Using model’s temporal features and hierarchical structure for similar activity recognition

Qingjuan Li, Huansheng Ning, Liming (Luke) Chen, Lingfeng Mao

Research output: Contribution to journalArticle

Abstract

Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times.

Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
DOIs
Publication statusPublished - 26 May 2020

Keywords

  • Activity recognition
  • Hierarchical structure
  • Markov logic network
  • Similar activity

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