基于小波分析和自相關計算的非接觸式生理信號檢測

劉璐瑤; 張森; 肖文棟

doi:10.13374/j.issn2095-9389.2021.01.13.001

基于小波分析和自相關計算的非接觸式生理信號檢測

doi: 10.13374/j.issn2095-9389.2021.01.13.001

劉璐瑤^{1, 2},
張森^{1, 2},
肖文棟^{1, 2, 3, ,}

1.
北京科技大學自動化學院，北京 100083
2.
北京市工業波譜成像工程技術研究中心，北京 100083
3.
北京科技大學順德研究生院，廣東 528399

基金項目: 國家重點研發計劃課題資助項目（2017YFB1401203）；佛山市科技創新專項資助項目（BK20AF005）

詳細信息

通訊作者:
E-mail: wdxiao@ustb.edu.cn

中圖分類號: TP274.2
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-01-13
- 網絡出版日期: 2021-03-25
- 刊出日期: 2021-09-18

Noncontact vital signs detection using joint wavelet analysis and autocorrelation computation

LIU Lu-yao^{1, 2},
ZHANG Sen^{1, 2},
XIAO Wen-dong^{1, 2, 3
, ,}

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China
3.
Shunde Graduate School, University of Science and Technology Beijing, Guangdong 528399, China

More Information

Corresponding author: E-mail: wdxiao@ustb.edu.cn

摘要

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%。
- 非接觸式生理信號檢測 /
- 心跳檢測 /
- 呼吸檢測 /
- 小波分析 /
- 自相關計算 /
- 調頻連續波雷達
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.
- noncontact vital signs detection /
- heartbeat detection /
- respiration detection /
- wavelet analysis /
- autocorrelation computation /
- millimeter-wave FMCW radar

HTML全文

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

下載: 全尺寸圖片幻燈片

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

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圖 3 小波包分解圖

Figure 3. Wavelet packet decomposition diagram

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圖 4 自相關計算示意圖

Figure 4. Autocorrelation computation diagram

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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.

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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)

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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

下載: 全尺寸圖片幻燈片

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

下載: 全尺寸圖片幻燈片

表 1 雷達參數配置

Table 1. Radar configuration parameters

f_min/GHz	T_d/μs	S/(MHz·s^?1)	B/MHz	f_slow/Hz	f_fast/MHz
76.4	48	20	960	20	3.2

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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/%
Subject	Gender	Height/cm	Weight/kg	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

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)
Subject	Gender	Height/cm	Weight/kg	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

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