GPU Based Implementation for the Pre-Processing of Radar-Based Human Activity Recognition

Bordat, Alexandre; Dobiáš, Petr; Kernec, Julien Le; Guyard, David; Romain, Olivier

doi:10.1109/dsd57027.2022.00085

2022 25th Euromicro Conference on Digital System Design (DSD) 2022

DOI: 10.1109/dsd57027.2022.00085

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GPU Based Implementation for the Pre-Processing of Radar-Based Human Activity Recognition

Alexandre Bordat

Petr Dobiáš

Julien Le Kernec

et al.

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Cited by 3 publications

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Model Based Design of FMCW Radar Processing Systems on FPGA Platforms

Almorin,

Le Gal,

Jego

et al. 2023

2023 26th Euromicro Conference on Digital System Design (DSD)

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Model Based Design of FMCW Radar Processing Systems on FPGA Platforms

Almorin,

Le Gal,

Jego

et al. 2023

2023 26th Euromicro Conference on Digital System Design (DSD)

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Radar-Based Human Activity Acquisition, Classification and Recognition Towards Elderly Fall Prediction

Béranger,

Bordat,

Khelif

et al. 2023

2023 26th Euromicro Conference on Digital System Design (DSD)

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Falls represents the main risk of injuries for elderly people. One-third of adults aged over 65 and half of people over 80 will have at least one fall a year. However, the person needs to visit this type of service in order to detect gait difficulties. Solutions for detecting daily activities are being studied more and more, with the aim of developing a complementary method to early detect this type of health risk as effectively as possible. Non-intrusiveness in the person's life for this type of problem is an important criterion, which is why current research is focusing on solutions involving non-conventional imagery such as radar systems. This paper presents an embedded system for classifying daily activities based on the processing of micro-Doppler images. The implementation of the pre-processing chain with a filter enables the acquisition of detailed spectrograms, which proves to be effective in detecting walking. Additionally, by porting it onto the Jetson Orin, it will be possible to accelerate the inference phase of the classification model. We used ResNet-18 classification method to classify six human activities: Walking, Sitting, Standing, Picking up objects, Drinking water, and Fall events. The results showed that the model is capable of recognising most of the activities on real data.

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Real-Time Waveform-Diverse Pulse-Doppler Demo via Microwave Radar-in-a-Briefcase (MicRIB)

Fraka,

Higgins,

Owen

2024

2024 IEEE Radar Conference (RadarConf24)

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GPU Based Implementation for the Pre-Processing of Radar-Based Human Activity Recognition

Cited by 3 publications

References 20 publications

Model Based Design of FMCW Radar Processing Systems on FPGA Platforms

Model Based Design of FMCW Radar Processing Systems on FPGA Platforms

Radar-Based Human Activity Acquisition, Classification and Recognition Towards Elderly Fall Prediction

Real-Time Waveform-Diverse Pulse-Doppler Demo via Microwave Radar-in-a-Briefcase (MicRIB)

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