Transfer learning algorithms for autonomous reconfiguration of wearable systems

Wearables have emerged as a revolutionary technology in many application domains including healthcare and fitness. Machine learning algorithms, which form the core intelligence of wearables, traditionally deduce a computational model from a set of training examples to detect events of interest (e.g. activity type). However, in the dynamic environment in which wearables typically operate in, the accuracy of a computational model drops whenever changes in configuration of the system (such as device type and sensor orientation) occur. Therefore, there is a need to develop systems which can adapt to the new configuration autonomously. In this paper, using transfer learning as an organizing principle, we develop several algorithms for data mapping. The data mapping algorithms employ effective signal similarity methods and are used to adapt the system to the new configuration. We demonstrate the efficacy of the data mapping algorithms using a publicly available dataset on human activity recognition.