Every step.
Every phase.
Every stride.
ConductVision applies a Hidden Markov Model over per-foot contact and joint kinematics to produce smooth, gap-free temporal segmentation — turning a single sagittal video into stride-locked gait data.
- Bilateral heel-strike / toe-off event detection
- Cycle-aware smoothing eliminates flicker frames
- Stride length, cadence, double-support, symmetry — one model
- Exports CSV, C3D and clinical-report-ready summaries
- frame
- 2,148
- time
- 00:01:11.6
- stride
- #42 of 87
- phase
- R-mid-stance
HMM · 4-state
Bilateral segmentation
30 FPS
Real-time decode, CPU-only
96.8%
Foot-phase F1 vs. force plate
Markerless
Single sagittal camera
From a single video to stride-locked gait data
Record standard video
Use a single sagittal camera in a hallway, lab walkway, rehabilitation setup, or controlled motion protocol — no force plate or wearable sensors.
Decode with the HMM
A 4-state Hidden Markov Model reads per-foot contact and joint kinematics frame by frame, then smooths across the temporal sequence for gap-free stride boundaries.
Export reproducible metrics
Stride length, cadence, double-support, and symmetry export to CSV, C3D, and clinical-report-ready summaries you can take straight into statistics.
Metrics built for clinical and translational gait
The same HMM decoder powers human mobility studies and sits beside rodent gait and animal-behavior analysis, keeping a common video and export workflow across translational programs.
Gait analysis — methods & FAQ
Bring HMM gait analysis into your ConductVision workflow
Send a sample video or schedule a consultation to map the stride metrics your protocol needs.
