On the Use of Low-Cost Radars and Machine Learning for In-Vehicle Passenger Monitoring

Abedi, Hajar; Luo, Shenghang; Shaker, George

doi:10.1109/sirf46766.2020.9040191

Cited by 19 publications

(13 citation statements)

References 5 publications

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“…Advances in 77 GHz RF design with integrated digital Complementary Metal-Oxide Semiconductor (CMOS) and packaging led to radar-on-chip and antenna-on-chip systems. 35 In our lab, we work closely with Texas Instruments (TI), 33,34 Infineon, 36 and Vayyar's mm-wave systems. 37 Since they have been widely available in evaluation kits, we will only discuss here TI's 77 GHz FMCW radar chips and the corresponding evaluation boards which are built with the low-power 45-nm RF CMOS process.…”

Section: Fmcw Radar Conceptmentioning

confidence: 99%

“…This leads to better detection results compared to methods using only one transmitter and one receiver, as will be shown in this work. Contrary to our previous research 33,34 in which we deployed machine learning algorithms to count the number of passengers and identify occupied seats, in this paper, we base our method on a simple signal processing method. In fact, there is no need to know the number of occupants or their location in a parked car as the key required information is the presence or absence of a living body, especially a child or an infant, to prevent death.…”

Section: Introductionmentioning

confidence: 99%

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Improving passenger safety in cars using novel radar signal processing

Abedi

Magnier

Mazumdar

et al. 2021

Engineering Reports

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According to the group Kids and Cars, since 1990, nearly 1000 kids lost their lives because they were deliberately or unintentionally left in parked vehicles to potentially overheat or freeze. The development of technology able to prevent and address this serious, worldwide problem is crucial. In this paper, we deploy a radar-based sensor for in-vehicle presence-absence detection of a living body. We present a novel radar signal processing technique to identify the presence or absence of a living body in a vehicle using a mm-wave frequency-modulated continuous-wave (FMCW) radar. Our proposed method is based on reflections from breathing cycles creating correlated and consistent micro-Doppler effects over time. The performance of the system is evaluated with adults and two phantoms mimicking the breathing of children in various scenarios. The results show that we can clearly detect any tiny living body in vehicles with 100% accuracy without a need for any compute-intensive complex signal processing, making the system of extreme low-cost. The results demonstrate the high sensitivity and robustness of the mm-wave system in extensive studies over the course of multiple months.

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Section: Fmcw Radar Conceptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving passenger safety in cars using novel radar signal processing

Abedi

Magnier

Mazumdar

et al. 2021

Engineering Reports

Self Cite

View full text Add to dashboard Cite

show abstract

“…The presence of passengers for each seat was investigated using IR-UWB radar [15,16]. In addition, using a multiple-input and multiple-output frequency-modulated continuouswave (FMCW) radar, machine learning algorithms were integrated with DOA estimation to identify occupied seats [17]. In addition, a small child or infant was detected by consistent micro-Doppler effects on the breathing cycles [18].…”

Section: Introductionmentioning

confidence: 99%

“…Although these results were based on certain criteria for the performance evaluation, the criteria were neither rigorous nor clearly defined. Multi-passenger occupancy detection has been studied using the multi-channel FMCW radar [17,18]. However, the multi-channel FMCW radar is difficult to implement and its size and cost is relatively unfavourable compared with the single-channel version.…”

Section: Introductionmentioning

confidence: 99%

Single-Channel FMCW-Radar-Based Multi-Passenger Occupancy Detection Inside Vehicle

Song

Shin

2021

Entropy

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In this paper, we provide the results of multi-passenger occupancy detection inside a vehicle obtained using a single-channel frequency-modulated continuous-wave radar. The physiological characteristics of the radar signal are analyzed in a time-frequency spectrum, and features are proposed based on these characteristics for multi-passenger occupancy detection. After clutter removal is applied, the spectral power and Wiener entropy are proposed as features to quantify physiological movements arising from breathing and heartbeat. Using the average means of both the power and Wiener entropy at seats 1 and 2, the feature distributions are expressed, and classification is performed. The multi-passenger occupancy detection performance is evaluated using linear discriminant analysis and maximum likelihood estimation. The results indicate that the proposed power and Wiener entropy are effective features for multi-passenger occupancy detection.

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“…In conventional supervised machine-learning, well-defined extracted features are essential. Typical machine learning methods are the k-nearest neighbor (KNN), support vector machine (SVM), and random forest methods [ 29 , 30 , 31 , 32 , 33 ]. However, the recognition performance is strongly dependent on the predefined features.…”

Section: Introductionmentioning

confidence: 99%

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Song

Kim

et al. 2021

Sensors

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Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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On the Use of Low-Cost Radars and Machine Learning for In-Vehicle Passenger Monitoring

Cited by 19 publications

References 5 publications

Improving passenger safety in cars using novel radar signal processing

Improving passenger safety in cars using novel radar signal processing

Single-Channel FMCW-Radar-Based Multi-Passenger Occupancy Detection Inside Vehicle

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

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