Abstract
Hyperspectral imaging (HSI) provides multiwavelength physiological sensing for standoff biometric detection; however, ambient light fluctuations limit the robustness of conventional systems. Here we introduce a lock-in camera-based HSI framework that rapidly modulates wavelength-specific illumination and synchronizes detection, enabling robust hyperspectral video reconstruction under varying ambient conditions. In photoplethysmography validation, the system estimates heart rate with errors below 3 bpm, outperforming conventional HSI, which typically exceeds 10 bpm. Using dual-wavelength illumination (660 nm, 940 nm), we further extract blood oxygen saturation (SpO2) dynamics with a maximum error under 3% and a 2.7-fold improvement in mean accuracy under fluctuating light. We use machine learning models trained on the high-fidelity photoplethysmography signals to reconstruct blood pressure and electrocardiogram waveforms accurately. Our approach could offer a practical route for hyperspectral biosensing, advancing robust, multiparameter biometric detection for remote health assessment.
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Data availability
The PPG data cannot be made publicly available due to privacy assurances included in the informed consent signed by the study participants. Requests to access the data must be forwarded to the corresponding authors. Requests should include the name and contact details of the person requesting the data, which data and clinical variables are requested and the purpose of the data request. As real data cannot be made publicly available, synthetic data are generated by accompanying code for the purpose of reproducing the study. The spectral datasets are available via Zenodo at https://doi.org/10.5281/zenodo.17402764 (ref. 39).
Code availability
The codes that support the findings in this article are available via GitHub at https://github.com/zshao56/Lock_in_Bio.
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Acknowledgements
This work was supported by National Institutes of Health under award number 10450190. The funder had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Z.Y. and Z.S. conceived the project, with Z.Y. providing the overall supervision. Z.S. led the device design and optimization with input from G.H. A.M. and H.S. processed the PPG data and optimized the calibration. The experimental set-up and characterization were undertaken by Z.S. and G.H. The network analysis was conducted by G.H. and Q.Z. The data analysis was led by Z.S., G.H. and Z.Y., with contributions from all co-authors. The paper was principally written by Z.S. and Z.Y., with contributions from all co-authors.
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Nature Sensors thanks Takao Fuji and Suman Mandal for their contribution to the peer review of this work. Peer reviewer reports are available.
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Shao, Z., Huang, G., Mielczarek, A. et al. Robust spectral sensor for standoff biometric detection. Nat. Sens. 1, 155–162 (2026). https://doi.org/10.1038/s44460-025-00012-0
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DOI: https://doi.org/10.1038/s44460-025-00012-0
