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MiTAR: a study on human activity recognition based on NLP with microscopic perspective
Frontiers of Computer Science ( IF 4.2 ) Pub Date : 2021-06-29 , DOI: 10.1007/s11704-020-9495-0
Huichao Men , Botao Wang , Gang Wu

Nowadays, human activity recognition is becoming a more and more significant topic, and there is also a wide range of applications for it in real world scenarios. Sensor data is an important data source in engineering and application. At present, some studies have been carried out in the field of human activity recognition based on sensor data in a macroscopic perspective. However, many studies in this perspective face some limitations. One pivotal limitation is uncontrollable data segment length of different kinds of activities. Suitable feature and data form are also influencing factors. This paper carries out the study creatively on a microscopic perspective with an emphasis on the logic and relevance between data segments, attempting to apply the idea of natural language processing and the method of data symbolization to the study of human activity recognition and try to solve the problem above. In this paper, several activity-element definitions and three algorithms are proposed, including the algorithm of dictionary building, the algorithm of corpus building, and activity recognition algorithm improved from a natural language analysis method, TF-IDF. Numerous experiments on different aspects of this model are taken. The experiments are carried out on six complex and representative single-level sensor datasets, namely UCI Sports and Daily dataset, Skoda dataset, WISDM Phoneacc dataset, WISDM Watchacc dataset, Healthy Older People dataset and HAPT dataset, which prove that this model can be applied to different datasets and achieve a satisfactory recognition result.



中文翻译:

MiTAR:微观视角下基于NLP的人类活动识别研究

如今,人类活动识别正成为一个越来越重要的话题,并且在现实世界场景中也有广泛的应用。传感器数据是工程应用中的重要数据源。目前,在宏观角度基于传感器数据的人体活动识别领域已经开展了一些研究。然而,从这个角度来看,许多研究都面临一些局限性。一个关键的限制是不同类型活动的数据段长度不可控。合适的特征和数据形式也是影响因素。本文创造性地从微观角度进行研究,强调数据段之间的逻辑性和相关性,试图将自然语言处理的思想和数据符号化的方法应用到人类活动识别的研究中,并试图解决上述问题。本文提出了几种活动元素定义和三种算法,包括字典构建算法、语料库构建算法和从自然语言分析方法TF-IDF改进而来的活动识别算法。对该模型的不同方面进行了大量实验。实验在六个复杂且具有代表性的单级传感器数据集上进行,即UCI Sports and Daily数据集、Skoda数据集、WISDM Phoneacc数据集、WISDM Watchacc数据集、Healthy Older People数据集和HAPT数据集​​,证明该模型是可以应用的到不同的数据集,并取得令人满意的识别结果。

更新日期:2021-06-29
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