Wearable devices are increasingly prevalent in our everyday lives. This thesis examines the potential of combining multiple wearable devices worn on different body locations for fitness activity recognition and inertial dead-reckoning. First, a novel method is presented to classify fitness activities using head-worn sensors, with comparisons to other common worn locations on the body. Using multiclass Support Vector Machine (SVM) on head-worn sensors, high degree of accuracy was obtained for classifying standing, walking, running, ascending/descending stairs and cycling. Next, a complete inertial dead-reckoning system for walking and running using smartwatch and smartglasses is proposed. Head-turn motion can derail the position propagation on a head-worn dead-reckoning system. Using the relative angle rate-of-change between arm swing direction and head yaw, head-turn motion can be detected. The experimental results show that using the proposed head-turn detection algorithm, head-worn deadreckoning performance can be greatly improved.