Dataflow-based modeling and performance analysis for online gesture recognition

Dataflow-based modeling and performance analysis for online gesture recognition

Grützmacher, F.
Beichler, B.
Haubelt, C.
Theelen, B.

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.

ICT
ESI - Embedded Systems Innovations
TS - Technical Sciences
Informatics
Industrial Innovation
Human computer interaction
Cyber-physical systems
CPS
Hand-gesture recognition
On-line recognition
Gesture recognition
Dynamic time warping
DTW

http://resolver.tudelft.nl/uuid:0352bb12-8cd7-44c4-8daa-6026ce2fa474

https://doi.org/10.1109/cpsdata.2016.7496423

572393

Institute of Electrical and Electronics Engineers Inc.

9781509011544

2nd International Workshop on Modelling, Analysis, and Control of Complex CPS, CPS Data 2016, 11 April 2016

7496423

conference paper