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首页> 外文会议>IEEE International Conference on e-Health Networking, Applications and Services >Multivariate time-series classification of sleep patterns using a hybrid deep learning architecture
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Multivariate time-series classification of sleep patterns using a hybrid deep learning architecture

机译：使用混合深度学习架构的睡眠模式的多元时间序列分类

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With the growing public interest in health today, people are rapidly increasing their use of sleep sensing devices and smartphone apps in their daily lives to check and manage their sleeping health. However, some of the current sleep monitoring services are void of technical reliability in terms of data collection and analytic methodologies. In this research, for the purpose of robust representativeness, Internet-of-things (IoT) sensors were utilized for precise and sufficient data collection and a hybrid of Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) was proposed for accurate sleep patterns classification. In addition, we explore people's sleep sequence clusters and examine differentiations between them.
机译：随着当今公众对健康日益增长的兴趣，人们在日常生活中迅速增加了对睡眠感应设备和智能手机应用程序的使用，以检查和管理他们的睡眠健康。然而，就数据收集和分析方法而言，当前的某些睡眠监测服务缺乏技术可靠性。在这项研究中，出于鲁棒的代表性的目的，物联网（IoT）传感器用于精确和足够的数据收集，并提出了深度信念网络（DBN）和长期短期记忆（LSTM）的混合体。准确的睡眠模式分类。此外，我们探索人们的睡眠序列簇，并研究它们之间的区别。

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