DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices

Sensors

Rui Zhang¹  Giovanni Schiboni¹  JuanCarlos Suarez¹  Oliver Amft¹ 

[1] Chair of Digital Health, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany;

关键词: health monitoring;    automatic dietary monitoring;    physiological sensing;    pattern spotting;    energy saving;    embedded machine learning;   

DOI  :  10.3390/s20216104

来源: DOAJ

【 摘 要 】

We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.

【 授权许可】

Unknown