MoonBit sports science and biomechanics force plate signal analysis library

force-plate

biomechanics

jump-analysis

dsp

cop-sway

moon add hnriiuu/moonbit_forceplate@0.1.0

Download zip

Author

hnriiuu

Version

0.1.0

License

Apache-2.0

Repository

github.com/hnriiuu/moonbit-forceplate

Last updated

5 hours ago

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README

#MoonBit Force Plate

MoonBit libraries and command-line tools for force-plate biomechanics. The project turns synchronized force, moment, timestamp, and center-of-pressure (COP) samples into reproducible jump, kinetics, posturography, bilateral-loading, quality, and export reports.

The implementation is designed for real acquisition data: malformed timestamps, non-finite channels, short recordings, duplicate samples, invalid filter parameters, zero denominators, and incomplete COP trajectories are handled explicitly instead of being silently converted into plausible-looking metrics.

#Capabilities

Force and COP primitives, calibration, robust baseline estimation, metadata validation, feature vectors, windowed statistics, and signal agreement metrics.
Butterworth low/high/band-pass, notch, moving-average, Savitzky–Golay, zero-phase, and validated filter chains.
Linear, cubic-spline, Akima/PCHIP resampling, time normalization, and complete multichannel trial resampling.
Net force, impulse, RFD, velocity, displacement, work, mechanical envelopes, and force-window statistics.
Movement onset, take-off/landing, persistent thresholds, hysteresis segments, contact intervals, and phase metrics.
CMJ, squat jump, drop jump, pogo, IMTP, landing impact, readiness, and repeated-jump summaries.
COP path, velocity, covariance, confidence ellipse, convex hull, spectral, entropy, crossing, and windowed stability metrics.
Bilateral load shares, symmetry/asymmetry indices, pointwise profiles, binned profiles, and integrated load balance.
Quality gates, robust MAD outlier detection, interpolation repair, physical-range clamping, and auditable repair reports.
Repeated-session assessment with robust trial statistics, baseline comparison, direction-aware change interpretation, readiness scoring, and repeatability/outlier reports.
CSV, JSON, Markdown, ASCII, batch, stage-trace, feature, and quality-report exports.
A composable pipeline package with quality gates, optional tare/filter stages, CMJ/sway/bilateral workflows, batch execution, diagnostics, and acceptance telemetry.

#Package layout

Package	Responsibility
core	Types, calibration, metadata, timebase, robust statistics, signal comparison, trial/window features
filter	Validated DSP filters and auditable filter chains
resample	Interpolation, splines, time normalization, and multichannel trial resampling
kinetic	Force-derived quantities, integration, impulse, RFD, work, and kinematic envelopes
event	Onset, contact, hysteresis, jump-event, phase, TKEO, and CUSUM detection
jump	CMJ, SJ, DJ, IMTP, pogo, landing, stiffness, readiness, and repeated-trial metrics
sway	COP metrics, ellipse/hull/spectral analysis, stability, entropy, and sliding windows
asymmetry	Bilateral metrics, trajectories, profiles, bins, and load integration
quality	Trial validation, quality scoring, quality gates, outliers, repair, and report export
assessment	Repeated-trial statistics, session summaries, baseline comparison, readiness, and report export
pipeline	Auditable single-trial and batch workflows plus CSV diagnostics
export	Machine-readable and human-readable report generation
cmd / cmd/main	Deterministic synthetic fixtures, benchmark API, and runnable CLI demonstration

#Quick start

Install the current stable MoonBit toolchain, then run the demo:

moon update
moon check --deny-warn
moon test --deny-warn
moon run cmd/main

The CLI generates deterministic synthetic CMJ, IMTP, and sway trials, applies a zero-phase filter, prints biomechanical reports, and runs the same CMJ pipeline used by the benchmark API.

Library consumers can compose the pipeline from a ForceTrial:

let quality = @quality.assess_trial(
  trial,
  @quality.QualityPolicy::default(),
)
let result = @pipeline.run_cmj(
  trial,
  body_mass_kg,
  @pipeline.PipelineOptions::default(),
)
match result.status {
  @pipeline.PipelineStatus::Completed => println("analysis complete")
  @pipeline.PipelineStatus::Rejected => println("quality gate rejected trial")
}

For a batch, use @pipeline.run_cmj_batch; for pre-analysis ingestion use @pipeline.run_quality_batch. The quality and pipeline reports retain issue categories, scores, stage status, changed-sample counts, and exportable traces.

For repeated testing, convert scalar results into an assessment session. The assessment layer ignores non-finite values, applies the configured quality floor, and keeps baseline changes direction-aware:

let trial = @assessment.AssessmentTrial::from_cmj(
  "cmj-01",
  1,
  98.0,
  true,
  jump_height_m=0.42,
  peak_force_n=2350.0,
  rsi_modified=0.44,
  peak_power_w=2176.0,
  takeoff_velocity_m_s=1.75,
)
let session = @assessment.AssessmentSession::new(
  "session-2026-08-20",
  "morning CMJ",
  [trial],
)
let summary = @assessment.summarize_session(
  session,
  @assessment.AssessmentPolicy::default(),
)
let csv = @assessment.session_summary_to_csv(summary)

#Benchmark

The benchmark performs real work: synthetic trial generation, quality assessment, the configured pipeline, event detection, kinematics, and CMJ metrics. Its checksum accumulates peak force so a benchmark cannot accidentally measure an unused result.

moon run cmd/main

The CLI benchmark configuration is 3 iterations, 1,000 Hz, 75 kg, and a requested maximum of 2,000 samples. Two consecutive local Windows runs on 2026-08-19 produced:

iterations=3 samples=1700 elapsed_ms=8 trials_per_second=375 mean_jump_height_m=0.1961330000000001 checksum_peak_force_n=7060.7880000000005
iterations=3 samples=1700 elapsed_ms=12 trials_per_second=250 mean_jump_height_m=0.1961330000000001 checksum_peak_force_n=7060.7880000000005

Elapsed time is machine- and toolchain-dependent; the sample count, configuration, and checksum are the reproducibility anchors. Use @cmd.run_cmj_benchmark when a program needs structured results rather than console text.

#Verification

The repository keeps tests next to their package and emphasizes boundary behavior as well as nominal biomechanical fixtures:

moon fmt --check
moon check --deny-warn
moon test --deny-warn
moon check --target all --deny-warn
moon build cmd/main --target wasm-gc --release

The GitHub Actions workflow also checks formatting and package interfaces, tests the supported target matrix, and records the toolchain version used by each job. Local native backends may require the platform C toolchain supplied by the runner.

The current handwritten MoonBit inventory contains 159 .mbt files. Using the repository's acceptance metric—non-empty, non-comment lines in tracked-source .mbt files, excluding generated build output—there are 9,929 production lines and 2,872 test lines (12,801 effective lines total). These figures are generated from the working tree rather than copied from an external claim.

#License

Licensed under the Apache License 2.0.

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