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

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 sympt...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-02, Vol.22 (4), p.1556
Main Authors: Wang, Huasang, Atif, Othmane, Tian, Jirong, Lee, Jonguk, Park, Daihee, Chung, Yongwha
Format: Article
Language:English
Subjects:
Accelerometers
Animal behavior
Animals
animal–computer interaction (ACI)
Anxiety
Anxiety, Separation - diagnosis
Behavior, Animal
complex event processing (CEP)
Data collection
Development and progression
Dogs
Fuzzy Logic
Knowledge
long short-term memory (LSTM)
Mental disorders
Methods
Monitoring
Monitoring systems
pattern recognition
Posture
Sensors
Separation
Separation anxiety
Separation anxiety (Child psychology)
Signs and symptoms
Wearable computers
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22041556