DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and heterogeneous user behaviors. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. On one hand, on-device sensor measurements are noisy. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Mobile sensing and computing applications usually require time-series inputs from sensors, such as accelerometers, gyroscopes, and magnetometers.
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