Weighted respiration waveform reconstruction algorithm based on empirical modal decomposition

doi:10.11996/JG.j.2095-302X.2025040847

Abstract

Abstract:

Respiration rate estimation based on Wi-Fi signals has garnered significant attention from academic and industrial communities due to its non-contact advantage. However, extracting high-quality respiration waveforms to ensure accurate respiration rate estimation has been a persistent challenge for researchers. In this paper, a weighted respiration waveform reconstruction algorithm based on empirical mode decomposition (EMD), referred to as (weighted empirical mode decomposition) WEMD, was proposed to improve the accuracy and robustness of respiration rate estimation under different environments. First, subcarriers with better periodicity were selected using breathing-to-noise ratio (BNR) and I/O decompose methods, and various respiration waveforms were generated. Second, principal component analysis (PCA) was applied to calibrate respiration waveforms, and EMD was employed to decompose respiration waveforms. Finally, an adaptive weighting mechanism was designed to reconstruct them by estimating the correlation between different frequency components and the original respiratory pattern. Experimental results demonstrated that the WEMD algorithm achieved an average respiration estimation accuracy of over 94% in four indoor experimental environments. The WEMD algorithm not only effectively addressed the impact of the low-quality Wi-Fi data on personal respiration estimation, but also accurately estimated irregular respiration rates, achieving high-precision respiratory rate monitoring across various environments, with an error below 10%.

Key words: wireless sensing, channel state information, empirical mode decomposition, respiratory waveform reconstruction, respiration rate estimation

CLC Number:

TP391
TN92

GUO Linlin, YAO Min, ZHANG Wenqing, ZHANG Jia, SUN Jiande. Weighted respiration waveform reconstruction algorithm based on empirical modal decomposition[J]. Journal of Graphics, 2025, 46(4): 847-854.

Figures/Tables 15

Table 1 Related works on respiration sensing

文献	识别技术	年份
文献[1-2]	Wi-Fi	2021, 2022
文献[3]	Wi-Fi	2024
文献[4]	Wi-Fi	2024
文献[5]	Wi-Fi	2023
文献[6-7]	Wi-Fi	2022, 2024
文献[8]	Acoustic	2023
文献[9-10]	Wi-Fi	2018
文献[11-13]	RF, Wi-Fi	2015, 2017 2018
文献[14-20]	Wi-Fi	2016—2019, 2021, 2022

Fig. 1 Propagation path of indoor radio signals

Fig. 2 Framework for personal respiration estimation

Fig. 3 Raw CSI and CSI Ratio ((a) Raw CSI amplitude from the first antenna; (b) Raw CSI amplitude from the second antenna; (c) CSI Ratio: the rate and the first antenna and the second antenna)

Fig. 4 Data preprocessing ((a) Raw CSI Ratio; (b) Denoised CSI Ratio)

Fig. 5 Subcarrier selection ((a) CSI Ratio of thirty subcarriers; (b) CSI Ratio of the selected four subcarriers)

Fig. 6 Example of a single subcarrier projection ((a) Selected one subcarrier; (b) Multiple respiration pattern produced by the selected subcarrier)

Fig. 7 Combination of respiration patterns

Fig. 8 PCA calibrates respiration waveform

Fig. 9 Respiration Waveform reconstruction

Fig. 10 Respiration Waveform fusion

Fig. 11 Autocorrelation results

Fig. 12 Experimental equipment ((a) Receiver; (b) Command configuration interface; (c) Visual interface for Wi-Fi CSI)

Fig. 13 Experimental environment ((a) Corridor; (b) Meeting room; (c) Office; (d) Bedroom)

Fig. 14 Comparison of three respiration estimation methods ((a) MRC; (b) MRC-PCA; (c) WEMD)

References 26

[1]	ZHANG F, WU C S, WANG B B, et al. SMARS: sleep monitoring via ambient radio signals[J]. IEEE Transactions on Mobile Computing, 2021, 20(1): 217-231.
[2]	ZENG X L, WANG B B, WU C S, et al. Intelligent Wi-Fi based child presence detection system[C]// 2022 IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE Press, 2022: 11-15.
[3]	XIE X C, ZHANG D H, LI Y D, et al. Robust WiFi respiration sensing in the presence of interfering individual[J]. IEEE Transactions on Mobile Computing, 2024, 23(8): 8447-8462.
[4]	ZHANG Y W, HAN F Y, YANG P L, et al. Wi-Cyclops: room-scale WiFi sensing system for respiration detection based on single-antenna[J]. ACM Transactions on Sensor Networks, 2024, 20(4): 94.
[5]	LI B F, REN Y L, WANG Y C, et al. SpaceBeat: identity-aware multi-person vital signs monitoring using commodity WiFi[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2024, 8(3): 113.
[6]	ZHANG S J, ZHENG T Y, WANG H B, et al. Quantifying the physical separability of RF-based multi-person respiration monitoring via SINR[C]// The 20th ACM Conference on Embedded Networked Sensor Systems. New York: ACM, 2022: 47-60.
[7]	HU J Y, JIANG H B, ZHENG T Y, et al. M²-Fi: multi-person respiration monitoring via handheld WiFi devices[C]// 2024 IEEE Conference on Computer Communications. New York: IEEE Press, 2024: 1221-1230.
[8]	WU Y, LI F, XIE Y D, et al. SymListener: detecting respiratory symptoms via acoustic sensing in driving environments[J]. ACM Transactions on Sensor Networks, 2023, 19(1): 3.
[9]	LIU J, WANG Y, CHEN Y Y, et al. Tracking vital signs during sleep leveraging off-the-shelf WiFi[C]// The 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York: ACM, 2015: 267-276.
[10]	LIU J, CHEN Y Y, WANG Y, et al. Monitoring vital signs and postures during sleep using WiFi signals[J]. IEEE Internet of Things Journal, 2018, 5(3): 2071-2084.
[11]	YUE S C, HE H, WANG H, et al. Extracting multi-person respiration from entangled RF signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(2): 86.
[12]	ADIB F, MAO H Z, KABELAC Z, et al. Smart homes that monitor breathing and heart rate[C]// The 33rd Annual ACM Conference on Human Factors in Computing Systems. New York: ACM, 2015: 837-846.
[13]	HSU C Y, AHUJA A, YUE S C, et al. Zero-effort in-home sleep and insomnia monitoring using radio signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2017, 1(3): 59.
[14]	WU D, ZENG Y W, ZHANG F S, et al. WiFi CSI-based device-free sensing: from Fresnel zone model to CSI-ratio model[J]. CCF Transactions on Pervasive Computing and Interaction, 2022, 4(1): 88-102.
[15]	YU B H, WANG Y X, NIU K, et al. WiFi-Sleep: sleep stage monitoring using commodity Wi-Fi devices[J]. IEEE Internet of Things Journal, 2021, 8(18): 13900-13913.
[16]	WANG H, ZHANG D Q, MA J Y, et al. Human respiration detection with commodity WiFi devices: do user location and body orientation matter?[C]// 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York: ACM, 2016: 25-36.
[17]	ZENG Y W, WU D, GAO R Y, et al. FullBreathe: full human respiration detection exploiting complementarity of CSI phase and amplitude of WiFi signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3): 148.
[18]	LI X, ZHANG D Q, XIONG J, et al. Training-free human vitality monitoring using commodity Wi-Fi devices[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3): 121.
[19]	ZHANG F S, ZHANG D Q, XIONG J, et al. From Fresnel diffraction model to fine-grained human respiration sensing with commodity Wi-Fi devices[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(1): 53.
[20]	ZENG Y W, WU D, XIONG J, et al. FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019, 3(3): 121.
[21]	谢磊, 王楚豫, 宁静仪. 见微知著: 基于无线信号的微状态感知[J]. 中国计算机学会通讯, 2024, 20(3): 16-21.
	XIE L, WANG C Y, NING J Y. Micro state sensing based on wireless signal[J]. Communications of the CCF, 2024, 20(3): 16-21 (in Chinese).
[22]	卢洋, 陈林慧, 姜晓恒, 等. SDENet: 基于多尺度注意力质量感知的合成缺陷数据评价网络[J]. 图学学报, 2025, 46(1): 94-103. DOI
	LU Y, CHEN L H, JIANG Y H, et at. SDENet: a synthetic defect data evaluation network based on multi-scale attention quality perception[J]. Journal of Graphics, 2025, 46(1): 94-103 (in Chinese). DOI
[23]	HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[24]	王欣雨, 刘慧, 朱积成, 等. 基于高低频特征分解的深度多模态医学图像融合网络[J]. 图学学报, 2024, 45(1): 65-77. DOI
	WANG X Y, LIU H, ZHU J C, et al. Deep multimodal medical image fusion network based on high-low frequency feature decomposition[J]. Journal of Graphics, 2024, 45(1): 65-77 (in Chinese). DOI
[25]	KOMATY A, BOUDRAA A O, AUGIER B, et al. EMD-based filtering using similarity measure between probability density functions of IMFs[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(1): 27-34.
[26]	AYENU-PRAH A, ATTOH-OKINE N. A criterion for selecting relevant intrinsic mode functions in empirical mode decomposition[J]. Advances in Adaptive Data Analysis, 2010, 2(1): 1-24.