Recent advances in MEMS wearable inertial/magnetic sensors and mobile computing have fostered a dramatic growth of interest for ambulatory human motion capture (MoCap). Compared to traditional optical MoCap systems such as the optical systems, inertial (i.e. accelerometer and gyroscope) and magnetic sensors do not require external fixtures such as cameras. Hence, they do not have in-the-lab measurement limitations and thus are ideal for ambulatory applications. However, due to the manufacturing process of MEMS sensors, existing wearable MoCap systems suffer from drift error and accuracy degradation over time caused by time-varying bias. The goal of this research is to develop algorithms based on multi-sensor fusion and machine learning techniques for precise tracking of human motion and location using wearable inertial sensors integrated with absolute localization technologies. The main focus of this research is on true ambulatory applications in active sports (e.g., skiing) and entertainment (e.g., gaming and filmmaking), and health-status monitoring. For active sports and entertainment applications, a novel sensor fusion algorithm is developed to fuse inertial data with magnetic field information and provide drift-free estimation of human body segment orientation. This concept is further extended to provide ubiquitous indoor/outdoor localization by fusing wearable inertial/magnetic sensors with global navigation satellite system (GNSS), barometric pressure sensor and ultra-wideband (UWB) localization technology. For health applications, this research is focused on longitudinal tracking of walking speed as a fundamental indicator of human well-being. A regression model is developed to map inertial information from a single waist or ankle-worn sensor to walking speed. This approach is further developed to estimate walking speed using a wrist-worn device (e.g., a smartwatch) by extracting the arm swing motion intensity and frequency by combining sensor fusion and principal component analysis.