Multi-Task Learning Radar Transformer (MLRT): A Personal Identification and Fall Detection Network Based on IR-UWB Radar.

Radar-based personal identification and fall detection have received considerable attention in smart healthcare scenarios. Deep learning algorithms have been introduced to improve the performance of non-contact radar sensing applications. However, the original Transformer network is not suitable for multi-task radar-based applications to effectively extract temporal features from time-series radar signals. This article proposes the Multi-task Learning Radar Transformer (MLRT): a personal Identification and fall detection network based on IR-UWB radar. The proposed MLRT utilizes the attention mechanism of Transformer as its core to automatically extract features for personal identification and fall detection from radar time-series signals. Multi-task learning is applied to exploit the correlation between the personal identification task and the fall detection task, enhancing the performance of discrimination for both tasks. In order to suppress the impact of noise and interference, a signal processing approach is employed including DC removal and bandpass filtering, followed by clutter suppression using a RA method and Kalman filter-based trajectory estimation. An indoor radar signal dataset is generated with 11 persons under one IR-UWB radar, and the performance of MLRT is evaluated using this dataset. The measurement results show that the accuracy of MLRT improves by 8.5% and 3.6% for personal identification and fall detection, respectively, compared to state-of-the-art algorithms. The indoor radar signal dataset and the proposed MLRT source code are publicly available.

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring.

Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.

[Non-contact Blood Pressure Measurement Method Using Frequency Modulated Continuous Wave Radar].

OBJECTIVE: Non-contact continuous blood pressure monitoring is significant in vital sign monitoring. Frequency modulated continuous wave (FMCW) radar is suitable for non-contact wave signal extraction. A heartbeat-guided blood pressure monitoring algorithm using FMCW radar is proposed. METHODS: The target heart rate is detected and pulse wave signal is extracted based on FMCW reflected signals. The variational mode decomposition (VMD) is introduced to alleviate the interferences of human breath and slight body movements. The pulse wave signal is extracted based on target heart rate. Blood pressure related features of pure pulse waveform are extracted to obtain blood pressure based on its estimation model. RESULTS: Experiments are conducted indoor among 15 participants sitting in a nature state. The average accuracy of diastolic blood pressure (DBP) is 94.3% and that of systolic blood pressure is 94.4%. CONCLUSIONS: The experimental results demonstrate the robustness and effectiveness of the proposed algorithm, which makes it possible to further achieve long-term real-time non-contact blood pressure monitoring.