Filtering Data Bins of UWB Radars for Activity Recognition with Random Forest

Imbeault-Nepton, Thomas; Maître, Julien; Bouchard, Kévin; Gaboury, Sébastien

doi:10.1016/j.procs.2022.03.009

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Afterwards, DL based healthcare applications using radar gained most of the attention. Nevertheless, shallow learning models are still in practice for applications based on VS measurement [47] and activity recognition [122].…”

Section: Market Statistics and Ots Radarsmentioning

confidence: 99%

“…where x U W B (t) represents the transmitted discrete signal, n represents the delay between the two consecutive pulses within a frame, and s represents the shape of transmitted signal. 76 GHz [8], [46], [65], [75], [83], [86], [105], [114], [ 7 GHz [11], [48], [54], [66], [71], [80], [99], [113], [122], [ Similarly, signal transmitted by FMCW radar can be expressed as:…”

Section: B Radar Taxonomymentioning

confidence: 99%

“…For instance, we witnessed a two-stage classification approach based on random forest recently [121]. Another recent attempt utilizing random forest classifier to recognize fifteen activities of daily living [122]. In addition to that, shallow SVM and shallow NN are recently being used for activity classification in references [141].…”

Section: B Human Activity Recognitionmentioning

confidence: 99%

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Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

Ahmed,

Cho

2024

IEEE Commun. Surv. Tutorials

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The unprecedented non-contact, non-invasive, and privacy-preserving nature of radar sensors has enabled various healthcare applications, including vital sign monitoring, fall detection, gait analysis, activity recognition, fitness evaluation, and sleep monitoring. Machine learning (ML) is revolutionizing every domain, with radar-based healthcare being no exception. Progress in the field of healthcare radars and ML is complementing the existing radar-based healthcare industry. This article provides an overview of ML usage for two major healthcare applications: vital sign monitoring and activity recognition. Vital sign monitoring is the most promising healthcare application of radar, as it can predict several chronic cardiac and respiratory diseases. Activity recognition is also a prominent application since the inability to perform activities may result in critical suffering. The article presents an overview of commercial radars, radar hardware, and historical progress of healthcare radars, followed by the usage of ML for healthcare radars. Subsequently, the paper discusses how ML can overcome the limitations of conventional radar data processing chains for healthcare radars. The article also touches upon recent generative ML concepts used in healthcare radars. Among several interesting findings, it was discovered that ML does not completely replace existing vital sign monitoring algorithms; rather, ML is deployed to overcome the limitations of traditional algorithms. On the other hand, activity recognition always relies on ML approaches. The most widely used algorithms for both applications are Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM). Generative AI has the capability to augment data and is expected to have a significant impact soon. Recent trends, lessons learned from these trends, and future directions for both healthcare applications are presented in detail. Finally, the future work section discusses a wide range of healthcare topics for humans, ranging from neonates to elderly individuals.

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Section: Market Statistics and Ots Radarsmentioning

confidence: 99%

Section: B Radar Taxonomymentioning

confidence: 99%

Section: B Human Activity Recognitionmentioning

confidence: 99%

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Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

Ahmed,

Cho

2024

IEEE Commun. Surv. Tutorials

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“…In addition to the above classification algorithms (SVM [72][73][74][75]43,61], GMM [76,77], HMM [70,69,78]), some other classification algorithms are used for posture recognition, such as k-nearest neighbor (k-NN) [79], random forest (RF) [80][81][82], Bayesian classification algorithm [83], decision tree (DT) [72,84,85], linear discriminant analysis [86,60], naïve Bayes (NB) [72,87], etc.…”

Section: Other Classification Approachesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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