Package: PhysioTwin 0.1.2

PhysioTwin: Digital-Twin Simulation and Data Assimilation for Physiological and Movement Systems

A digital-twin engine that couples mechanistic simulation of movement and physiology with realistic sensor emulation and data assimilation, so a subject-specific model can be personalised from real measurements and then used for in-silico experiments. It ships a parameterised limb model (forward dynamics) and mechanistic physiological and network generators -- a Jansen-Rit neural-mass EEG column with an optional 1/f aperiodic background, a McSharry limit-cycle electrocardiogram, a Windkessel arterial blood-pressure model with a closed-loop baroreflex, a respiration waveform and respiratory-sinus-arrhythmia heart-rate-variability generator, and Kuramoto coupled-oscillator and Matsuoka central-pattern-generator rhythms. A forward sensor-emulation layer turns clean signals into realistic recordings (inertial-measurement-unit, optical-marker occlusion, surface-electromyography electrode and electrocardiogram lead), with richer chains for multi-channel electromyography crosstalk and amplifier characteristics, multi-lead electrocardiogram and analogue-to-digital conversion. A data-assimilation layer personalises the twin to recorded data with a family of state-space estimators (unscented, extended, particle and ensemble Kalman filters), likelihood-free Bayesian calibration (rejection and sequential Monte-Carlo approximate Bayesian computation), Markov-chain Monte- Carlo, a Gaussian-process surrogate and GP-accelerated (Bayesian-optimisation) calibration -- including personalising the closed-loop cardiovascular- respiratory twin to measured heart-rate and blood-pressure variability -- as well as particle Markov-chain Monte-Carlo and particle Gibbs (with ancestor sampling) for whole-waveform Bayesian fitting -- including a whole-record fit of the closed-loop cardiovascular model that identifies the baroreflex and respiratory gains a summary fit cannot, with either a resonant Mayer-oscillator or a broadband first-order-autoregressive low-frequency component for records without a distinct Mayer peak -- and Fisher-information optimal experimental design for choosing the most informative experimental condition. Applications include single- and population-level in-silico intervention (perturb parameters and predict the change, with effect size, responder rate and statistical power), multi-modal coupling, and labelled synthetic training-data generation with sensor domain randomisation, all backed by a simulation validity-and-verification harness (identifiability, global sensitivity and validation against real recordings). A clinical decision loop ties these together as research tooling: a personalised twin is certified by a validation gate (goodness of fit and parameter identifiability) before it may advise, candidate interventions are scored with their predictive uncertainty and ranked on multiple criteria, mechanistic predictions are reconciled with external evidence rather than overriding it, and a follow-up measurement is checked against the predicted interval. A closed-loop cardiovascular-respiratory model couples a delayed baroreflex with respiration to produce emergent Mayer waves and respiratory sinus arrhythmia, and an optional bridge holds the twin's predictions to account against a real single-case (SCED/MCID/ICF) clinical evaluation.

Authors:Yusuke Matsui [aut, cre]