MiTAR:a study on human activity recognition based on NLP with microscopic perspective  

作　　者：Huichao MEN Botao WANG Gang WU 

机构地区：[1]School of Computer Science and Engineering,Northeastern University,Shenyang 110000,China [2]State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210000,China

出　　处：《Frontiers of Computer Science》2021年第5期93-110,共18页中国计算机科学前沿（英文版）

基　　金：the National Key R&D Program of China(2019YFB1405302);the NSFC(Grant No.61872072);the State Key Laboratory of Computer Software New Technology Open Project Fund(KFKT2018B05).

摘　　要：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,TFIDF.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.

关 键 词：human activity recognition NLP time series data analysis data symbolization 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]