Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Zhang, Xinyu; Abbasi, Qammer H.; Fioranelli, Francesco; Romain, Olivier; Kernec, Julien Le

doi:10.1007/978-3-030-95593-9_4

Cited by 6 publications

(6 citation statements)

References 32 publications

(44 reference statements)

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“…One such vari-ant was used in reference [123] where the authors trained ResNet model to classify six activities and achieved 96% accuracy. In the similar way, GoogleNET has also shown its effectiveness to recognize six activities [146]. For the same dataset, ResNet with time-Doppler as input showed 85% accuracy [123] whereas GoogleNET with phase and amplitude of time-Doppler map as input showed 86% accuracy.…”

Section: Cnn Based Classifiersmentioning

confidence: 99%

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: Cnn Based Classifiersmentioning

confidence: 99%

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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“…Feature extraction from radar data can leverage the increasing dimensionality of radar signals (e.g., higher range resolution), frontends with multiple input and multiple output capabilities, increasing angular and spatial diversity, and on the various data representations ranging from raw IQ data directly sampled by the radar directly to range-time, range-Doppler, range-azimuth, angle-of-arrival, spectrograms, cadence velocity diagrams, cepstrograms, phase plots [46], [47], cyclostationarity signatures [48], radon transform signatures [49], and other composite views e.g., range-Doppler surfaces [30]. Thus, open research questions remain as to what format or combination of formats are most suitable for the classification process, perhaps exploiting forms of cognition that modify accordingly such processes depending on the specific activities to be classified.…”

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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“…In this study, we used the open dataset Radar Signatures of Human Activities [35], which was recently used by Zhang et al [83] to produce hybrid maps and train the CNN architectures LeNet-5 and GoogLeNet for classification and benchmarking, respectively, using transfer learning. Jiang et al [54] used this dataset for RNN-based classification with an LSTM-based classifier, achieving an average testing accuracy of 93.9%.…”

Section: Datasetmentioning

confidence: 99%

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

Papadopoulos,

Jelali

2023

Applied Sciences

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The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability.

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Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Abstract: There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

Cited by 6 publications

References 32 publications

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

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

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

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

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