Noncontact vital signs detection using joint wavelet analysis and autocorrelation computation
-
摘要: 采用調頻連續波（Frequency modulated continuous wave, FMCW）雷達實現非接觸式生理信號檢測，并提出了基于小波分析和自相關計算（Wavelet analysis and autocorrelation computation, WAAC）的檢測方法。首先，毫米波FMCW雷達發射電磁波信號，并接收來自身體的反射信號。然后，通過信號預處理從中頻信號中提取包含呼吸和心跳的相位信息，消除直流偏置并完成相位解纏。最后，基于小波包分解（Wavelet packet decomposition, WPD）從原始信號中得到心跳和呼吸信號，利用自相關計算減小雜波對心跳信號的影響，進而提取高精度的心率參數。應用FMCW雷達對10名受試者進行實驗測試，結果表明本文方法得到的呼吸和心率的平均絕對誤差率平均值分別小于1.65%和1.83%。Abstract: Vital signs are important parameters for human health status assessment, and timely, accurate detection is of great significance for modern health care and intelligent medical applications. Detecting vital signs, such as heartbeat and respiration signals, provides a variety of diseases with reliable diagnosis and effective prevention. Conventional contact detection may restrict the behaviors of users, cause additional burdens, and render users uncomfortable. In recent years, noncontact detection technology has successfully achieved remote long-term detection for respiration and heartbeat signals. Compared to conventional contact-detection approaches, noncontact heartbeat and respiration detection using a millimeter-wave radar is preferable as it causes no disturbance to the subject, bringing a comfortable experience, and detects vital signs under natural conditions. However, noncontact vital signs detection is challenging owing to environmental noise. Especially, heartbeat signals are very weak and are merged with respiration harmonics and environmental noise, and their extraction and recognition are even more difficult. This paper applied a frequency-modulated continuous wave (FMCW) radar to detect vital signs. The study also presented a noncontact heartbeat and respiration signals detection approach based on wavelet analysis and autocorrelation computation (WAAC). The millimeter-wave FMCW radar first transmited the electromagnetic signal and received the reflected echo signals from the human body. Thereafter, the phase information of the intermediate frequency signals was extracted, which included respiration and heartbeat signals. The direct current offset of the phase information was corrected, and the phase was unwrapped. Finally, the wavelet packet decomposition was used to reconstruct heartbeat and respiration signals from the original signal, and an autocorrelation computation was utilized to reduce the effect of clutters on the heart rate detection. Experiments were conducted on ten subjects. Results show that the average absolute error percentage of WAAC is less than 1.65% and 1.83% for respiration and heartbeat rates, respectively.
-
圖 1 基于FMCW雷達的非接觸式生理信號檢測模型
Figure 1. Noncontact vital signs detection model based on FMCW radar
圖 2 基于FMCW雷達的非接觸式生理信號檢測方法流程圖
Figure 2. Noncontact vital signs detection processing procedure based on FMCW radar
圖 5 基于FMCW雷達的非接觸式生理信號檢測實驗場景
Figure 5. Scenario of noncontact vital signs detection based on FMCW radar.
圖 6 雷達相位信號（a）以及雷達相位信號頻譜（b）
Figure 6. Radar phase signal (a) and radar phase frequency spectrum (b)
圖 7 雷達和參考傳感器的呼吸和心跳信號比較。（a）呼吸信號；（b）心跳信號
Figure 7. Time domain respiration and heartbeat signals from the radar system and reference sensor: (a) respiration signal; (b) heartbeat signal
圖 8 雷達和參考信號的呼吸速率及心跳速率。（a）呼吸速率；（b）心跳速率
Figure 8. Instantaneous BR and HR from the radar system and reference signal: (a) instantaneous BR; (b) instantaneous HR
表 1 雷達參數配置
Table 1. Radar configuration parameters
fmin/GHz Td/μs S/(MHz·s?1) B/MHz fslow/Hz ffast/MHz 76.4 48 20 960 20 3.2 
下載: 導出CSV
表 2 10名受試者在不同距離生理特征速率測量平均絕對誤差率
Table 2. AAEP of radar instantaneous vital sign rates detection from ten subjects at six different distances
Subject Gender Height/cm Weight/kg HR AAEP/% BR AAEP/% 0.5 m 1.0 m 1.5 m 2.0 m 2.5 m 3.0 m 0.5 m 1.0 m 1.5 m 2.0 m 2.5 m 3.0 m 1 Male 178 90 0.87 1.08 0.99 1.59 2.80 3.93 0.45 1.38 1.03 2.84 2.77 3.11 2 Male 176 85 0.69 3.02 1.47 2.98 3.60 4.14 0.84 0.63 2.19 3.12 4.61 3.50 3 Male 171 65 2.01 1.69 2.94 3.26 3.73 4.82 1.44 1.55 2.21 3.28 1.65 4.35 4 Male 173 68 1.92 3.45 4.78 4.90 5.76 7.74 3.23 1.10 4.06 2.45 3.96 4.06 5 Male 183 82 1.70 3.86 3.97 4.05 4.14 5.64 2.25 3.63 1.76 3.48 3.50 5.62 6 Male 171 73 2.07 2.98 3.77 4.72 4.90 6.58 0.96 1.53 2.64 2.67 2.45 4.81 7 Female 170 50 2.20 4.24 4.27 4.35 6.66 7.05 1.76 2.68 2.52 3.73 3.77 5.90 8 Female 173 53 2.33 3.95 3.94 4.91 6.80 9.66 1.09 2.39 2.28 2.32 4.82 6.17 9 Female 176 65 2.50 3.08 4.62 5.22 6.36 7.93 1.62 1.94 2.33 2.20 2.12 2.34 10 Female 163 48 2.05 3.02 3.33 3.68 5.35 7.28 2.86 1.68 2.13 2.94 4.77 5.00 Average 1.83 3.03 3.41 3.97 5.01 6.48 1.65 1.85 2.32 2.90 3.44 4.49
下載: 導出CSV
中文字幕在线观看表 3 10名受試者在不同距離生理特征速率測量平均絕對誤差
Table 3. AAE of radar instantaneous vital sign rates detection from ten subjects at six different distances
Subject Gender Height/cm Weight/kg HR AAE (bpm) BR AAE (bpm) 0.5 m 1.0 m 1.5 m 2.0 m 2.5 m 3.0 m 0.5 m 1.0 m 1.5 m 2.0 m 2.5 m 3.0 m 1 Male 178 90 0.66 0.73 0.62 0.96 1.80 2.45 0.08 0.22 0.15 0.41 0.38 0.46 2 Male 176 85 0.54 2.34 1.14 1.76 2.27 2.45 0.11 0.10 0.33 0.43 0.57 0.51 3 Male 171 65 1.64 1.37 2.16 1.98 2.39 3.10 0.29 0.29 0.38 0.48 0.25 0.63 4 Male 173 68 1.25 2.32 2.96 4.65 5.62 7.74 0.44 0.15 0.26 0.63 1.02 1.17 5 Male 183 82 1.16 2.62 2.60 2.47 2.45 3.28 0.30 0.51 0.21 0.58 0.51 0.91 6 Male 171 73 1.38 1.98 2.50 2.91 4.65 3.77 0.13 0.19 0.33 0.32 0.63 0.70 7 Female 170 50 1.56 3.83 3.63 4.03 6.30 6.96 0.26 0.29 0.38 0.92 0.97 1.50 8 Female 173 53 2.18 2.32 2.39 4.63 6.54 10.4 0.20 0.42 0.38 0.58 1.12 1.68 9 Female 176 65 1.74 2.22 3.40 5.41 6.59 7.65 0.26 0.31 0.31 0.63 0.30 0.55 10 Female 163 48 1.74 1.96 2.19 3.50 5.25 7.12 0.37 0.25 0.32 0.79 1.15 1.42 Average 1.39 2.17 2.36 3.23 4.39 5.49 0.24 0.27 0.30 0.58 0.69 0.93
下載: 導出CSV
-
參考文獻
[1] Loon K, Breteler M J M, Wolfwinkel L, et al. Wireless non-invasive continuous respiratory monitoring with FMCW radar: A clinical validation study. J Clin Monit Comput, 2016, 30(6): 797 doi: 10.1007/s10877-015-9777-5 [2] Kundu S K, Kumagai S, Sasaki M. A wearable capacitive sensor for monitoring human respiratory rate. Jpn J Appl Phys, 2013, 52(4S): 04CL05 doi: 10.7567/JJAP.52.04CL05 [3] Sevindir H K, ?et$ {\rm{\dot u}} $nkaya S, ?ayli ?. Wavelet transform based noise removal from ECG signal for accurate heart rate detection using ECG // 2015 Medical Technologies National Conference (TIPTEKNO). Bodrum, 2015: 1 [4] Madhav K V, Ram M R, Krishna E H, et al. Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition // 2011 IEEE International Instrumentation and Measurement Technology Conference. Hangzhou, 2011: 1 [5] Xiao S L, Yang P F, Liu L Y, et al. Extraction of respiratory signals and respiratory rates from the photoplethysmogram // 15th EAI International Conference, BODYNETS 2020. Tallinn, 2020: 184 [6] Zhao H, Hong H, Sun L, et al. Noncontact physiological dynamics detection using low-power digital-IF Doppler radar. IEEE Trans Instrum Meas, 2017, 66(7): 1780 doi: 10.1109/TIM.2017.2669699 [7] Hu W, Zhao Z Y, Wang Y F, et al. Noncontact accurate measurement of cardiopulmonary activity using a compact quadrature Doppler radar sensor. IEEE Trans Biomed Eng, 2014, 61(3): 725 doi: 10.1109/TBME.2013.2288319 [8] Sachs J, Helbig M, Herrmann R, et al. Remote vital sign detection for rescue, security, and medical care by ultra-wideband pseudo-noise radar. Ad Hoc Networks, 2014, 13: 42 doi: 10.1016/j.adhoc.2012.07.002 [9] Xu Y K, Shi P, Yu H L. Progress on human physiological parameter detection based on imaging PPG. Beijing Biomed Eng, 2017, 36(6): 648 doi: 10.3969/j.issn.1002-3208.2017.06.016許彥坤, 石萍, 喻洪流. 基于成像式光電容積描記技術的人體生理參數檢測研究進展. 北京生物醫學工程, 2017, 36(6):648 doi: 10.3969/j.issn.1002-3208.2017.06.016 [10] Takano C, Ohta Y. Heart rate measurement based on a time-lapse image. Med Eng Phys, 2007, 29(8): 853 doi: 10.1016/j.medengphy.2006.09.006 [11] Qin R X, Chen Z X. Non-contact stable heart rate measurement algorithm under face motion conditions. Opt Tech, 2021, 47(1): 87秦睿星, 陳兆學. 人臉運動狀態下的非接觸式心率穩定測量算法. 光學技術, 2021, 47(1):87 [12] Ghanadian H, Ghodratigohar M, Osman H A. A machine learning method to improve non-contact heart rate monitoring using an RGB camera. IEEE Access, 2018, 6: 57085 doi: 10.1109/ACCESS.2018.2872756 [13] Hanawa D, Inou H, Mishima S, et al. Basic study on noncontact sensing of flow velocity in nasal breathing by using far infrared optical imaging // 2020 Opto-Electronics and Communications Conference (OECC). Taipei, 2020: 1 [14] Wang F K, Horng T S, Peng K C, et al. Single-antenna Doppler radars using self and mutual injection locking for vital sign detection with random body movement cancellation. IEEE Trans Microw Theory Tech, 2011, 59(12): 3577 doi: 10.1109/TMTT.2011.2171712 [15] Li C Z, Lin J. Random body movement cancellation in Doppler radar vital sign detection. IEEE Trans Microw Theory Tech, 2008, 56(12): 3143 doi: 10.1109/TMTT.2008.2007139 [16] Liang X L, Zhang H, Ye S B, et al. Improved denoising method for through-wall vital sign detection using UWB impulse radar. Digit Signal Process, 2018, 74: 72 doi: 10.1016/j.dsp.2017.12.004 [17] Ren L Y, Koo Y S, Wang H F, et al. Noncontact multiple heartbeats detection and subject localization using UWB impulse Doppler radar. IEEE Microw Wirel Components Lett, 2015, 25(10): 690 doi: 10.1109/LMWC.2015.2463214 [18] Kim S, Lee K K. Low-complexity joint extrapolation-MUSIC-based 2-D parameter estimator for vital FMCW radar. IEEE Sensors J, 2019, 19(6): 2205 doi: 10.1109/JSEN.2018.2877043 [19] Wang G C, Gu C Z, Inoue T, et al. A hybrid FMCW-interferometry radar for indoor precise positioning and versatile life activity monitoring. IEEE Trans Microw Theory Tech, 2014, 62(11): 2812 doi: 10.1109/TMTT.2014.2358572 [20] Li C Z, Lubecke V M, Boric-Lubecke O, et al. A review on recent advances in Doppler radar sensors for noncontact healthcare monitoring. IEEE Trans Microw Theory Tech, 2013, 61(5): 2046 doi: 10.1109/TMTT.2013.2256924 [21] Yan J M, Hong H, Zhao H, et al. Through-wall multiple targets vital signs tracking based on VMD algorithm. Sensors, 2016, 16(8): 1293 doi: 10.3390/s16081293 [22] Mu?oz-Ferreras J M, Wang J, Peng Z Y, et al. From Doppler to FMCW radars for non-contact vital-sign monitoring. // 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC). Gran Canaria, 2018: 1 [23] Zhang D, Kurata M, Inaba T. FMCW radar for small displacement detection of vital signal using projection matrix method. Int J Antennas Propag, 2013, 2013: 1 [24] Wang Y, Wang W, Zhou M, et al. Remote monitoring of human vital signs based on 77-GHz mm-wave FMCW radar. Sensors, 2020, 20(10): 2999 doi: 10.3390/s20102999 [25] Hu W, Zhang H Y, Zhao Z Y, et al. Real-time remote vital sign detection using a portable Doppler sensor system // 2014 IEEE Sensors Applications Symposium (SAS). Queenstown, 2014: 89 [26] Zhangi T, Valerio G, Sarrazin J, et al. Wavelet-based analysis of 60 GHz Doppler radar for non-stationary vital sign monitoring // 2017 11th European Conference on Antennas and Propagation (EUCAP). Paris, 2017: 1876 [27] Li M Y, Lin J. Wavelet-transform-based data-length-variation technique for fast heart rate detection using 5.8-GHz CW Doppler radar. IEEE Trans Microw Theory Tech, 2018, 66(1): 568 doi: 10.1109/TMTT.2017.2730182 [28] Kim J Y, Park J H, Jang S Y, et al. Peak detection algorithm for vital sign detection using Doppler radar sensors. Sensors, 2019, 19(7): 1575 doi: 10.3390/s19071575 [29] Alizadeh M, Shaker G, Almeida J C M D, et al. Remote monitoring of human vital signs using mm-wave FMCW radar. IEEE Access, 2019, 7: 54958 doi: 10.1109/ACCESS.2019.2912956 [30] Sun L, Li Y S, Hong H, et al. Super-resolution spectral estimation in short-time non-contact vital sign measurement. Rev Sci Instruments, 2015, 86(4): 044708 doi: 10.1063/1.4916954 [31] Zhang Z L, Pi Z Y, Liu B Y. TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Trans Biomed Eng, 2015, 62(2): 522 doi: 10.1109/TBME.2014.2359372 -
點擊查看大圖
圖(8) / 表(3)
計量
- 文章訪問數: 1075
- HTML全文瀏覽量: 570
- PDF下載量: 141
- 被引次數: 0
