Tremerity-Fi：利用商用毫米波雷达进行非接触式日常生活震颤严重程度评估
Tremerity-Fi: Non-Contact Daily-Life Tremor Severity Assessment by Commercial mmWave Radar
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中文总结 AI 辅助
针对神经疾病患者日常震颤评估难题，提出Tremerity-Fi系统，设计自适应波束形成算法、利用多径反射并提出无监督域适应算法，通过多场景数据集实验，在震颤检测和严重程度评估上取得高准确率，展现辅助监测潜力。
中文摘要 AI 辅助
震颤是神经疾病的常见症状，定期评估日常震颤有助于评估疾病进展并辅助临床医生优化治疗策略。然而，当前家庭监测解决方案在处理用户合作、隐私问题、环境干扰和系统通用性方面存在困难。为此，我们提出了Tremerity-Fi，一种基于毫米波雷达的非接触且注重隐私的震颤严重程度评估系统。我们首先设计了一种自适应波束形成算法，以准确识别环境中众多反射中的有用但微弱的信号。其次，利用携带目标运动有用信息的多径反射来重建手部信号并提高传感性能。此外，还提出了一种无监督域适应算法来提高适应未知环境和用户的能力。我们在办公室、家庭和医院等3种场景下收集了5名患者和25名健康受试者的多样化数据集。大量实验表明，我们的系统在震颤检测中准确率达到94.51%，比当前最优毫米波雷达方法高约5个百分点，在震颤严重程度评估中为89.13%，证明其作为神经疾病患者震颤监测助手具有足够潜力。
英文摘要
Tremor is a common symptom of neurological diseases. The regular assessment of daily tremors facilitates the evaluation of disease progression and assists clinicians in optimizing treatment strategies. However, current home monitoring solutions have difficulty in dealing with user cooperation, privacy concerns, environmental interference, and system generalization, leading to feasibility concerns in activities of daily living (ADL). To this end, we propose Tremerity-Fi, a non-contact and privacy-friendly tremor severity assessment system based on mmWave radar. To realize Tremerity-Fi, we first design an adaptive beamforming algorithm to accurately identify useful but weak signals from numerous reflections captured in the environment. Second, unlike primary reflections commonly used in mmWave sensing, we leverage multipath reflections that carry useful information about the target's motion, even though they are generally considered harmful, to help reconstruct hand signals and improve sensing performance. Furthermore, we propose an unsupervised domain adaptation algorithm to improve the ability to adapt to unseen environments and users. We collect a diverse dataset of 5 patients and 25 healthy subjects in 3 scenarios, such as offices, homes, and hospitals. Extensive experiments show that our system achieves 94.51% accuracy in tremor detection, about 5 higher than the SOTA mmWave radar method, and 89.13% in tremor severity assessment, demonstrating its sufficient potential as a tremor monitoring assistant for patients with neurological diseases.
