when relevant. Color coding indicates confidence levels—green for high-confidence detections, yellow for moderate confidence, red for low confidence—allowing users to quickly assess tracking quality.

Historical tracking captures movement patterns over time, storing pose sequences for playback and analysis. Side-by-side comparison views let therapists show patients their current form versus target form, or athletes compare their technique against professional demonstrations. The timeline scrubber allows frame-by-frame analysis of critical movement phases, like the transition from eccentric to concentric muscle contraction during a squat.

Export capabilities generate detailed biomechanical reports in CSV, JSON, or PDF formats, including joint angle time series, velocity profiles, and statistical summaries. These exports integrate with research workflows, enabling scientists to process the data in tools like MATLAB or Python for advanced statistical analysis. For clinical applications, the reports provide documentation for insurance claims or medical records.

Real-World Applications

Fitness trainers use the system to provide real-time form feedback during remote coaching sessions, ensuring clients perform exercises correctly even without in-person supervision. The system can trigger audio cues when form degrades—'knees tracking over toes' during squats or 'keep your back straight' during deadlifts—providing coaching at scale that would be impossible to deliver manually.

Physical therapists track patient recovery with objective metrics, moving beyond subjective assessments to quantifiable improvements. Post-surgery knee rehabilitation can be monitored by tracking range of motion increases week over week. Balance training can be quantified by measuring center of mass stability during single-leg stands. This objective data helps justify continued treatment to insurance providers and gives patients concrete evidence of their progress.

Sports performance analysts use the system to optimize running form, reducing injury risk and improving efficiency. Analyzing sprinters' acceleration phases reveals power output asymmetries or suboptimal joint angles. Distance runners can be coached toward more economical movement patterns that reduce energy expenditure. The system has been used by Olympic training programs to fine-tune technique in everything from swimming to gymnastics, providing insights that previously required expensive lab visits.

Ergonomic assessments in workplace settings identify risky movement patterns before they cause repetitive strain injuries. The system can monitor warehouse workers performing lifting tasks, office workers at their desks, or assembly line workers doing repetitive motions. By detecting awkward postures, excessive force application, or high-frequency repetitive movements, the system helps companies proactively address ergonomic risks, reducing workers' compensation claims and improving employee wellbeing.