An Autoencoder-based Approach for Recognizing Null Class in Activities of Daily Living In-the-wild via Wearable Motion Sensors

Recognizing activities of daily living (ADL) in-the-wild, while users follow their daily routine, is challenging due to the presence of various activities that do not belong to the set of desired activities in which the system is interested (i.e., NULL class). In this paper, we propose a framework for ADL recognition via wearable motion sensors with the ability to detect NULL class. Existing ADL recognition systems either ignore the NULL class or use some training data to train a model for recognizing it. However, our framework uses only samples of the desired activities in the training phase and learns to detect the NULL samples based on a modified variational autoencoder model that outputs reconstruction probability. Experimental results show that in detecting six ADL with accelerometer data, our system achieves 14% higher F1-score compared to the models that use training samples of NULL activities.