One in four people who have experienced a myocardial infarction (MI) will go on to experience a second event in their lifetime, often resulting in early mortality. Despite this known high incidence of adverse outcomes, preventative monitoring for this population remains largely absent. In particular, there is a lack of continuous and objective assessment of the capability of the patient's cardiovascular system to withstand and adapt to daily stressors following an MI. This dissertation describes biosignal processing and algorithm development efforts towards enabling reliable ambulatory cardiovascular monitoring in the context of stress for this population. Such a system would be able to automatically identify abnormal stress-induced cardiovascular changes remotely, enabling preventative care. Towards this goal, we make the following key contributions through the research described. We first demonstrate the feasibility of quantifying complex stress-induced changes in cardiomechanical function, vascular dynamics, and eletrophysiology using a compact multimodal wearable patch, previously only possible through a disjoint collection of obtrusive wired devices. We then make several advances towards improving the usability of these signals. We validate that ECG features can be derived from the patch with equivalent fidelity to gold-standard wired devices. Utilizing the unique morphology of the SCG signal, we develop a novel TDA-based algorithm with potential to enable robust SCG signal quality assessment without reliance on an reference signal. We additionally take the first step towards the understanding the physiological origins and signal quality of core-body PPG signals as compared to peripheral measurement sites. Importantly, we find that the core-body PPG amplitude reacts in an opposite fashion to that of the peripheral site during respiratory challenges. Finally, using these insights, we create the first automated algorithm with capability to identify abnormal hemodynamic responses to stress in the post-MI population, uncovering novel potential SCG-based biomarkers of cardiac dysfunction in the process. Thus, this work presents a step towards continuous cardiovascular monitoring in at-home settings, which can provide clinicians with real-time physiological information to enable early, personalized clinical intervention for the growing population of post-MI survivors.