Sleep Posture Recognition With a Dual-Frequency Microwave Doppler Radar and Machine Learning Classifiers
Sleep Posture Recognition With a Dual-Frequency Microwave Doppler Radar and Machine Learning Classifiers
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DOI：
10.1109/lsens.2022.3148378
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发表时间：
2022-03-01
影响因子：
2.8
通讯作者：
Lubecke, Victor M.
中科院分区：
文献类型：
--
作者：
Islam, Shekh Md Mahmudul;Lubecke, Victor M.
这封信中提出了一种自动化、稳健、非接触式睡眠姿势识别技术，该技术使用可优化（贝叶斯超参数调整）机器学习（ML）分类器，应用于双频（2.4 GHz、5.8 GHz）单基地连续波雷达测量的有效雷达横截面和胸部位移。该技术被证明可以准确识别 20 名参与者的三种不同的关键睡眠姿势类别，与之前发表的涉及自定义 ML 模型或基于阈值的评估的研究相比，具有更高的准确性和计算效率。我们评估了三种 ML 分类器（K 最近邻、支持向量机 (SVM) 和决策树），其中使用二次核的 SVM 在 2.4 和 5.8 GHz 下的准确度分别达到 85% 和 80%，决策树分类器在 2 分钟内识别睡眠姿势，双频组合测量的准确度为 98.4%。
An automated, robust, noncontact sleep posture recognition technique is proposed in this letter, which uses optimizable (Bayesian hyperparameter tuning) machine learning (ML) classifiers applied to dual-frequency (2.4 GHz, 5.8 GHz) monostatic continuous-wave radar-measured effective radar cross section and chest displacement. The technique is demonstrated to accurately recognize three different key sleep postures categories for 20 participants, with greater accuracy and computational efficiency than prior published research involving either a custom ML model or threshold-based assessment. Three ML classifiers (K-nearest neighbor, support vector machine (SVM), and decision tree) were assessed, with an SVM using a quadratic kernel achieving an accuracy of 85 and 80%, at 2.4 and 5.8 GHz, respectively, and the decision tree classifier recognizing sleep postures in less than 2 min with 98.4% accuracy for dual-frequency combined measurements.