An incremental learning mechanism for human activity recognition

S. Ntalampiras, M. Roveri
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引用次数: 13

Abstract

This paper proposes an incremental mechanism for the automatic recognition of physical activities performed by humans. The specific research field has become quite relevant as it may offer important information to areas such as ambient intelligence, pervasive computing, and assistive technologies. The works in the related literature so far assume the a-priori availability of the dictionary of activities to be recognised. This work is focused on relaxing that assumption by learning and recognizing the human activities in an incremental manner based on the acquired datastreams. To this end, we designed a learning mechanism based on hidden Markov models for recognising human activities among those of a dictionary. The major novelty of the proposed mechanism is its ability to detect the occurrence of new activities and update the dictionary accordingly. We conducted experiments on a publicly available dataset of six human activities, i.e. walking, walking upstairs, walking downstairs, sitting, standing, and laying, where the efficiency of the proposed algorithm is demonstrated.
人类活动识别的增量学习机制
本文提出了一种自动识别人类身体活动的增量机制。这个特定的研究领域已经变得非常相关,因为它可以为环境智能、普适计算和辅助技术等领域提供重要的信息。到目前为止,相关文献中的作品假设要识别活动字典的先验可用性。这项工作的重点是通过基于获取的数据流以增量的方式学习和识别人类活动来放松这一假设。为此,我们设计了一种基于隐马尔可夫模型的学习机制,用于从字典中识别人类活动。所提出的机制的主要新颖之处在于它能够检测新活动的发生并相应地更新字典。我们在公开的六种人类活动数据集上进行了实验,即步行,上楼,下楼,坐着,站着和躺着,其中证明了所提出算法的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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