NEWS

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PhysioMSKNet 0.3.1

Documentation

• ?PhysioMSKNet 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 100 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.

PhysioMSKNet 0.3.0

• Documentation: added a package-level help page (?PhysioMSKNet) with a task-grouped index of entry points, a "Getting started" vignette, and runnable @examples across the core hypergraph, metric, community, simulation, null-model, Hill-model and visualization functions.
• Elastic-tendon extension to the Hill model (R/hill-elastic.R):
  • tendonForceLength() — tendon force-length curve.
  • equilibriumFiberLength() — solves the quasi-static muscle-tendon force equilibrium for the fiber length, accounting for tendon compliance.
  • emgDrivenForceElastic() — EMG-driven force with a compliant tendon.

PhysioMSKNet 0.2.0

• Forward EMG-driven Hill-type muscle model (R/hill-model.R): turns an EMG envelope into physical muscle force and joint torque (N.m), replacing the amplitude proxy.
  • excitationToActivation() — first-order activation dynamics (Thelen 2003).
  • forceLengthActive(), forceLengthPassive(), forceVelocity(), hillMuscleForce() — the Hill contractile + passive elements.
  • emgToExcitation(), emgDrivenForce(), emgDrivenJointMoment() — the forward EMG-to-moment pipeline over normalised fiber kinematics from muscleTendonKinematics().
  • calibrateEMGDrivenModel() — CEINMS-style calibration to measured joint moments.

PhysioMSKNet 0.1.0

Initial release as a standalone package in the x-biosignal ecosystem. PhysioMSKNet brings network-science analysis of the human musculoskeletal system to R, implementing the hypergraph model of Murphy et al. (2018, PLOS Biology) and bridging it to multi-modal physiological signals (EMG, MoCap, IMU, EEG, OpenSim) for rehabilitation-oriented analysis.

New Features

• Hypergraph data model in which bones are vertices and muscles are hyperedges:
  • MSKHypergraph() constructs the model from a sparse incidence matrix, with a bundled 173-bone x 270-muscle anatomical dataset loadable via loadMSKData(), loadIncidenceMatrix(), loadMuscleMetadata(), and loadValidationData().
  • One-mode projections projectBoneGraph() and projectMuscleGraph(), plus hyperedgeDegree(), vertexDegree(), and degreeDistribution().
• Damped harmonic-oscillator physical simulation and perturbation-based impact scoring:
  • mskSimulate() sets up a spring-mass system (spring constant 1/(deg-1) per muscle); mskImpactScore() and mskImpactScoreAll() perturb muscles via velocity-Verlet integration in a 4th spatial dimension to quantify network-wide displacement.
  • mskNullHypergraph() / mskNullEnsemble() generate degree-preserving randomized null models, and mskImpactDeviation() scores each muscle against its degree-matched null distribution.
• Community detection and graph metrics:
  • mskCommunityDetect() and mskConsensusPartition() run Louvain community detection (with a pure-R fallback when igraph is absent), with mskModularity(), mskZRand(), and mskConsensusPartition() consensus over repeated runs.
  • Centrality and distance metrics via mskNetworkMetrics(), mskBetweenness() (Brandes), mskCloseness(), and mskShortestPaths().
• Statistical validation against the reference paper:
  • mskRobustRegression() (weighted / MASS::rlm robust fitting), mskImpactRecoveryModel(), and mskHomuncCorrelation() reproduce the impact-vs-recovery and community-vs-homunculus results, and mskReproducePaper() runs the full validation pipeline end to end.
• Base-graphics visualization: plotMSKNetwork(), plotDegreeDistribution(), plotCommunityStructure(), plotImpactVsDegree(), plotImpactVsRecovery(), and plotHomunculus().

Multi-Modal Bridges

• EMG bridge: emgToMSKMapping() matches EMG channels to muscles, and emgStructuralCoherence(), emgCommunityCompare(), and emgMSKEnrichment() compare functional coherence and activation against network structure (with a Mantel test and Welch-coherence fallback).
• MoCap and IMU bridges: mocapToMSKMapping() / imuToMSKMapping() resolve segment and sensor names to bones, and mocapNetworkKinematics(), mocapImpactPrediction(), mocapCommunityDynamics(), imuNetworkKinematics(), imuImpactPrediction(), and imuCommunityDynamics() propagate kinematic stress and movement synchrony through the network.
• OpenSim bridge: opensimToMSKHypergraph() builds a hypergraph directly from an .osim model (parsing PathPoint attachments), with opensimNetworkAnalysis(), opensimCompareModels(), and opensimForceToImpact() for force-weighted impact analysis.
• Neuromechanics bridge: corticomuscular and directional coupling (neuromechCorticomuscularCoupling(), neuromechDirectionalCoupling() via Granger causality / transfer entropy), electromechanical delay (neuromechElectromechanicalDelay()), joint-torque estimation from moment arms (neuromechJointTorque()), NMF/PCA muscle-synergy decomposition (neuromechMuscleSynergy()), motor-drive topography, integrated vulnerability, and a neuromechSummary() orchestrator.
• Knowledge-graph bridge (via PhysioAnnotationHub): mskAnnotate(), mskEnrichKG() (hypergeometric enrichment), mskPathwayQuery(), mskCommunityProfile(), mskClinicalEvidence(), and mskKGSummary().

Rehabilitation Analysis

• Clinical prediction from network topology: mskClinicalPredictor(), mskRecoveryTimeline(), mskInjuryRiskProfile(), mskRehabProtocol(), and the mskOutcomeSummary() orchestrator.
• Compensation detection: mskDetectCompensation(), mskCompensationRiskScore(), mskCompensationEvolution(), mskCompensationNetwork(), and mskCompensationSummary() identify and track compensatory activation in muscles neighboring an injury.
• Longitudinal tracking: mskLongitudinalTracker() with ICC-based mskMinimalDetectableChange(), parametric mskRecoveryTrajectoryFit() (exponential / sigmoid / linear), mskDetectResponderStatus(), mskDetectRecoveryPlateau(), mskSynergyChangeIndex(), mskNeuralAdaptationIndex(), and mskCoordinationQualityScore().
• Functional outcome prediction: mskPredictFunctionalOutcome() (ROM, strength, composite score), mskFunctionalMilestones(), mskOutcomeConfidenceInterval(), mskReassess(), mskAdaptProtocol(), and mskOutcomeReport().

Notes

• Clinical and outcome functions are research-exploration tools, not diagnostic devices; recovery models were validated on aggregate muscle groups and patient-factor adjustments are heuristic.
• igraph, MASS, readxl, and the sibling PhysioEMG / PhysioMoCap / PhysioOpenSim / PhysioCrossModal / PhysioAnnotationHub packages are optional (Suggests); pure-R and minimal fallbacks are used when absent.