TY - JOUR
T1 - Multi-resident type recognition based on ambient sensors activity
AU - Li, Qingjuan
AU - Wei, Huangfu
AU - Farha, Fadi
AU - Zhu, Tao
AU - Yang, Shunkun
AU - Chen, Liming (Luke)
AU - Ning, Huansheng
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - High-dimensional features
KW - MLN
KW - Multi-label of characteristics
KW - Resident type
UR - http://www.scopus.com/inward/record.url?scp=85084936540&partnerID=8YFLogxK
U2 - 10.1016/j.future.2020.04.039
DO - 10.1016/j.future.2020.04.039
M3 - Article
VL - 112
SP - 108
EP - 115
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
SN - 0167-739X
ER -