DeepSense: A unified deep learning framework for time-series mobile sensing data processing

DeepSense is a deep learning framework that runs on mobile devices, and can be used for regression and classification tasks based on data coming from mobile sensors (e.g., motion sensors). An example of a classification task is heterogeneous human activity recognition (HHAR) – detecting which activity someone might be engaged in (walking, biking, standing, and so on) based on motion sensor measurements. Another example is biometric motion analysis where a user must be identified from their gait. An example of a regression task is tracking the location of a car using acceleration measurements to infer position. Compared to the state-of-art, DeepSense provides an estimator with far smaller tracking error on the car tracking problem, and outperforms state-of-the-art algorithms on the HHAR and biometric user identification tasks by a large margin.