Multi-resident type recognition based on ambient sensors activity

Qingjuan Li, Huangfu Wei, Fadi Farha, Tao Zhu, Shunkun Yang, Liming (Luke) Chen, Huansheng Ning

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
159 Downloads (Pure)


With the development of sensing and intelligent technologies, ambient sensor-based activity recognition is attracting more attention for a wide range of applications. One of the technology challenges is the recognition of the activity performer in a multi-occupancy scenario. This paper proposes a multi-label Markov Logic Network classification method to recognize resident types based on their activity habits and preference. The activity preference mainly includes time sequence preference, duration and period preference, and the location preference of a basic entity or action events. According to the resident type (gender, age bracket, job), the further reasoning work is the family role (mother, father, daughter and so on.) recognition. We have designed simple and combined preferences to test and evaluate our proposed method. Initial experiments have produced good performance in many cases proving this solution is an efficient and feasible method for resident type recognition which could be applied to real-world scenarios.
Original languageEnglish
Pages (from-to)108-115
Number of pages8
JournalFuture Generation Computer Systems
Early online date7 May 2020
Publication statusPublished (in print/issue) - Nov 2020


  • High-dimensional features
  • MLN
  • Multi-label of characteristics
  • Resident type


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