Beamforming Feedback-Based Respiration and Heart Rate Estimation Toward Firmware-Agnostic WiFi Sensing

WiFi-based vital sign monitoring has attracted growing attention for its potential applications in contactless healthcare. However, most existing techniques rely on channel state information (CSI), which typically requires custom firmware and specific chipsets. To address this issue, this study explores firmware-agnostic respiration and heart rate estimation using beamforming feedback (BFF), compressed representation of CSI. This eliminates the need for custom firmware or chipset support, enabling broader applicability using off-the-shelf devices. However, it is not trivial to apply CSI-based estimation techniques to BFF-based estimation because the information content and data structure of BFF differ from those of CSI. The proposed BFF-based estimation algorithm addresses this issue by adapting the CSI-based estimation techniques to work with BFF. The algorithm consists of four key components: subcarrier selection, data calibration, signal extraction, and respiration and heart rate estimation. The performance of the BFF-based estimation algorithm is experimentally validated in several indoor environments using commodity IEEE 802.11ac devices. Results show that respiration rate and heart rate can be estimated with average errors below 1 breaths/min and 10 beats/min, respectively. Furthermore, accuracy comparisons between BFF-based and CSI-based estimations are provided to investigate the impact of lossy compression from CSI to BFF, specifically singular value decomposition (SVD) calculation and quantization. Comparisons reveal that the accuracy degradation of the BFF-based estimation compared to CSI-based estimation is primarily caused by the quantization rather than the SVD calculation.