HyperECG: ECG Signal Inference From Radar With Hyperdimensional Computing

Contactless ECG monitoring with radar technology is used in both long-term and remote healthcare monitoring. While first attempts were able to only estimate heart rate, more recently deep learning (DL) has been used to infer the continuous ECG signal, which is essential for many health monitoring applications. However, the compute-intensive nature of DL models makes it hard to deploy and personalize them in low-power systems that are critical for remote healthcare monitoring. To address this challenge, we introduce HyperECG, a pioneering approach based on Hyperdimensional Computing (HDC), an efficient alternative machine learning method, to infer ECG signals from radar inputs. We combine a novel learnable HDC projection encoding with state-of-the-art HDC regressors to achieve high-quality ECG estimation. Experimental results reveal that HyperECG achieves output quality comparable to the state-of-the-art DL while reducing inference and training runtime up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$23 \times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$36 \times$</tex>, respectively. HyperECG supports on-device model personalization, crucial in medical settings, with accuracy improvements of up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$68 \%$</tex> on patient-specific evaluations, compared to before fine-tuning HyperECG.