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PhysioRehab

ICF-native, single-case clinical-reasoning tools for rehabilitation.

Rehabilitation reasons differently from disease medicine: it targets disability and life reconstruction, evaluates the individual rather than a randomised group, combines qualitative judgement with quantitative signals, and is delivered by allied-health professionals (PT / OT / ST) with varied statistical backgrounds. PhysioRehab re-centres physiological measurement around that way of reasoning: it anchors measures and goals to the International Classification of Functioning (ICF), detects change with single-case methods, keeps the clinician in the loop, and drafts an ICF-structured progress note.

Installation 

install.packages("PhysioRehab", repos = "https://x-biosignal.r-universe.dev")

Quick start

The core is a single guided workflow:

measure the individual over time against personal goals
  -> detect change defensibly with single-case methods (NAP + MCID + 2-SD band)
  -> visualise against ICF and the trajectory
  -> auto-draft an ICF-structured progress note
library(PhysioRehab)

ep  <- simulate_stroke_gait_case()          # a synthetic single case
hyp <- rehab_hypothesis("task-oriented training improves gait",
                        expect_speed_mcid = TRUE, expect_icf = c(d450 = 1))
res <- rehab_workflow(ep, hyp)              # analyse -> reason -> plot -> draft

cat(res$report)                             # the ICF-structured progress note
res$plot_file                               # the single-case trajectory plot

A runnable end-to-end example is included:

Rscript inst/examples/run_demo.R

It simulates a chronic-stroke, left-hemiplegia single case (AB design, 12 sessions), runs the workflow, and writes two clinician-facing artefacts — an auto-drafted, ICF-structured progress note and a single-case trajectory plot.

Design

Component Description Code
ICF-native semantics Measures, goals and observations are anchored to WHO ICF codes with 0–4 qualifiers — the domain data model of rehabilitation. R/icf.R, R/goals.R
Quantitative–qualitative fusion with an honest reasoning scaffold Single-case change detection fuses signals with ICF qualifiers; a hypothesis-to-evidence check keeps the clinician in the loop and explicitly reports what the evidence does not support. R/sced.R, R/reasoning.R
Guided workflow and clinician GUI One call, rehab_workflow(), runs the whole path; a Shiny interface, rehab_app(), exposes it to non-programmers, while experts call the components directly. R/workflow.R, R/app.R

The clinician-facing output (R/report.R) drafts the progress note the therapist already has to write, so measurement and analysis accompany routine documentation.

Ecosystem integration

PhysioRehab builds on the x-biosignal ecosystem rather than reimplementing it:

  • Single-case statistics — PhysioAppKit (NAP effect size, threshold-and-band analysis; reconciled with PhysioClinStats::scedNAP).
  • ICF ontology — PhysioAnnotationHub (icf_for_measure() resolves a measure to ICF codes from the maintained ontology).
  • Real signals — PhysioMoCap (markerless gait via add_session_from_mocap()) and PhysioEMG (muscle fatigue via add_session_from_emg()).
  • Ordinal-to-interval measurement — Rasch calibration via PhysioAppKit.
  • Reporting — PhysioReport.

Scope and limitations

  • Early-stage (0.1.0). The bundled examples use synthetic data; the worked instance is stroke hemiplegic gait, and the framework is condition-agnostic.
  • PhysioRehab is a research tool for measurement, documentation, and clinician-in-the-loop reasoning support. It is not a medical device and is not intended for diagnosis; final clinical judgement rests with the clinician.

Citation

See CITATION.cff. Licensed under the MIT License.