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Architecture of Ultra Low Power Node for Body Area Network

Abstract : Wireless Body Sensor Network (WBSN) is a promising technology that can be used in a lot of application domains from health care to Human Machine Interface (HMI). The BoWI project ambition is to evaluate and design a WBSN that can be used in various applications with daily usage and accessible to the public. This necessitates to design a ultra-low power node that reach a day of use without discomfort for the user. The elected solution is to design a node that operates with the power budget similar to what can be provided by the state of the art of the energy harvesting. An Application Specific Integrated Circuit (ASIC) solution is privileged in order to meet the integration and low power constraints. Designing the dedicated architecture required a preliminary study at several level which are: a state of the art of the energy harvesting in order to determine the objective of energy/power budget of our system, A study of the usage of the system to determine and select typical application cases. A study of the algorithms to address the selected applications while considering the implementation viability of the solutions. The power budget objective is set to 100µW. The application selected are the posture recognition, the gesture recognition and the motion capture. The algorithmic solution proposed are a data-fusion based on an Extended Kalman FIlter (EKF) with the addition of a classification using Principal Component Analysis (PCA). The implementation tool used to design the architecture is an High Level Synthesis (HLS) solution. Implementation results mainly focus on the EKF since this is by far the most power consuming digital part of the system. Using a 28nm technology the power budget objective can be reached for the algorithmic part. A study of the top level management of all components of the node is done in order to estimate performances of the system in real application case. This is possible using an activity detection which dynamically estimates the computing load required and then save a maximum of energy while the node is still.
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Submitted on : Friday, March 31, 2017 - 11:25:07 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
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  • HAL Id : tel-01499325, version 1


Alexis Aulery. Architecture of Ultra Low Power Node for Body Area Network. Hardware Architecture [cs.AR]. Université de Bretagne Sud, 2016. English. ⟨NNT : 2016LORIS419⟩. ⟨tel-01499325⟩



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