Dataflow-based modeling and performance analysis for online gesture recognition
Florian Grützmacher, Benjamin Beichler, Christian D. Haubelt, Bart D. Theelen · 2016
Cyber-Physical Systems (CPS) are tightly coupled with the environment, and therefore it is important that interactions with the surroundings like Human-Computer-Interactions are performed very responsive. Since CPS are often embedded without traditional input devices, like in medical or automotive contexts, gesture recognition approaches are emerging. As those algorithms are computationally complex especially when implemented on multi-core architectures, design decisions have to be taken carefully in order to meet performance and energy constraints. In this paper, we present a Scenario-Aware Dataflow model to estimate the performance of a template-based hand gesture recognition system based on Dynamic Time Warping (DTW). Our model enables us to estimate the important characteristics like real-time capabilities for online recognition and response time of the system when implemented on a multi-core architecture. Moreover, we introduce an extension to existing SADF performance analysis tools, which enables us to acquire processor utilization from our model. Based on the performance estimations the real-time capability for online recognition was validated for different configurations and verified in our experiments.