Falls are the leading cause of injuries and even fatalities among elderly individuals in home environments, resulting in the development of fall detection technology particularly crucial. In this paper, we propose a robust human fall detection method based on the millimeter-wave radar and 4D point cloud imaging technology. The main objective of this method is to detect various types of fall actions in real-time and provide timely alerts to assist the fallen individuals. In our proposed method, we first perform range-FFT and static clutter suppression on the radar echo data. Subsequently, we conduct range-domain target detection and angle estimation to generate initial point cloud information. Next, we introduce the median absolute deviation (MAD) based outlier removal method to eliminate non-human body outliers from the point cloud. Lastly, we present a suspected fall detection (SFD) method and a secondary fall detection method based on support vector machines (SVM) to maintain high detection accuracy while minimizing false alarms. The experimental results demonstrate that the average detection accuracy of our method for different types of falls is 97.5%, with an average false alarm rate of 0.4%.
Due to the excellent advantages of the radar sensor, it is considered to be one of the most potential technologies for the sleep monitoring. In this paper, we propose a sleep stage estimation method based on state transition using frequency-modulated continuous wave (FMCW) millimeter-wave (MMW) radar. The core of the proposed method is to utilize the physiological characteristics of different state transitions during sleep to achieve state transitions for different human targets. Firstly, we conduct signal preprocessing and target detection to determine the presence of the target. Secondly, we extract features from the respiratory rate and body movement to determine the start of sleep and the end time of sleep. Finally, we employ reference thresholds to determine the state transition of sleep for sleep staging. A total of more than 138 nights of data from 10 participants were tested and compared with the Mi Band 6, Mi Band 7, and Huawei Band 6. The results demonstrate the effectiveness of the proposed method.
Non-contact sleep apnea detection based on radar is a technology of great significance and becomes a research hotspot because of the outstanding advantages of the radar sensors. In this paper, we propose a low-complexity sleep apnea detection method using millimeter-wave radar. The core idea of the proposed method is utilizing the reference thresholds to obtain strong generalization ability for different human targets and sleep postures. Firstly, we perform pre-processing and target detection to obtain the range bins occupied by the target. Secondly, we detect the big movement based on the body movement index and extract the features based on resting energy and respiratory waveform of multiple scatterers. Finally, the reference thresholds are utilized for sleep apnea detection to avoid the impact of different human targets and sleep postures. Experiments with ten participants and three sleep postures are conducted and results show that the proposed method can detect sleep apnea in different sleep postures including supine, lateral, and prone, with an average accuracy of 99.8%.
A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar is presented in this paper. The main task is to detect whether there are vehicles approaching on both rear sides of the vehicle, and to give an early warning when the relative distance between the two vehicles is less than the early warning range. The difficulty in accurately detecting an approaching target vehicle lies in the fact that the vehicle is driving in a complex traffic scene and there are metal roadblocks in the road environments. In this paper, firstly, in order to reduce the complexity, the cascaded distance and angle of the potential target calculation is adopted to achieve the localization of the potential target. Then, rough recognition of vehicle targets and roadblocks is performed by clustering algorithm and structural features extraction. Since the features of some of the roadblocks are similar to those of the vehicle targets, secondary recognition is required to classify the vehicle targets and roadblocks by the target motion trend and trajectory length. The experimental results show that the average detection rate is 91.13% and the average false detection rate is 0.48% in different traffic scenarios.
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