HEИндивидуална стипендия2023–2025

AIRHAR · An Energy-Efficient AI Powered Portable Radar System for Human Activity Recognition

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2023-09-01 → 2025-08-31
Финансиране от ЕС: 203 464 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Енергийно ефективни радарни системи с изкуствен интелект се разработват за разпознаване на човешки дейности, като например следене на пациенти в здравеопазването. Това позволява създаването на достъпни, батерийни устройства, които пазят личното пространство на потребителите.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

The project, AIRHAR, addressed the high energy consumption of modern Artificial Intelligence (AI) models, which limits their use in portable, battery-operated devices. Specifically, it focused on Human Activity Recognition (HAR) using radar, a technology that offers privacy-preserving monitoring for applications like ambient assisted living in healthcare, mobile robotics, and indoor security. The project's overall objective was to develop an energy-efficient, AI-powered portable radar system for HAR. The central strategy to achieve this was a hardware-software co-design approach, ensuring that the AI algorithms and the hardware accelerators were developed together to achieve maximum efficiency. The goal was to enable the creation of affordable, battery-powered, and privacy-respecting radar systems.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

TECHNISCHE UNIVERSITEIT DELFT · DelftКоординаторНидерландия