论文标题
人类活动识别的有效数据插补技术
An Efficient Data Imputation Technique for Human Activity Recognition
论文作者
论文摘要
人类活动识别的巨大应用正在从健康监测系统到虚拟现实应用程序飙升。因此，对日常生活活动的自动识别对于众多应用已变得重要。近年来，已经提出了许多数据集来培训机器学习模型，以有效监控和识别人类日常生活活动。但是，当数据集中有不完整的活动，即在数据集捕获中缺少样本时，机器学习模型在活动识别中的性能会受到至关重要的影响。因此，在这项工作中，我们提出了一种推断数据集缺失样本的方法，以更好地认识人类的日常生活活动。所提出的方法有效地预处理数据捕获并利用K-Nearest邻居（KNN）插入技术来推断数据集捕获中缺失的样品。所提出的方法优雅地推断了与实际数据集中的类似活动模式。
The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.
