Algorithmic Dynamics Lab, Unit of Computational Medicine, Karolinska Institute, 171 77 Stockholm, Sweden.
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G1T7, Canada.
Sensors (Basel). 2023 Jan 26;23(3):1390. doi: 10.3390/s23031390.
Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded-scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.
人体活动识别（HAR）已经成为医疗保健领域中一个有趣的话题。该应用在健康监测、老年人护理和疾病诊断等多个领域都非常重要。考虑到智能设备的日益改进，我们日常生活中会产生大量数据。在这项工作中，我们提出了无监督、缩放的基于 Dirichlet 的隐马尔可夫模型来分析人体活动。我们的动机是人体活动具有序列模式，而隐马尔可夫模型（HMM）是用于对具有连续流的数据进行建模的最强有力的统计模型之一。在本文中，我们假设 HMM 中的发射概率遵循有界缩放的 Dirichlet 分布，这是对比例数据进行建模的合理选择。为了学习我们的模型，我们应用了变分推理方法。我们使用了一个公开的数据集来评估我们提出的模型的性能。
