Research

OpenStep in Research Contexts

OpenStep was built with clinical transparency as a core principle. That same foundation makes it suitable for research applications where clarity, reproducibility, and raw data access are essential.

OpenStep does not rely on proprietary “black-box” scoring systems. All derived metrics are computed using clearly defined signal-processing steps that are documented and available for review.

For research teams requiring insight into data provenance, transformation, and computation, OpenStep provides both transparency and control.

Raw Data Export

OpenStep supports export of raw sensor data.

Exported data includes:

  • Time-stamped inertial measurements

  • Original sampling resolution

  • Unfiltered acceleration data

  • Session-level metadata

Raw data export enables:

  • Independent verification of derived metrics

  • Reprocessing using custom pipelines

  • Validation against external systems

  • Secondary analysis beyond the OpenStep interface

OpenStep does not restrict researchers to precomputed outputs. All derived metrics can be recomputed externally from exported raw data.

This ensures data ownership remains with the clinician or research team.

Transparent Metric Methodology

Each metric within OpenStep is derived using documented computational steps.

For example:

  • Fixed-duration time windows

  • Explicit filtering approaches

  • Defined thresholding procedures

  • Robust central tendency (e.g., median-based interval estimation)

  • Clearly described confidence metrics

The intent is not to obscure interpretation but to make every step reproducible.

OpenStep is designed so that:

  • Raw data are preserved

  • Derived values are recomputable

  • Assumptions are explicit

  • No hidden normalization or performance scoring is applied

This transparency supports peer review, publication, and replication.

Within-Subject Emphasis

OpenStep emphasizes within-subject comparison rather than normative labeling.

This aligns with many research designs where:

  • Change over time is the primary outcome

  • Intervention effects are evaluated longitudinally

  • Individual response patterns are of interest

Researchers remain free to apply their own normative frameworks or statistical models to exported data.

Adaptable for Research Projects

OpenStep can be adapted for specific research purposes.

Potential adaptations include:

  • Custom metric definitions

  • Modified window durations

  • Experimental threshold parameters

  • Additional export variables

  • Project-specific data tagging

Because OpenStep preserves raw data and uses modular metric computation, modifications can be made without compromising the integrity of the underlying signal record.

Collaboration for research-specific adaptations is possible.

Data Integrity and Signal Context

Signal quality metrics are included alongside derived outputs to help researchers interpret:

  • Sampling continuity

  • Movement energy thresholds

  • Confidence of periodic detection

This ensures cadence and other temporal measures are not divorced from signal context.

Confidence metrics describe estimator behavior, not biomechanical correctness.

Intended Role in Research

OpenStep is not presented as a laboratory motion-capture replacement.

It is designed as:

  • A transparent inertial measurement platform

  • A reproducible temporal gait analysis tool

  • A clinician-informed data collection system

It may be particularly suitable for:

  • Field-based data collection

  • Intervention studies

  • Longitudinal tracking

  • Footwear comparisons

  • Cadence manipulation research

Collaboration

OpenStep welcomes collaboration with clinicians, universities, and research groups.

If you are exploring a project involving running mechanics, cadence manipulation, wearable sensors, or field-based gait monitoring, please reach out.

OpenStep was built by a clinician who understands both practical assessment and methodological clarity.

Research partnerships are encouraged.