b-COELM: A fast, lightweight and accurate activity recognition model for mini-wearable devices

Various mini-wearable devices have emerged in the past few years to recognize activities of daily living for users. Wearable devices are normally designed to be miniature and portable. Models running on the devices inevitably face following challenges: low-computational-complexity, lightweight and high-accuracy. In order to meet these requirements, a novel powerful activity recognition model named b-COELM is proposed in this paper. b-COELM retains the superiorities (low-computational-complexity, lightweight) of Proximal Support Vector Machine, and extends the powerful generalization ability of Extreme Learning Machine in multi-class classification problems. Experimental results show the efficiency and effectiveness of b-COELM for recognizing activities of daily living.