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AIRHAR

Financiado

An Energy-Efficient AI Powered Portable Radar System for Human Activity Recognit...

An Energy-Efficient AI Powered Portable Radar System for Human Activity Recognition Radar systems have been used in ambient sensing to track various subjects using electromagnetic waves. Thanks to the increasing capability of artificial intelligence algorithms in solving classification tasks, human activity detec... ver más

31/08/2025

TU Delft

203K€

Presupuesto del proyecto: 203K€

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-05-13

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

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Cerrada hace 2 años

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1 Participantes

TU Delft

203.46K€ | Lider

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Duración del proyecto: 27 meses Fecha Inicio: 2023-05-13
Fecha Fin: 2025-08-31

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 203K€

Descripción del proyecto Radar systems have been used in ambient sensing to track various subjects using electromagnetic waves. Thanks to the increasing capability of artificial intelligence algorithms in solving classification tasks, human activity detection (HAR) using radar systems have become possible. However, most previous solutions use bulky fixed radar systems with tens to hundreds of watts of power consumption, requiring rigid wall plug connection, making them environmentally unfriendly and difficult to use in applications like indoor security, healthcare, and mobile robots. In this project, we aim to develop a portable radar system for HAR by following a hardware-software co-design approach to significantly reduce the signal processing energy consumption compared to conventional radar-based HAR systems. On the software side, we will explore novel time-domain feature extraction methods to reduce the energy consumption of radar data analysis by at least 2 times. We will also apply brain-inspired neuromorphic principles to reduce 50 times the energy cost of state-of-the-art deep neural network architectures to solve radar-based HAR tasks. On the hardware side, we will develop artificial intelligence (AI) accelerator hardware based on field-programmable gated arrays (FPGAs) and application-specific integrated circuits (ASICs) to decrease the hardware energy consumption in radar data processing by at least 10 times. Overall, we expect the project results to revolutionize the paradigm of radar data processing to achieve over 20 times the whole system energy reduction, which will significantly contribute to the target of greenhouse gas emission reduction defined in the European Green Deal. The developed energy-efficient AI-powered portable radar system for HAR is promising to initiate a commercialized product for indoor security, healthcare, and mobile robot applications, and it will be competitive in the rapidly growing Internet-of-Things market worth USD 2400 billion by 2029.

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