OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition

Miscellaneous mini-wearable devices (Jawbone Up, Apple Watch, Google Glass, et al.) have emerged in recent years to recognize the user's activities of daily living (ADLs) such as walking, running, climbing and bicycling. To better suits a target user, a generic activity recognition (AR) model inside the wearable devices requires to adapt itself according to the user's personality in terms of wearing styles and so on. In this paper, an online kernelized and regularized extreme learning machine (OKRELM) is proposed for wearable-based activity recognition. A small-scale but important subset of every incoming data chunk is chosen to go through the update stage during the online sequential learning. Therefore, OKRELM is a lightweight incremental learning model with less time consumption during the update and prediction phase, a robust and effective classifier compared with the batch learning scheme. The performance of OKRELM is evaluated and compared with several related approaches on a UCI online available AR dataset and experimental results show the efficiency and effectiveness of OKRELM.