Sensors, Vol. 20, Pages 5114: Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders

Sensors, Vol. 20, Pages 5114: Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders Sensors doi: 10.3390/s20185114 Authors: Qin Ni Zhuo Fan Lei Zhang Chris D. Nugent Ian Cleland Yuping Zhang Nan Zhou Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the ov...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research