Changes in version 0.5.2 Documentation - ?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. - A vignette carries one task end to end on synthetic or bundled data, offline, and is built and run by R CMD check. - Runnable @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. - The README's quick start runs as written: it attaches the package, builds its own inputs, and uses only hard dependencies. Changes in version 0.5.1 - Wired 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. Changes in version 0.5.0 - 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. Changes in version 0.4.3 - Honest-scope correction: eating (d550) sensor_har performance is moved back from 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%. Changes in version 0.4.2 - Washing (d510) moved from 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. Changes in version 0.4.1 - Self-care recognition validated on real data: brushing/combing (d520), eating (d550) and drinking (d560) are moved from 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). Changes in version 0.4.0 - The ADL coverage registry gains a 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. Changes in version 0.3.1 - Register real-world upper-limb use (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%). Changes in version 0.3.0 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. Changes in version 0.2.0 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. Changes in version 0.1.0 First public release. - ICF-native semantic layer: icf_catalog(), icf_qualifier_label(), icf_change(), and icf_for_measure() resolving measures to ICF codes via the PhysioAnnotationHub ontology (with a local fallback). - Individualised single-case container: new_rehab_episode(), add_session(), with Goal Attainment Scaling goals (rehab_goal()). - Single-case change detection over PhysioAppKit: sced_analyze() (NAP + MCID - 2-SD band) and a clinician-in-the-loop reasoning scaffold (rehab_hypothesis(), evaluate_hypothesis()) that honestly flags unsupported claims. - Auto-drafted, ICF-structured progress note: draft_progress_note(); a guided one-call rehab_workflow(); a trajectory plot (plot_trajectory()). - Real-signal adapters: mocap_gait_measures() / add_session_from_mocap() (markerless gait via PhysioMoCap) and emg_fatigue_measures() / add_session_from_emg() (muscle fatigue via PhysioEMG). - A clinician GUI (rehab_app() / launch_rehab_app(), Shiny) whose actions mirror the equivalent R code. - Worked instance: stroke hemiplegic gait (simulate_stroke_gait_case()).