Human Activity Signatures Captured under Different Directions Using SISO and MIMO Radar Systems

Waqar, Sahil; Muaaz, Muhammad; Pätzold, Matthias

doi:10.3390/app12041825

Cited by 10 publications

(16 citation statements)

References 56 publications

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“…. , L. For the wireless link A Tx i -A Rx k and the lth scatterer, the received beat signal s (l) b,ik (t ′ , t) can be expressed as [53] s…”

Section: A Mimo Radar Signal Preprocessingmentioning

confidence: 99%

“…Finally, the TV radial velocity distribution p ik (v, t) is obtained from the TV micro-Doppler signature S ik (f, t) according to [53]…”

Section: A Mimo Radar Signal Preprocessingmentioning

confidence: 99%

“…In the following, we develop a more pragmatic and complex HAR system suitable for detecting human activities with motion in multiple directions. To this end, we utilize the multiperspective 2 × 2 distributed MIMO radar configuration [53] (see Fig. 1) to eventually realize a direction-independent HAR system.…”

Section: Experimental Setup and The Proposed Solutionmentioning

confidence: 99%

“…and i = 1, 2. With the help of the Dirac delta function δ(•), we can express the transmit signal s i (t ′ , t) in terms of fast time t ′ and slow time t as[53]…”

mentioning

confidence: 99%

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Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Waqar,

Muaaz,

Pätzold

2023

IEEE Sensors J.

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Modern monostatic radar-based human activity recognition (HAR) systems perform very well as long as the direction of human activities is either towards or away from the radar. The monostatic single-input single-output (SISO) and monostatic multipleinput multiple-output (MIMO) radar systems cannot detect motion of an object that moves perpendicularly to the radar's boresight axis. Due to this physical layer limitation, today's radar-based HAR systems fail to classify multi-directional human activities. In this paper, we resolve this typical but critical physical layer problem of contemporary HAR systems. We propose a HAR system underlying a distributed MIMO radar configuration, where multiple antennas of a millimeter wave MIMO radar system (Ancortek SDR-KIT 2400T2R4) are distributed in an indoor environment. In our proposed HAR system, we have two independent and identical monostatic radar subsystems that irradiate and capture the multi-directional human movement from two perspectives, which allows to compute two distinct time-variant radial velocity distributions. A feature extraction network extracts numerous features from the measured time-variant radial velocity distributions, which are then fused by a multiclass classifier to detect five types of human activities. The proposed multi-perspective MIMO-radar-based HAR system achieves a classification accuracy of 98.52%, which surpasses the accuracy of SISO radar-based HAR system by more than 9%. Our approach resolves the physical layer limitations of modern HAR systems that are based on either monostatic SISO or monostatic MIMO radar systems.

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“…. , L. For the wireless link A Tx i -A Rx k and the lth scatterer, the received beat signal s (l) b,ik (t ′ , t) can be expressed as [53] s…”

Section: A Mimo Radar Signal Preprocessingmentioning

confidence: 99%

“…Finally, the TV radial velocity distribution p ik (v, t) is obtained from the TV micro-Doppler signature S ik (f, t) according to [53]…”

Section: A Mimo Radar Signal Preprocessingmentioning

confidence: 99%

Section: Experimental Setup and The Proposed Solutionmentioning

confidence: 99%

“…and i = 1, 2. With the help of the Dirac delta function δ(•), we can express the transmit signal s i (t ′ , t) in terms of fast time t ′ and slow time t as[53]…”

mentioning

confidence: 99%

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Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Waqar,

Muaaz,

Pätzold

2023

IEEE Sensors J.

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“…x and R 2 x are the transmission and receiving antennas of Radar II, respectively. To avoid interference between radar subsystems (Radar I and Radar II), we used RF cables of different lengths for each pair of collocated antennas of Radar I and Radar II [43,44]. A complete list of the system parameters of the SDR-KIT and the lengths of the RF cables used in the experimental setup are provided in Table 1.…”

Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing

Muaaz

Waqar

Pätzold

2023

Sensors

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RF sensing offers an unobtrusive, user-friendly, and privacy-preserving method for detecting accidental falls and recognizing human activities. Contemporary RF-based HAR systems generally employ a single monostatic radar to recognize human activities. However, a single monostatic radar cannot detect the motion of a target, e.g., a moving person, orthogonal to the boresight axis of the radar. Owing to this inherent physical limitation, a single monostatic radar fails to efficiently recognize orientation-independent human activities. In this work, we present a complementary RF sensing approach that overcomes the limitation of existing single monostatic radar-based HAR systems to robustly recognize orientation-independent human activities and falls. Our approach used a distributed mmWave MIMO radar system that was set up as two separate monostatic radars placed orthogonal to each other in an indoor environment. These two radars illuminated the moving person from two different aspect angles and consequently produced two time-variant micro-Doppler signatures. We first computed the mean Doppler shifts (MDSs) from the micro-Doppler signatures and then extracted statistical and time- and frequency-domain features. We adopted feature-level fusion techniques to fuse the extracted features and a support vector machine to classify orientation-independent human activities. To evaluate our approach, we used an orientation-independent human activity dataset, which was collected from six volunteers. The dataset consisted of more than 1350 activity trials of five different activities that were performed in different orientations. The proposed complementary RF sensing approach achieved an overall classification accuracy ranging from 98.31 to 98.54%. It overcame the inherent limitations of a conventional single monostatic radar-based HAR and outperformed it by 6%.

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3D Pain Face Expression Recognition Using a ML-MIMO Radar Profiler

López,

Palacios-Arias,

Romeu

et al. 2024

IEEE Access

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This study proposes a new method for the detection of facial expressions of pain using a 3D profiler that combines a multiple-input-multiple-output (MIMO) radar system with a machine learning (ML) model (ML-MIMO radar profiler). It offers a promising solution for the pain detection of facial expressions in a non-invasive, non-intrusive, and cost-effective manner. The ML-MIMO radar profiler employs six radars behind a lens to monitor changes in six facial regions and to build a 3D facial profile with real-time facial activity information. A dielectric lens was used to ensure an optimal beam size to effectively illuminate each face region. Signal processing is performed using dynamic time deformation to determine the longitudinal distance and a discrete stationary wavelet transform to filter the signal and improve accuracy. The information from the 3D profiler was compared with the facial action coding system (FACS) to determine actual facial expressions. A machine learning algorithm was trained to learn action units from the FACS and compare them with the information provided by the ML-MIMO radar profiler, thus performing facial expression classification. In this study, we analyzed four facial expressions: joy, sadness, anger, and pain. Identification and classification were performed using a machine-learning model based on multilayer perceptrons. The results revealed 92% accuracy of the system for pain expression, whereas expressions of happiness, sadness, and anger were detected with 88, 86, and 87% accuracy, respectively.

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Human Activity Signatures Captured under Different Directions Using SISO and MIMO Radar Systems

Cited by 10 publications

References 56 publications

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing

3D Pain Face Expression Recognition Using a ML-MIMO Radar Profiler

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