A Posture Recognition Based Fall Detection System using a 24GHz CMOS FMCW Radar SoC

Liang, Tingxuan; Xu, Hongtao

doi:10.1109/iws52775.2021.9499555

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Traditionally, researchers have explored serval methods based on FMCW radars, which range from 57–85 GHz [ 14 ]. The Doppler information could describe the velocity attribute of a motion; thus, the range-Doppler map has been widely used in FMCW radar-based fall detection methods proposed in the literature [ 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Liang,

Liu,

Yang

et al. 2024

Sensors

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Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.

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Section: Introductionmentioning

confidence: 99%

Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Liang,

Liu,

Yang

et al. 2024

Sensors

Self Cite

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“…The resulting academic developments are based on continuous wave (CW), frequency-modulated CW (FMCW), stepped-frequency CW (SFCW) and ultra-wideband impulse-radio (UWB-IR) radars [20]- [49]. Those devices have been also used for fall detection [50]- [62]. The most investigated approaches are based on machine and deep learning techniques, such as convolutional neural network (CNN), long short-term memory (LSTM), random forest, support vector machine (SVM), and dynamic time warping (DTW) [50]- [58].…”

Section: Introductionmentioning

confidence: 99%

“…They aim at detecting the difference in speed profile between a fall and a normal activity (e.g., walking, eating, sitting down, etc.). Alternative solutions do not rely on speed information but aim at determining the posture change, assuming that almost all falls involve a transition of posture from standing to lying on the ground [59]- [62]. However, in these papers, the radar signals, and then the single activities, were collected and processed offline.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Radar System for Real-Time Contactless Fall Detection and Indoor Localization

Mercuri

Soh

Mehrjouseresht

et al. 2023

IEEE J. Electromagn. RF Microw. Med. Biol.

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Fall incidents represent a major public health problem among elderly people. This resulted in a significant increase of the number of investigated systems aiming at detecting falls promptly. In this respect, in this work, a biomedical radar system is proposed for remote real-time fall detection and indoor localization. The system, consisting of a sensor and a base station, combines radar and wireless communication techniques, and uses a data processing technique to distinguish between fall events and normal movements. The classification, based on a Least-Square Support Vector Machine (LS-SVM), combined with the sliding window principle allows to perform fall detection in real-time. Moreover, it is capable to localize the subjects when the fall incident has been detected. The in-vivo validation showed a high success rate in detecting fall events, with a maximum delay of 340 ms. Moreover, a maximum mean absolute errors (MAE) of 3.8 cm and a maximum root-mean-square error (RMSE) of 7.5 cm were reported in measuring the subject's absolute distance.

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“…Aman Shrestha et al [ 20 ] introduced a method based on recurrent long and short-term memory (LSTM) and bi-directional LSTM network architecture for continuous human activity monitoring and classification, achieving an average accuracy of over 90% when combined with Doppler domain data from FMCW radar. Liang et al [ 21 ] designed a fall detection system based on FMCW radar, using Bi-LSTM for classification. The system achieved a remarkable 99% classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

Zhang

et al. 2023

Sensors

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Human posture recognition technology is widely used in the fields of healthcare, human-computer interaction, and sports. The use of a Frequency-Modulated Continuous Wave (FMCW) millimetre-wave (MMW) radar sensor in measuring human posture characteristics data is of great significance because of its robust and strong recognition capabilities. This paper demonstrates how human posture characteristics data are measured, classified, and identified using FMCW techniques. First of all, the characteristics data of human posture is measured with the MMW radar sensors. Secondly, the point cloud data for human posture is generated, considering both the dynamic and static features of the reflected signal from the human body, which not only greatly reduces the environmental noise but also strengthens the reflection of the detected target. Lastly, six different machine learning models are applied for posture classification based on the generated point cloud data. To comparatively evaluate the proper model for point cloud data classification procedure—in addition to using the traditional index—the Kappa index was introduced to eliminate the effect due to the uncontrollable imbalance of the sampling data. These results support our conclusion that among the six machine learning algorithms implemented in this paper, the multi-layer perceptron (MLP) method is regarded as the most promising classifier.

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A Posture Recognition Based Fall Detection System using a 24GHz CMOS FMCW Radar SoC

Cited by 7 publications

References 7 publications

Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Biomedical Radar System for Real-Time Contactless Fall Detection and Indoor Localization

Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

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