DeepSEAS: Smartphone-based Early Ailment Sensing Using Coupled LSTM AutoEncoders - Murthy, Shreesha Narasimha; Asani, Florina; Srikanthan, Srinarayan; Agu, Emmanuel (2020 IEEE International Conference on Big Data (Big Data), 2020)
Infectious diseases epidemics such as the current COVID-19 pandemic have an immense impact on all facets of life. Consequently, the current dearth of effective and timely public health surveillance methods, especially at the individual level, have been accentuated, prompting research into supplementary methods. Sensor-rich, ubiquitously owned smartphones can now gather large volumes of data that has been utilized for passive and continuous physical and mental health assessment. In this paper, we propose a Deep learning based Smartphone Early Ailment Sensing (DeepSEAS) framework that predicts a smart-phone user's future manifestation of influenza-like biological symptoms (e.g. coughing and sneezing) a day early while they are still asymptomatic. DeepSEAS works by analyzing a subject's historical one-day smartphone sensor and mobility data. First, we utilize the mean shift clustering algorithm to create clusters of users with similar social and behavioral traits such as their socialization levels, social media presence, eating and working out habits. Then, DeepSEAS employs an end-to-end trainable LSTM Autoencoder (LSTM AE) coupled with a Feed Forward Neural network classifier, a chieving a sensitivity of 7 8% i n correctly identifying users who will manifest biological symptoms a day later. DeepSEAS facilitates up-to-date influenza s urveillance at the individual level, which could transform the current healthcare system. Early detection can enable asymptomatic users to be alerted, notified and isolated, which could reduce disease transmission.