In-bed motion detection and classification are important techniques that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. In this article, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. To detect movements, we have designed a feature that we refer to as Log-Peak, which can be extracted from load cell data that is collected through wireless links in an energy-efficient manner. After detection, we set out to achieve a precise body motion classification. Toward this goal, we define nine classes of movements, and design a machine learning algorithm using Support Vector Machine, Random Forest, and XGBoost techniques to classify a movement into one of nine classes. For every movement, we have extracted 24 features and used them in our model. This movement detection/classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. We have applied multiple tree topologies for each technique to reach their best results. After examining various combinations, we have achieved a final classification accuracy of 91.5%. This system can be used conveniently for long-term home monitoring.

中文翻译：

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床内人体运动检测与分类系统

床上运动检测和分类是可以实现一系列应用的重要技术，其中包括睡眠监测和异常运动检测。在本文中，我们介绍了一种低成本、低开销和高度稳健的系统，用于使用低端称重传感器进行床内运动检测和分类。为了检测运动，我们设计了一种称为 Log-Peak 的功能，该功能可以从通过无线链路以节能方式收集的称重传感器数据中提取。检测后，我们着手实现精确的身体运动分类。为实现这一目标，我们定义了九类动作，并使用支持向量机、随机森林和 XGBoost 技术设计了一种机器学习算法，将动作分类为九类之一。对于每一个动作，我们提取了 24 个特征并在我们的模型中使用它们。该运动检测/分类系统是根据从 40 名受试者中收集的数据进行评估的，这些受试者在每个实验中进行了 35 次预定义的运动。我们为每种技术应用了多种树形拓扑，以达到最佳效果。在检查了各种组合之后，我们达到了 91.5% 的最终分类准确率。该系统可方便地用于长期家庭监控。