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Multi-level Hierarchical Complex Behavior Monitoring System for Dog Psychological Separation Anxiety Symptoms

Authors
Wang, HuasangAtif, OthmaneTian, JirongLee, JongukPark, DaiheeChung, Yongwha
Issue Date
2월-2022
Publisher
MDPI
Keywords
complex event processing (CEP); long short-term memory (LSTM); fuzzy logic; pattern recognition; separation anxiety; animal-computer interaction (ACI); sensor
Citation
SENSORS, v.22, no.4
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
22
Number
4
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/139386
DOI
10.3390/s22041556
ISSN
1424-8220
Abstract
An increasing number of people own dogs due to the emotional benefits they bring to their owners. However, many owners are forced to leave their dogs at home alone, increasing the risk of developing psychological disorders such as separation anxiety, typically accompanied by complex behavioral symptoms including excessive vocalization and destructive behavior. Hence, this work proposes a multi-level hierarchical early detection system for psychological Separation Anxiety (SA) symptoms detection that automatically monitors home-alone dogs starting from the most fundamental postures, followed by atomic behaviors, and then detecting separation anxiety-related complex behaviors. Stacked Long Short-Term Memory (LSTM) is utilized at the lowest level to recognize postures using time-series data from wearable sensors. Then, the recognized postures are input into a Complex Event Processing (CEP) engine that relies on knowledge rules employing fuzzy logic (Fuzzy-CEP) for atomic behaviors level and higher complex behaviors level identification. The proposed method is evaluated utilizing data collected from eight dogs recruited based on clinical inclusion criteria. The experimental results show that our system achieves approximately an F1-score of 0.86, proving its efficiency in separation anxiety symptomatic complex behavior monitoring of a home-alone dog.
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Park, Dai Hee
과학기술대학 (컴퓨터융합소프트웨어학과)
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