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

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.