Running biomechanics / Gait analysis

Gait analysis should follow the runner—not stay in the lab

A treadmill test can capture a moment. A camera can capture an angle. STRIV captures the run: both feet, every phase of the step, and the way mechanics change across real miles.

04

The short answer

Running gait analysis connects four stories: timing, movement, pressure, and force. STRIV combines them in one wearable view, then adds what a short test usually misses—left–right behavior, step-to-step consistency, and change across the whole session.

01

Every step

Cadence, contact time, loading, midstance, push-off, and off-ground timing.

02

Both feet

Separate left–right pressure, force, pronation-related motion, and load paths.

03

Across the run

See when a stable personal pattern holds—and when it begins to drift.

01

Pace is the result. Gait is how you produced it.

Gait analysis connects timing, movement, and ground interaction. Cadence and contact time describe when a step happens. Motion describes how the foot moves. Pressure and force describe how the runner meets the ground. Variability and left–right comparisons reveal whether that strategy repeats.1,2

Put together, those measurements describe the mechanics alongside the pace: not only that the runner sped up or slowed down, but what changed under each foot as it happened.

02

Every step is a connected sequence

Landing shows where and how load arrives. Midstance shows how the foot adapts and carries the body forward. Push-off shows where pressure finishes and how quickly the foot leaves. The next contact reveals whether the opposite side repeats the same strategy.

A single average erases that sequence. STRIV keeps the phases connected so cadence, contact time, pressure distribution, CoP, pronation-related motion, and force can be read as one step—not six unrelated charts.

03

The signal at the foot opens a window on the whole chain

Researchers have used plantar-pressure signals to reconstruct continuous running ground-reaction forces, and instrumented-insoles to estimate knee contact force and knee moments across walking, running, and daily movements.5,6

Wearable motion models have also estimated structure-specific peaks, impulses, and loading rates around the Achilles tendon, patellar tendon, ankle, and knee during running. These advances are why the foot–ground signal matters: it is the mechanical starting point for understanding how load travels through the runner.7

STRIV turns that starting point into something runners can collect every session—not only during an occasional lab visit.

04

26 measurements. Two feet. One running story.

With 128 pressure sensors beneath each foot plus motion sensing, STRIV captures 26 measurements per step. That includes pressure distribution, CoP progression, timing, pronation-related roll, peak vertical GRF, loading rate, cadence, and left–right differences.

The value is not the metric count. It is the connection between them: whether one side lands harder, stays longer, rolls differently, moves pressure through another path, or changes first as the session progresses.

05

Real runs reveal what a short test can miss

Running mechanics change with speed, step rate, terrain, incline, footwear, and the demands of the session. Repeated wearable measurements make those changes visible in the environment where they actually happen.1,3

That creates a personal feedback loop: see the pattern, test a cue or shoe, run again, and learn whether the change held across real steps. Gait analysis stops being a one-time verdict and becomes part of training.

  • Compare the same runner before comparing different runners.
  • Separate left and right instead of hiding them in a whole-body average.
  • Follow change across the run, not only the session summary.
Evidence base

References

  1. Mason R et al.Wearables for Running Gait Analysis: A Systematic ReviewSports Medicine, 2023
  2. Horsley BJ et al.Does Site Matter? Impact of Inertial Measurement Unit Placement on the Validity and Reliability of Stride Variables During RunningSports Medicine, 2021
  3. Heiderscheit BC et al.Effects of step rate manipulation on joint mechanics during runningMedicine & Science in Sports & Exercise, 2011
  4. Peterson B et al.Biomechanical and Musculoskeletal Measurements as Risk Factors for Running-Related Injury in Non-elite RunnersSports Medicine - Open, 2022
  5. Honert EC et al.Estimating Running Ground Reaction Forces from Plantar Pressure during Graded RunningSensors, 2022
  6. Snyder SJ et al.Prediction of knee loads during activities of daily living using custom instrumented insoles and machine learningJournal of Biomechanics, 2025
  7. Bogaert S et al.Machine learning-based estimation of structure-specific load around the ankle and knee joint during running using IMU dataFrontiers in Bioengineering and Biotechnology, 2026