UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912490810507264 |
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| author | Gruzewska, Elzbieta Rao, Pooja Baur, Sebastien Baugh, Matthew Bellaiche, Mathias M. J. Srinivas, Sharanya Ponce, Octavio Thompson, Matthew Rudrapatna, Pramod Sanchez, Michael A. Cai, Lawrence Z. Chico, Timothy JA Storey, Robert F. Maz, Emily Telang, Umesh Shetty, Shravya Daswani, Mayank |
| author_facet | Gruzewska, Elzbieta Rao, Pooja Baur, Sebastien Baugh, Matthew Bellaiche, Mathias M. J. Srinivas, Sharanya Ponce, Octavio Thompson, Matthew Rudrapatna, Pramod Sanchez, Michael A. Cai, Lawrence Z. Chico, Timothy JA Storey, Robert F. Maz, Emily Telang, Umesh Shetty, Shravya Daswani, Mayank |
| contents | Radar technology presents untapped potential for continuous, contactless, and passive heart rate monitoring via consumer electronics like mobile phones. However the variety of available radar systems and lack of standardization means that a large new paired dataset collection is required for each radar system. This study demonstrates transfer learning between frequency-modulated continuous wave (FMCW) and impulse-radio ultra-wideband (IR-UWB) radar systems, both increasingly integrated into consumer devices. FMCW radar utilizes a continuous chirp, while IR-UWB radar employs short pulses. Our mm-wave FMCW radar operated at 60 GHz with a 5.5 GHz bandwidth (2.7 cm resolution, 3 receiving antennas [Rx]), and our IR-UWB radar at 8 GHz with a 500 MHz bandwidth (30 cm resolution, 2 Rx). Using a novel 2D+1D ResNet architecture we achieved a mean absolute error (MAE) of 0.85 bpm and a mean absolute percentage error (MAPE) of 1.42% for heart rate monitoring with FMCW radar (N=119 participants, an average of 8 hours per participant). This model maintained performance (under 5 MAE/10% MAPE) across various body positions and heart rate ranges, with a 98.9% recall. We then fine-tuned a variant of this model, trained on single-antenna and single-range bin FMCW data, using a small (N=376, avg 6 minutes per participant) IR-UWB dataset. This transfer learning approach yielded a model with MAE 4.1 bpm and MAPE 6.3% (97.5% recall), a 25% MAE reduction over the IR-UWB baseline. This demonstration of transfer learning between radar systems for heart rate monitoring has the potential to accelerate its introduction into existing consumer devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach Gruzewska, Elzbieta Rao, Pooja Baur, Sebastien Baugh, Matthew Bellaiche, Mathias M. J. Srinivas, Sharanya Ponce, Octavio Thompson, Matthew Rudrapatna, Pramod Sanchez, Michael A. Cai, Lawrence Z. Chico, Timothy JA Storey, Robert F. Maz, Emily Telang, Umesh Shetty, Shravya Daswani, Mayank Signal Processing Artificial Intelligence Machine Learning Radar technology presents untapped potential for continuous, contactless, and passive heart rate monitoring via consumer electronics like mobile phones. However the variety of available radar systems and lack of standardization means that a large new paired dataset collection is required for each radar system. This study demonstrates transfer learning between frequency-modulated continuous wave (FMCW) and impulse-radio ultra-wideband (IR-UWB) radar systems, both increasingly integrated into consumer devices. FMCW radar utilizes a continuous chirp, while IR-UWB radar employs short pulses. Our mm-wave FMCW radar operated at 60 GHz with a 5.5 GHz bandwidth (2.7 cm resolution, 3 receiving antennas [Rx]), and our IR-UWB radar at 8 GHz with a 500 MHz bandwidth (30 cm resolution, 2 Rx). Using a novel 2D+1D ResNet architecture we achieved a mean absolute error (MAE) of 0.85 bpm and a mean absolute percentage error (MAPE) of 1.42% for heart rate monitoring with FMCW radar (N=119 participants, an average of 8 hours per participant). This model maintained performance (under 5 MAE/10% MAPE) across various body positions and heart rate ranges, with a 98.9% recall. We then fine-tuned a variant of this model, trained on single-antenna and single-range bin FMCW data, using a small (N=376, avg 6 minutes per participant) IR-UWB dataset. This transfer learning approach yielded a model with MAE 4.1 bpm and MAPE 6.3% (97.5% recall), a 25% MAE reduction over the IR-UWB baseline. This demonstration of transfer learning between radar systems for heart rate monitoring has the potential to accelerate its introduction into existing consumer devices. |
| title | UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.14195 |