Continuous and robust monitoring of physiological signals with wearable devices provides new opportunities for improving the care and outcomes for people with or at risk of a broad range of adverse health events. One challenge has been that many devices are obtrusive, cumbersome, not very convenient to wear, and unsuitable for ambulatory care. Limited reliability and robustness of these sensing paradigms have also been among contributing factors that prohibit the adoption of wearable devices into the FDA-regulatory space and medical applications. Another challenge to the current method of measuring certain hemodynamic parameters such as blood pressure (BP), is that cuff-based sensors are only capable of providing infrequent measurements and are uncomfortable. Elevated BP is a critically important risk factor for various cardiovascular disorders (i.e., heart attack, stroke, heart failure), kidney diseases, vision loss, and sexual dysfunction. In this talk, we discuss our techniques that directly address these unmet needs for a device that can unobtrusively, accurately, and continuously measure BP. We seek to develop and test a novel transformative solution which addresses concerns of wearability and robust sensing, and enables new sensing paradigms that can be deployed for field-based, mobile, or ambulatory care leveraging bio-impedance. We will discuss a number of sensing, circuit and signal processing paradigms that capture physiological observations including bio-impedance. Our primary focus remains estimating blood pressure from a wrist-worn device with a watch form factor with high degrees of precision. We will discuss several methodologies for noise rejection that improve the robustness of signal acquisition. We will also discuss machine learning techniques that converts our bio-impedance observations to blood pressure. We will offer concluding remarks on the trends of wearable computing technology development and potential future directions.