Locomotion activity recognition: A deep learning approach
Fuqiang Gu, Kourosh Khoshelham, Shahrokh Valaee · 2017
Human activity recognition is important for a large number of applications including indoor localization. Existing methods usually involve manually-designed features, which require expert knowledge and are laborious. Also, previous works use only the accelerometer for activity recognition, which may fail to recognize some complex activities. In this paper, we propose a deep learning-based method for locomotion activity recognition by using the combination of data from multiple smartphone built-in sensors. Eight types of locomotion activities are identified including the new `False Motion' activity introduced for the first time in this work. Experimental results show that the proposed method, which learns useful features automatically, outperforms conventional classifiers that require hand-engineering of features. Also, using data from multiple sensors helps to improve recognition accuracy by about 10% compared to that using accelerometer data only.