Heart rate variability (HRV) is governed by the autonomic nervous system (ANS) and is routinely used to estimate the state of body and mind. At the same time, recorded HRV features can vary substantially between people. A model for HRV that (1) correctly simulates observed HRV, (2) reliably functions for multiple scenarios, and (3) can be personalised using a manageable set of parameters, would be a significant step forward toward understanding individual responses to external influences, such as physical and physiological stress. Current HRV models attempt to reproduce HRV characteristics by mimicking the statistical properties of measured HRV signals. The model presented here for the simulation of HRV follows a radically different approach, as it is based on an approximation of the physiology behind the triggering of a heart beat and the biophysics mechanisms of how the triggering process-and thereby the HRV-is governed by the ANS. The model takes into account the metabolisation rates of neurotransmitters and the change in membrane potential depending on transmitter and ion concentrations. It produces an HRV time series that not only exhibits the features observed in real data, but also explains a reduction of low frequency band-power for physically or psychologically high intensity scenarios. Furthermore, the proposed model enables the personalisation of input parameters to the physiology of different people, a unique feature not present in existing methods. All these aspects are crucial for the understanding and application of future wearable health.