Fusion of Depth and Inertial Sensing for Human Action Recognition

Download or read book Fusion of Depth and Inertial Sensing for Human Action Recognition written by Chen Chen and published by . This book was released on 2016 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human action recognition is an active research area benefitting many applications. Example applications include human-computer interaction, assistive-living, rehabilitation, and gaming. Action recognition can be broadly categorized into vision-based and inertial sensor-based. Under realistic operating conditions, it is well known that there are recognition rate limitations when using a single modality sensor due to the fact that no single sensor modality can cope with various situations that occur in practice. The hypothesis addressed in this dissertation is that by using and fusing the information from two differing modality sensors that provide 3D data (a Microsoft Kinect depth camera and a wearable inertial sensor), a more robust human action recognition is achievable. More specifically, effective and computationally efficient features have been devised and extracted from depth images. Both feature-level fusion and decision-level fusion approaches have been investigated for a dual-modality sensing incorporating a depth camera and an inertial sensor. Experimental results obtained indicate that the developed fusion approaches generate higher recognition rates compared to the situations when an individual sensor is used. Moreover, an actual working action recognition system using depth and inertial sensing has been devised which runs in real-time on laptop platforms. In addition, the developed fusion framework has been applied to a medical application.