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Author

Paula García

Bio: Paula García is an academic researcher from ICESI University. The author has contributed to research in topics: Training system. The author has co-authored 1 publications.

Papers
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Book ChapterDOI
26 Jul 2019
TL;DR: Preliminary results support the idea that technology-enhanced training is a feasible alternative to motivate and guide owners to implement separation training with their dogs.
Abstract: Separation anxiety in dogs is a common condition that is manifested by destructive behavior when dogs are left alone. The most successful treatment for canine separation-related problems requires dog’s behavior modification via a time consuming training. Moreover, this type of training needs a high commitment from the dog’s owner. Here, a canine wearable interface connected to a mobile application was designed to monitor and guide a training program aiming at behavior modification in dogs. The objective was to design a system that enhances user engagement while monitoring dog’s biometrical signals. Preliminary testing of the system revealed significant behavior changes. Significant decrease in dog’s overall destructive behavior was recorded. Specifically, when using the technology-enhanced vest, dogs were quieter and reduced their anxious movements. These preliminary results support the idea that technology-enhanced training is a feasible alternative to motivate and guide owners to implement separation training with their dogs.

1 citations


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Journal ArticleDOI
01 Feb 2022-Sensors
TL;DR: 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 is proposed in this paper .
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.

6 citations