HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar

Lee, Shih-Po; Kini, Niraj Prakash; Peng, Wen-Hsiao; Ma, Ching-Wen; Hwang, Jenq‐Neng

doi:10.1109/wacv56688.2023.00567

Cited by 26 publications

(4 citation statements)

References 29 publications

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“…Moreover, an accurate and reliable full-body 3D pose estimation could be used as a direct replacement of optical motion capture. Several publications have proposed pose-estimation algorithms applying DL models on MIMO-generated spatial heatmaps [126]- [130]. However, the proposed works still need to be validated for their applicability in gait analysis.…”

Section: B Gait and Motion Analysismentioning

confidence: 99%

A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring

Krauss,

Engel,

Ott

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and MLbased data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

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Section: B Gait and Motion Analysismentioning

confidence: 99%

A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring

Krauss,

Engel,

Ott

et al. 2024

IEEE Open J. Eng. Med. Biol.

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show abstract

“…Having obtained the above interferometric phases, the azimuth position X and the elevation position Z can be calculated, respectively, via (5) as…”

Section: Interferometric Geometry and Retrieval Of Positionsmentioning

confidence: 99%

“…Using radar to detect human motion for classification and recognition has attracted significant attention in recent years [1][2][3]. Recent works have been focused on human skeletal posture estimation [4][5][6], human parsing [7], and 3D body mesh estimation [8,9] based on millimeter-wave MIMO radar. Except for the potential applications for human-computer interaction and gait recognition [10,11], there is a great potential to monitor human motions without privacy invasion for the physical health care of elderly people and patients [12,13].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Human Limbs Based on Micro-Doppler-Range Trajectories Using Wideband Interferometric Radar

He,

Zhang,

Dong

2023

Sensors

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In this paper, we propose to extract the motions of different human limbs by using interferometric radar based on the micro-Doppler-Range signature (mDRS). As we know, accurate extraction of human limbs in motion has great potential for improving the radar performance on human motion detection. Because the motions of human limbs usually overlap in the time-Doppler plane, it is extremely hard to separate human limbs without other information such as the range or the angle. In addition, it is also difficult to identify which part of the body each signal component belongs to. In this work, the overlaps of multiple components can be solved, and the motions from different limbs can be extracted and classified as well based on the extracted micro-Doppler-Range trajectories (MDRTs) along with a proposed three-dimensional constant false alarm (3D-CFAR) detection. Three experiments are conducted with three different people on typical human motions using a 77 GHz radar board of 4 GHz bandwidth, and the results are validated by the measurements of a Kinect sensor. All three experiments were repeatedly conducted for three different people of different heights to test the repeatability and robust of the proposed approach, and the results met our expectations very well.

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“…While research demonstrates the potential of mmWave sensors for various applications [17][18][19][20], including fire safety [21,22], widespread deployment faces significant challenges [23]. Sensor performance can be significantly influenced by variations in both hardware and deployment environments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Movable Mannequin Platform for Evaluating and Optimising mmWave Radar Sensor for Indoor Crowd Evacuation Monitoring Applications

Chan,

Gao,

Zhou

et al. 2024

Fire

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Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane.

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HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar

Cited by 26 publications

References 29 publications

A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring

A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring

Extraction of Human Limbs Based on Micro-Doppler-Range Trajectories Using Wideband Interferometric Radar

A Novel Movable Mannequin Platform for Evaluating and Optimising mmWave Radar Sensor for Indoor Crowd Evacuation Monitoring Applications

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