System-Level Power Consumption Modeling for Autonomy Estimation on Smart Connected Glasses

Description

Wearable devices running sensor-based applications for human health, wellness and safety monitoring are increasinglyuseful, looking for early diagnoses of pathologies as well as for detection of risks. The Ellcie-Healthy start-up is developing smart connected glasses, a wearable device designed for e-Health and road safety applications. The main constraint on this devices is the imited amount of embedded energy for running applications, making necessary the study of the power consumption through a system level modeling approach for getting fast and accurate information useful for energy consumption optimization. In this paper, we propose to estimate the autonomy of these glasses using analytical power models based on the system activities. Those models are validated through experimental measurements on real scenarios. The lifetime of the smart glasses can be estimated for different system configurations with less than 10% error.

Abstract

National audience

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