Skills are generally designed by a domain expert, but designing a `good' set of skills can be challenging in high-dimensional, complex domains. In some cases, the skills may contain useful prior knowledge but cannot solve the task, resulting in a sub-optimal solution or no solution at all. Given a `poor' set of skills, we would like to dynamically improve them. The main contribution of this paper is showing that Interrupting Options (IO) can improve the initial skill set in a high-dimensional, complex domain by learning when to switch between skills. Furthermore, we discuss some of the pitfalls we ran into while trying to get IO to work.

Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination of feature extraction using time-delay embedding and supervised learning. The computational requirements are considerably lower than existing approaches, so the processing can be done in real time on a low-powered portable device such as a mobile phone. We evaluate the performance of our algorithm on a large, noisy data set comprising over 50 hours of data from six different subjects, including activities such as running and walking up or down stairs. We also demonstrate the ability of the system to accurately classify an individual from a set of 25 people, based only on the characteristics of their walking gait. The system requires very little parameter tuning, and can be trained with small amounts of data.