Classification in C-band of doppler signatures of human activities in indoor environments

Cadart, Pierre; Merlin, M. Mary Mejrullo; Manfredi, Giovanni; Fix, Jérémy; Ren, Chengfang; Hinostroza, Israel; Letertre, Thierry

doi:10.1049/icp.2021.0597

Cited by 2 publications

(7 citation statements)

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“…Among these reported results, submission [34] only reported the best run for individual action accuracy using 10-fold crossvalidation. Submission [35] only reported the best run for average classification accuracy.…”

Section: A Challenge Outcomesmentioning

confidence: 99%

“…MobileNetV2, a lightweight network with 2.23M hyperparameters, achieved the highest performance amongst their selection of tested models on the seen data with 95.43% accuracy overall. The model from submission G0164 [34] and G0121 [30] both used slightly modified ensemble methods, which achieved the joint 4th place. Furthermore, Fig.…”

Section: A Challenge Outcomesmentioning

confidence: 99%

“…Denoising From the raw data, [34] used a wavelet transform approach to first denoise the signal before reconstructing it using Daubechy's db10 wavelet. This had the benefit of both reducing the noise by retaining only the decompositions Range compression All contributions used FFT to generate the range profiles; only [37] used a Hamming window on the raw data to smooth the range profiles at the cost of widening the range peaks.…”

Section: B Pre-processingmentioning

confidence: 99%

“…Moving target indicator (MTI) approaches were used in all submissions except [34]. MTI is used to remove stationary clutter from the environment to improve the contrast between static and moving targets.…”

Section: B Pre-processingmentioning

confidence: 99%

“…However, [27] used the phase information of the mD signature. The RT domain amplitude is used in [29], [34], [37] for classification leveraging the range information for classification, as it may help to distinguish from mD signatures that are similar but would have a larger range spread, for example. [29] and [30] utilize the Cadence Velocity Diagram which is obtained by applying an FFT on the mD signatures to determine if there are frequencies repeating themselves across the Doppler bins.…”

Section: Radar Data Domain For Classificationmentioning

confidence: 99%

See 4 more Smart Citations

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: A Challenge Outcomesmentioning

confidence: 99%

Section: A Challenge Outcomesmentioning

confidence: 99%

Section: B Pre-processingmentioning

confidence: 99%

Section: B Pre-processingmentioning

confidence: 99%

Section: Radar Data Domain For Classificationmentioning

confidence: 99%

See 3 more Smart Citations

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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A Voxelization Algorithm for Reconstructing mmWave Radar Point Cloud and an Application on Posture Classification for Low Energy Consumption Platform

Cui

Dahnoun

2023

Sustainability

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Applications for millimeter-wave (mmWave) radars have become increasingly popular in human activity recognition. Many researchers have combined radars with neural networks and gained a high performance on various applications. However, most of these studies feed the raw point cloud data directly into the networks, which can be unstable and inaccurate under certain circumstances. In this paper, we define a reliability measure of the point cloud data and design a novel voxelization algorithm to reconstruct the data. Experiments show that our algorithm can improve the stability of the point cloud generated from mmWave radars in terms of error reduction and scene re-construction. We demonstrate the effectiveness of our proposed algorithm using a neural network-based system for identifying a person’s sitting direction. In our experiment, compared with the baseline, our voxelization algorithm can improve the system in terms of accuracy (4.3%), training time (55.6%), and computational complexity, which is more suitable for light-weighted networks and low energy consumption platforms.

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Classification in C-band of doppler signatures of human activities in indoor environments

Cited by 2 publications

References 0 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

A Voxelization Algorithm for Reconstructing mmWave Radar Point Cloud and an Application on Posture Classification for Low Energy Consumption Platform

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