Edge-assisted multi-user millimeter-wave radar for non-contact blood pressure monitoring

Xu Ji; Fang Dong; Zhaowu Huang; Xiaolin Guo; Haopeng Zhu; Baijun Chen; Jun Shen; Xu Ji; Fang Dong; Zhaowu Huang; Xiaolin Guo; Haopeng Zhu; Baijun Chen; Jun Shen

doi:10.3934/aci.2025004

Applied Computing and Intelligence

2025, Volume 5, Issue 1: 57-76. doi: 10.3934/aci.2025004

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Research article

Edge-assisted multi-user millimeter-wave radar for non-contact blood pressure monitoring

1.
School of Computer Science and Engineering, Southeast University, Nanjing 211189, Jiangsu, China
2.
School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

Academic Editor: Pasi Fränti

Received: 25 December 2024 Revised: 12 February 2025 Accepted: 13 February 2025 Published: 19 February 2025

The non-contact blood pressure (BP) monitoring technology based on millimeter wave radar (mmWave) has been widely concerned for its advantages of non-invasive and real-time continuous monitoring. In recent years, studies have employed deep learning technologies to process mmWave radar, providing high-accuracy monitoring and high computing resource requirements. In this paper, we propose an edge-assisted framework for mmWave radar-based blood pressure monitoring to meet high accuracy and low latency application requirements because edge computing can provide a more powerful computing capability closer to users. However, it is non-trivial to effectively run such an edge-assisted mmWave radar-based blood pressure monitoring upon multiple users due to limited edge server resources. To solve this problem, we identify an opportunity to optimize the inference efficiency by adjusting key system parameters, such as sampling interval and input signal sequence length. This adjustment helps to reduce the inference latency and resource contention, especially in resource-constrained edge computing environments. By adaptively configuring these parameters for multiple users, we aim to strike a balance between a high accuracy and a low latency. First, we formulate the problem as an online learning problem and propose a deep reinforcement learning-based method to solve it. Finally, we implement a testbed to evaluate the performance of our method. Extensive experimental results show that our method outperforms the baselines, achieving a latency reduction of up to 70.3% and improving a reward by up to 29.7%, while maintaining an accuracy loss within 5%.
- millimeter-wave radar,
- blood pressure monitoring,
- edge computing,
- latency-sensitive,
- deep learning
Citation: Xu Ji, Fang Dong, Zhaowu Huang, Xiaolin Guo, Haopeng Zhu, Baijun Chen, Jun Shen. Edge-assisted multi-user millimeter-wave radar for non-contact blood pressure monitoring[J]. Applied Computing and Intelligence, 2025, 5(1): 57-76. doi: 10.3934/aci.2025004

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Abstract

The non-contact blood pressure (BP) monitoring technology based on millimeter wave radar (mmWave) has been widely concerned for its advantages of non-invasive and real-time continuous monitoring. In recent years, studies have employed deep learning technologies to process mmWave radar, providing high-accuracy monitoring and high computing resource requirements. In this paper, we propose an edge-assisted framework for mmWave radar-based blood pressure monitoring to meet high accuracy and low latency application requirements because edge computing can provide a more powerful computing capability closer to users. However, it is non-trivial to effectively run such an edge-assisted mmWave radar-based blood pressure monitoring upon multiple users due to limited edge server resources. To solve this problem, we identify an opportunity to optimize the inference efficiency by adjusting key system parameters, such as sampling interval and input signal sequence length. This adjustment helps to reduce the inference latency and resource contention, especially in resource-constrained edge computing environments. By adaptively configuring these parameters for multiple users, we aim to strike a balance between a high accuracy and a low latency. First, we formulate the problem as an online learning problem and propose a deep reinforcement learning-based method to solve it. Finally, we implement a testbed to evaluate the performance of our method. Extensive experimental results show that our method outperforms the baselines, achieving a latency reduction of up to 70.3% and improving a reward by up to 29.7%, while maintaining an accuracy loss within 5%.

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