Why Running Form Matters More Than Pace

StrideCoach is built on BiomechEngine™, a biomechanics analysis engine developed by Beflex’s research team specializing in human movement science.

Unlike generic fitness apps, BiomechEngine is grounded in:

• Biomechanics research

• Signal processing algorithms

• Motion symmetry analysis

• Frequency-domain gait analysis

It transforms raw IMU sensor signals into clinically meaningful running metrics.

The 5 Core Metrics — With Research Foundations

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1. Impact (Landing Shock)

What it measures:
Acceleration peak at foot strike, reflecting impact loading.

Why it matters:
Higher impact loading is associated with increased injury risk, particularly tibial stress fractures and knee pain.

Research Evidence:

• Milner et al., 2006 – Higher vertical loading rates observed in runners with tibial stress fractures.

• Davis et al., 2016 – Impact reduction linked to lower injury risk in gait retraining studies.

• Zadpoor & Nikooyan, 2011 – Systematic review linking impact loading to running injuries.

Impact monitoring enables runners to identify excessive mechanical loading before symptoms appear.

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2. Left–Right Symmetry

What it measures:
Stride-to-stride load distribution symmetry.

Why it matters:
Persistent asymmetry increases unilateral overuse injury risk.

Research Evidence:

• Zifchock et al., 2008 – Symmetry deviations associated with injury history.

• Queen et al., 2020 – Gait asymmetry linked to musculoskeletal stress imbalance.

BiomechEngine analyzes rhythmic symmetry patterns using cycle detection and comparative waveform modeling.

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3. Cadence

What it measures:
Steps per minute during running.

Why it matters:
Increasing cadence by 5–10% has been shown to reduce:

• Vertical loading rate

• Knee joint forces

• Overstriding

Research Evidence:

• Heiderscheit et al., 2011 – Increased cadence reduces hip and knee loading.

• Lenhart et al., 2014 – Cadence modification lowers patellofemoral stress.

BiomechEngine extracts cadence from frequency-domain gait signal analysis (FFT-based dominant frequency detection).

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4. Vertical Oscillation

What it measures:
Up-and-down displacement amplitude during running.

Why it matters:
Excessive vertical oscillation reduces forward efficiency and increases metabolic cost.

Research Evidence:

• Saunders et al., 2004 – Running economy linked to vertical displacement efficiency.

• Moore, 2016 – Lower vertical oscillation associated with improved performance economy.

BiomechEngine calculates oscillatory amplitude patterns from head-based motion signals.

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5. Ground Contact Time (GCT)

What it measures:
Duration of foot-ground interaction per stride.

Why it matters:
Shorter ground contact time is correlated with improved elastic energy utilization and performance.

Research Evidence:

• Weyand et al., 2000 – Faster runners apply greater force in shorter contact times.

• Barnes & Kilding, 2015 – Contact time related to running economy and speed.

BiomechEngine estimates contact timing through phase segmentation of acceleration waveforms.

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The Science Behind AirPods-Based Biomechanics

AirPods include:

• 3-axis accelerometer

• Gyroscope

• High-resolution motion sampling

BiomechEngine processes this sensor data through:

1. Stride segmentation algorithms

2. Peak acceleration detection

3. Waveform symmetry comparison

4. Frequency-domain transformation (FFT)

5. Pattern stability scoring

Unlike wrist-based devices, head-centered motion tracking provides:

• Reduced arm-swing noise

• Stable center-of-mass proxy

• Reliable rhythm detection

Head motion reflects whole-body coordination patterns, making it suitable for biomechanical inference.

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Why This Approach Is Different

Most apps track output.
BiomechEngine tracks movement quality.

Most runners respond to pain.
BiomechEngine identifies mechanical trends before pain develops.

StrideCoach is powered by a research-based motion analysis engine — not generic step counting.

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References

• Milner CE et al. (2006). Biomechanical factors associated with tibial stress fracture in female runners.

• Davis IS et al. (2016). Gait retraining to reduce impact loading in runners.

• Zadpoor AA & Nikooyan AA. (2011). The relationship between lower-extremity stress fractures and impact loading.

• Heiderscheit BC et al. (2011). Effects of step rate manipulation on joint mechanics.

• Lenhart RL et al. (2014). Increasing step rate reduces patellofemoral joint forces.

• Saunders PU et al. (2004). Factors affecting running economy.

• Moore IS. (2016). Is there an economical running technique?

• Weyand PG et al. (2000). Faster top running speeds are achieved with greater ground forces.

• Barnes KR & Kilding AE. (2015). Running economy: measurement, norms, and determinants.