UniTS
Shuheng Li, Ranak Roy Chowdhury, Jingbo Shang, Rajesh K. Gupta, Dezhi Hong
Abstract
Discovering patterns in time series data is essential to many key tasks in intelligent sensing systems, such as human activity recognition and event detection. These tasks involve the classification of sensory information from physical measurements such as inertial or temperature change measurements. Due to differences in the underlying physics, existing methods for classification use handcrafted features combined with traditional learning algorithms, or employ distinct deep neural models to directly learn from raw data.
Topics & Concepts
Computer scienceArtificial intelligenceKey (lock)Event (particle physics)Deep learningRaw dataMachine learningArtificial neural networkActivity recognitionPattern recognition (psychology)Computer securityQuantum mechanicsProgramming languagePhysicsTime Series Analysis and ForecastingContext-Aware Activity Recognition SystemsAnomaly Detection Techniques and Applications