?PhysioRehab now answers: a package help page gives one paragraph on what the
package is for, the main entry points grouped by task, and where to go next.R CMD check.@examples added or corrected across 27 help pages. Each runs
offline in seconds, writes nothing outside tempdir(), and is executed by
R CMD check; anything needing a device, a download or an optional backend is
fenced with the reason stated.fall_risk_index() into the reporting pipeline: fall_risk_from_episode()
derives the fall-risk domains from a rehab_episode's latest session measures
columns; draft_progress_note() gains a fall_risk argument and renders a
"転倒リスク（統合指標）" section (domains, z, composite/percentile, stratum or the
uncalibrated caveat); rehab_workflow() computes it and passes it into the note.fall_risk_index(): an integrated multi-domain fall-risk index that combines
per-domain summaries (postural sway, gait variability, TUG, lower-limb
strength, and optionally reaction time / vision / proprioception) following the
Physiological Profile Assessment framework. Honest scope: the default
z-score-composite method standardises each domain against a user-supplied
healthy reference (sign-aligned, equal weights by default) and stays
uncalibrated (no risk stratum) unless validated cutpoints are supplied; the
PPA method requires the caller's published discriminant weights (ppa_coef) —
no weights or cut-offs are fabricated.validated to supported. A strict leave-one-subject-out re-analysis on
the HMP wrist dataset shows eating recall 0.00 -- eating collapses into the
other hand-to-face gestures and is NOT distinguished cross-subject; the earlier
every-other-subject split had no eating in its test set. Grooming (d520) and
drinking (d560) remain validated. Validated performance coverage 56% -> 50%.supported to validated after cross-subject
recognition on the HTAD wrist-accelerometer dataset (washing-hands recall 0.53,
~3.7x chance). The method note records the weak evidence (HTAD N=3).
Validated performance coverage rises 50% -> 56%. Dressing (d540) and toileting
(d530) stay supported -- still no public wrist-IMU labels.supported to validated in the
coverage registry after cross-subject validation on the UCI wrist-worn
accelerometer ADL dataset (self-care detected-as-self-care 1.00). Validated
performance coverage rises 33% -> 50%. Washing/dressing/toileting stay
supported (no public wrist-IMU labels for those tasks).status column distinguishing validated
(proven on real data or an established measure) from supported (pipeline +
ICF mapping ready, but not yet validated on a task-specific dataset).
adl_coverage_summary() reports validated_performance per ADL and
pct_validated_performance overall -- an honest split of real vs potential
coverage. Registers the remaining self-care activities (washing/toileting/
dressing) as supported performance via wrist-gesture recognizeADL.PhysioWearable::upperLimbUse -> d445) and
wrist-gesture eating/drinking recognition (recognizeADL -> d550/d560) as
performance modalities in the ADL coverage registry, filling the self-care
performance gap (ecosystem performance coverage 39% -> 56%, multi-modal
33% -> 50%).ADL measurement-coverage registry.
adl_coverage(): what the ecosystem can measure for each ADL -- the ICF code,
the modality (scale / performance_test / kinematics / emg / dexterity /
sensor_freeliving / sensor_har), the qualifier it informs (capacity vs
performance) and the function that provides it.adl_coverage_summary(): per-ADL completeness -- how many modalities, and
whether covered on both qualifiers (genuinely multi-modal) -- with
ecosystem-level percentages.construct_coverage(): for one patient's icf_construct, the modalities the
ecosystem offers versus the qualifiers actually populated, flagging the gaps.Cross-modal ICF construct: capacity vs performance.
new_icf_construct() / add_icf_measure(): bind heterogeneous measures of one
ICF Activity/Participation domain under its capacity (clinic scale / Rasch) and
performance (free-living sensor) qualifiers on the ICF 0-4 scale.capacity_performance_gap(): the performance-minus-capacity discrepancy -- what
a person does versus what they can do.icf_construct_trajectory(): a time-ordered qualifier stream for single-case
analysis.add_performance_from_freeliving(): ingest a PhysioWearable::summarizeFreeLiving()
metric as the performance qualifier.draft_progress_note() gains an optional cross-modal capacity-vs-performance
section.First public release.
icf_catalog(), icf_qualifier_label(),
icf_change(), and icf_for_measure() resolving measures to ICF codes via
the PhysioAnnotationHub ontology (with a local fallback).new_rehab_episode(), add_session(),
with Goal Attainment Scaling goals (rehab_goal()).PhysioAppKit: sced_analyze() (NAP + MCID
rehab_hypothesis(), evaluate_hypothesis()) that honestly flags
unsupported claims.draft_progress_note(); a guided
one-call rehab_workflow(); a trajectory plot (plot_trajectory()).mocap_gait_measures() / add_session_from_mocap()
(markerless gait via PhysioMoCap) and emg_fatigue_measures() /
add_session_from_emg() (muscle fatigue via PhysioEMG).rehab_app() / launch_rehab_app(), Shiny) whose actions
mirror the equivalent R code.simulate_stroke_gait_case()).