| DOI | Trouver le DOI : https://doi.org/10.1109/LSENS.2023.3323903 |
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| Auteur | Rechercher : Dey, AnkitaIdentifiant ORCID : https://orcid.org/0000-0002-5201-1081; Rechercher : Rajan, SreeramanIdentifiant ORCID : https://orcid.org/0000-0003-0153-6723; Rechercher : Xiao, Gaozhi1Identifiant ORCID : https://orcid.org/0000-0001-7717-1818; Rechercher : Lu, Jianping1Identifiant ORCID : https://orcid.org/0000-0003-3152-7510 |
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| Affiliation | - Conseil national de recherches du Canada. Quantique et nanotechnologies
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| Format | Texte, Article |
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| Sujet | sensor signal processing; census transform (CT); fall detection; histogram of oriented gradients (HOG); local binary pattern (LBP); radar; radar imaging; feature extraction; radar detection; event detection; histograms; transforms |
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| Résumé | Existing deep learning techniques for radar-based fall event detection are computationally expensive and data hungry. This work proposes a novel methodology for radar-based fall event detection using computationally inexpensive traditional machine learning algorithms with the histogram of oriented gradients (HOG) of binary-encoded radar signatures. Radar signatures (binary) encoded using nonparametric local transforms, such as census transform (CT) and local binary pattern (LBP), contain enhanced gradient information and lead to distinguishable HOG features for better fall event detection. A comparative analysis of five different types of machine learning algorithms using HOG features from three different types of radar domain representations that are binary encoded in two different ways (CT and LBP) is presented. All evaluations are done on a publicly available dataset of human radar signatures. K-nearest neighbor algorithm using HOG features of binary-encoded spectrograms achieves an F1-score of 0.995. |
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| Date de publication | 2023-10-11 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | b86f8b65-eb91-4b80-87fb-e91af393e33e |
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| Enregistrement créé | 2024-10-28 |
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| Enregistrement modifié | 2024-10-28 |
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