Towards Real-Time 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/isie51582.2022.9831677

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This can enable the deployment of deep learning classifiers onto resource-constrained platforms. The effect of the precision of the representation for signal processing and classification (full/half-precision floating point, fixed precision) and the reduction of the general footprint (hardware resources, energy) are still open challenges for realtime implementation with works emerging in this area [50]- [52].…”

Section: Future Workmentioning

confidence: 99%

The Human Activity Radar Challenge: Benchmarking Based on the ‘Radar Signatures of Human Activities’ Dataset From Glasgow University

Yang

Kernec

Romain

et al. 2023

IEEE J. Biomed. Health Inform.

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Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting conditions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

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Section: Future Workmentioning

confidence: 99%

The Human Activity Radar Challenge: Benchmarking Based on the ‘Radar Signatures of Human Activities’ Dataset From Glasgow University

Yang

Kernec

Romain

et al. 2023

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

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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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Towards Real-Time Implementation for the Pre-Processing of Radar-Based Human Activity Recognition

Cited by 2 publications

References 14 publications

The Human Activity Radar Challenge: Benchmarking Based on the ‘Radar Signatures of Human Activities’ Dataset From Glasgow University

The Human Activity Radar Challenge: Benchmarking Based on the ‘Radar Signatures of Human Activities’ Dataset From Glasgow University

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

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