用于睡眠姿势监测的身体传感器流数据分析:一种自动标签方法

Poyuan Jeng, Li-Chun Wang
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引用次数: 8

摘要

睡眠是我们日常生活中最重要的活动之一。然而,很少有人真正了解他们的睡眠习惯,这会影响睡眠相关疾病,如睡眠呼吸暂停、背部问题甚至打鼾。目前大多数监测、预测和量化睡眠姿势的技术仅限于在医院使用和/或需要护理人员的干预。在本文中,我们描述了一个自动监测、预测和量化睡眠姿势的系统,即使在非医院环境中,如在家中,也可以由公众自行应用。在训练过程中采用随机森林方法来预测和量化睡眠姿势。在经过训练程序后,一个人只需要在手腕上放置一个传感器来识别人的睡眠姿势。我们使用一组测试数据进行的初步实验显示,准确率约为90%,这表明该设计在准确分析、预测和量化人类睡眠姿势方面具有广阔的前景。
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Stream data analysis of body sensors for sleep posture monitoring: An automatic labelling approach
Sleeping is one of the most important activities in our daily lives. However, very few people really understand their sleeping habits, which affect sleep-related diseases such as sleep apnea, back problems or even snoring. Most current techniques that monitor, predict and quantify sleep postures are limited to use in hospitals and/or need the intervention of caregivers. In this paper, we describe a system to automatically monitor, predict and quantify sleep postures that may be self-applied by the general public even in a non-hospital environment such as at a persons home. A Random Forest approach is adopted during training to predict and quantify sleep postures. After going through training procedures, a person needs only one sensor placed on the wrist to recognize the persons sleep postures. Our preliminary experiments using a set of testing data show about 90 percent accuracy, indicating that this design has a promising future to accurately analyze, predict and quantify human sleep postures.
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