Automatic Model Construction for Activity Recognition Using Wearable Devices

Abstract

Wearable devices play an essential role in the Internet-of-things applications. By leveraging machine learning algorithms, they can achieve activity recognition, which is an essential function for Internet-of-Things applications, such as home automation, intelligent health care, connected vehicles and so on. However, because of the high substitutability, the wearable devices always have short lifetime, which makes the wearable devices with automatic model construction ability imperative. In this paper, we introduce an automatic model construction method for newly added wearable device, which can automatically construct the machine learning model without the need for any newly labeled training data. The method adopts the self-paced learning regime, which ensures the quality of the learning curriculum and further improves the accuracy of the automatically reconstructed model. We conduct empirical evaluation of our method compared with four state-of-the-art methods; and the evaluation shows that by applying our automatic construction method, we can achieve the reconstructed machine learning model with higher accuracy and lower computational cost.