Sleep Posture Detection Using an Accelerometer Placed on the Neck
Sleep Posture Detection Using an Accelerometer Placed on the Neck
复制标题
使用放置在颈部的加速度计检测睡眠姿势
DOI：
10.1109/embc48229.2022.9871300
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
E. Rodríguez
中科院分区：
文献类型：
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作者：
Rawan S. Abdulsadig;Sukhpreet Singh;Zaibaa Patel;E. Rodríguez
睡眠姿势监测是试图解决姿势引发的睡眠障碍的关键。许多研究已经探索了从利用最佳身体位置（诸如躯干）的专用物理感测通道的睡眠姿势检测;或者备选地非接触方法。但是，几乎没有做过尝试从身体位置检测睡眠位置的工作，虽然对于该目的来说是次优的，但是允许从其他感测模态更好地提取更关键的生物标志物，使得在某些临床应用中的多模态监测成为可能。这项工作提出了两种不同的方法，在不同程度的复杂性，用于检测4个主要的睡眠位置（仰卧，俯卧，右侧和左侧）从加速度测量数据由一个单一的可穿戴设备放置在脖子上。在这项工作中提出了一个超轻重量的基于阈值的模型，除了一个额外的树分类。基于阈值的模型能够在样本外数据上实现95%的平均准确度和0.89的F1分数，这表明使用简单的基于规则的模型可以获得适度高的分类性能。另一方面，ExtraTrees分类器能够实现99%的平均准确率和0.99的平均F1分数，仅使用25个基本估计量，最大深度为20。这两种模型在从颈戴式加速度计传感器收集信号时，都显示出高精度检测睡眠姿势的前景。
Sleep position monitoring is key when attempting to address posture triggered sleep disorders. Many studies have explored sleep posture detection from a dedicated physical sensing channel exploiting optimum body locations, such as the torso; or alternatively non-contact approaches. But, little work has been done to try to detect sleep position from a body location which, whilst being suboptimal for that purpose, does however allow for better extraction of more critical biomarkers from other sensing modalities, making possible multi-modal monitoring in certain clinical applications. This work presents two different approaches, at varying levels of complexity, for detecting 4 main sleep positions (supine, prone, lateral right and lateral left) from accelerometry data obtained by a single wearable device placed on the neck. An ultra light-weight threshold-based model is presented in this work, in addition to an Extra-Trees classifier. The threshold-based model was able to achieve 95% average accuracy and 0.89 F1-score on out-of-sample data, showing that it is possible to obtain a moderately high classification performance using a simple rule-based model. The ExtraTrees classifier, on the other hand, was able to achieve 99 % average accuracy and 0.99 average F1-score using only 25 base estimators with maximum depth of 20. Both models show promise in detecting sleep posture with high accuracy when collecting the signals from a neck-worn accelerometer sensor.