Understanding the Science of Movement
Gait analysis sits at the intersection of biomechanics, physics, and computer science. To understand what gait analysis reveals and why it matters, we need to explore how human movement works at a fundamental level.
This article provides a scientific foundation for understanding gait analysis—the mechanics of walking and running, the technology used to measure it, and how data is interpreted.
The Gait Cycle
Walking Gait Cycle
One complete gait cycle spans from initial contact of one foot to the next initial contact of the same foot. In walking, this cycle divides into:
Stance Phase (~60% of cycle):
- Initial contact: Heel strikes the ground
- Loading response: Weight transfers onto the limb
- Midstance: Body passes over the supporting leg
- Terminal stance: Heel rises, weight moves forward
- Pre-swing: Toe-off preparation
Swing Phase (~40% of cycle):
- Initial swing: Foot leaves ground, knee flexes
- Mid-swing: Limb passes under body
- Terminal swing: Leg extends for next contact
Walking always has at least one foot on the ground, with periods of double support when both feet contact simultaneously.
Running Gait Cycle
Running fundamentally differs from walking with the addition of a flight phase where neither foot contacts the ground. The cycle becomes:
- Stance phase: 30-40% of cycle (shorter than walking)
- Swing phase: 30-35% of cycle
- Flight phase: 25-35% of cycle
At faster running speeds, stance time decreases and flight time increases. Ground contact patterns also shift—elite sprinters may contact with forefoot only, while distance runners often show heel or midfoot patterns.
Forces in Running
Ground Reaction Forces
Newton's third law dictates that when your foot pushes against the ground, the ground pushes back with equal and opposite force. These ground reaction forces (GRF) are measured in three directions:
Vertical GRF:
- Peak forces: 2.5-3x body weight in running
- Impact peak at initial contact
- Active peak during midstance push-off
Anterior-Posterior GRF:
- Braking force at initial contact (slows forward motion)
- Propulsive force during push-off (accelerates forward motion)
- Net force should be zero for steady-state running
Medial-Lateral GRF:
- Smaller than other components
- Related to balance and stability
- Asymmetry may indicate injury or compensation
Loading Rate
Loading rate measures how quickly force is applied—not just peak force magnitude. High loading rates are associated with increased injury risk, particularly for:
- Tibial stress fractures
- Plantar fasciitis
- Joint stress
Interventions like increasing cadence or changing foot strike can reduce loading rate even if peak force remains similar.
Joint Mechanics
The Kinetic Chain
The body functions as a linked chain—forces and movements at one joint affect all connected joints. In running:
- Foot pronation couples with tibial internal rotation
- Hip adduction affects knee alignment (valgus)
- Trunk position influences lower limb mechanics
This is why gait analysis examines the entire body, not just the foot or a single joint.
Key Joint Measurements
Ankle:
- Dorsiflexion/plantarflexion range
- Pronation/supination (rearfoot motion)
- Timing of maximum pronation
Knee:
- Flexion angle at contact and peak
- Valgus/varus alignment
- Extension velocity during push-off
Hip:
- Flexion/extension range
- Adduction (hip drop) magnitude
- Internal/external rotation
Pelvis and Trunk:
- Anterior/posterior tilt
- Lateral tilt (drop to swing side)
- Rotation
- Forward lean
Motion Capture Technology
Marker-Based Systems
The gold standard for research uses reflective markers tracked by multiple infrared cameras:
- Accuracy: Sub-millimeter precision possible
- Markers: Placed on bony landmarks to define joint centers
- Processing: 3D coordinates calculated via triangulation
- Limitations: Time-intensive setup, skin movement artifact, unnatural with markers attached
Markerless Motion Capture
Modern AI-powered systems use computer vision to track body segments without physical markers:
- Pose estimation: Deep learning algorithms identify skeletal points from video
- Multi-view: Multiple cameras improve accuracy and reduce occlusion
- Real-time: Processing speed allows immediate feedback
- Natural movement: No attachments affecting gait pattern
The Visbody Creator600 uses markerless tracking to analyze 17+ skeletal points at 30fps, enabling continuous gait assessment during treadmill running.
Pressure and Force Measurement
Force Plates:
- Embedded in floor or treadmill
- Measure GRF in three dimensions
- Capture center of pressure trajectory
Pressure Mats/Insoles:
- Array of pressure sensors
- Map pressure distribution across foot
- Track foot function during stance
AI and Machine Learning in Gait Analysis
Pattern Recognition
Machine learning algorithms excel at identifying subtle patterns across complex, multi-dimensional data:
- Classify gait patterns into injury-risk categories
- Detect early deviations from baseline
- Identify compensatory patterns
- Predict future injury risk
Normative Comparisons
AI systems compare individual data against large databases of healthy runners, accounting for:
- Age and sex
- Running speed
- Experience level
- Anthropometric factors
Real-Time Feedback
Advanced systems provide immediate feedback during running:
- Visual cues on displays
- Audio feedback for specific metrics
- Automatic alerts for concerning patterns
- Progress tracking against goals
Clinical Implications
From Data to Decisions
Gait analysis data must be interpreted in clinical context:
- Not all deviations from "normal" require intervention
- Individual variation is significant
- Changes from personal baseline may matter more than absolute values
- Symptoms and function guide decision-making
Evidence-Based Interventions
Research supports specific interventions based on gait findings:
- Increased cadence: Reduces peak forces and loading rate
- Hip strengthening: Reduces hip drop and knee valgus
- Footwear changes: Match stability/cushioning to individual patterns
- Running retraining: Modify specific aspects of technique
Conclusion
The science of gait analysis combines biomechanical understanding with sophisticated technology to reveal how individuals move. Modern AI-powered systems make this science accessible and practical, enabling real-time feedback and continuous monitoring that was previously impossible outside research laboratories.
Understanding the science behind gait analysis helps practitioners and athletes appreciate what the technology measures, why it matters, and how to apply insights to improve movement quality, prevent injuries, and optimize performance.