IoT applications for monitoring companion animals: A systematic literature review

Lima, Roberto Arruda de Souza

doi:10.1109/iit50501.2020.9299045

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…In the category of pet animals, a literature review which analyzes the different technologies used to monitor various target features, such as location, health, behavior, etc. can be found in [19]. In the domain of DAR, these results can aid us towards a better interpretation of the everyday routine of the animals and their needs, which in turn can directly benefit the interaction with their handlers or can be exploited to perceive the behavior SaR units, providing valuable information to their trainers (e.g., victim discovery).…”

Section: Activity Recognitionmentioning

confidence: 95%

Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

Kasnesis

Doulgerakis

Uzunidis

et al. 2022

Sensors

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Search and Rescue (SaR) dogs are important assets in the hands of first responders, as they have the ability to locate the victim even in cases where the vision and or the sound is limited, due to their inherent talents in olfactory and auditory senses. In this work, we propose a deep-learning-assisted implementation incorporating a wearable device, a base station, a mobile application, and a cloud-based infrastructure that can first monitor in real-time the activity, the audio signals, and the location of a SaR dog, and second, recognize and alert the rescuing team whenever the SaR dog spots a victim. For this purpose, we employed deep Convolutional Neural Networks (CNN) both for the activity recognition and the sound classification, which are trained using data from inertial sensors, such as 3-axial accelerometer and gyroscope and from the wearable’s microphone, respectively. The developed deep learning models were deployed on the wearable device, while the overall proposed implementation was validated in two discrete search and rescue scenarios, managing to successfully spot the victim (i.e., obtained F1-score more than 99%) and inform the rescue team in real-time for both scenarios.

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Section: Activity Recognitionmentioning

confidence: 95%

Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

Kasnesis

Doulgerakis

Uzunidis

et al. 2022

Sensors

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“…Haug et al [20] Used The study used a simple algorithm for lameness detection and did not use advanced machine learning techniques to improve the accuracy of the lameness detection, which could be a limitation in terms of the performance of the system. IoT systems used MQTT which has higher computational complexities.…”

Section: Temperature and Humiditymentioning

confidence: 99%

SCHeMoS -- Smart Cow Health Monitoring System: An IoT based Cow Hoof Detection and Healthcare Alert System by Using LSTM Network

G.R.

2024

SCPE

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Human life and existence are intertwined with a few domestic animals. One of the most important animals of this kind is the cow. Cows play a vital role in daily activities. Most of the people in India consume cow’s milk as one of their major nutrients. Monitoring the health of a cow’s everyday life is quite challenging. After infertility and mastitis, lameness is typically ranked as the third most economically significant health issue in dairy herds.Lameness are caused due to genetics, lack in nutrition i.e. a diet deficient in essential nutrients such as biotin, which can lead to hoof problems. Due to geographical environments like cows kept in wet, muddy conditions are more likely to develop hoof problems. This investigation analyses the typical characteristics of cow behavior, and a Smart Cow Health Monitoring System (ScHeMoS) using IoT is proposed to identify the cow’s health through the data obtained from Internet of Things (IoT) sensors, including position, body temperature, stability, acceleration, and animal feed. IoT is combined with Deep learning (DL) technique to monitor and diagnose animal health. We used the Long Short Term Memory (LSTM) network to predict cow lameness by capturing the body temperature and other parameters, which will aid in predicting their illness. The accelerometer values are stored so that it will further help to determine which cow is lame and which is pregnant or regular and could be intimated to the care takers in the farms. We utilised a self collected dataset to perform the investigation. By implementing this system, we achieved 92.45\% accuracy and 0.92 as F1 score.

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“…Over time, recapturing or spotting these banded birds provided crucial data on their movements and migrations. Similarly [8], larger animals might have been marked with paints or dyes to visually track their movements over shorter distances. However, these traditional methods had their limitations [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Animal Wearables and the Transformation of Movement Ecology Through IoT

Srinivas,

Shashikanth,

Raghuram

et al. 2023

J. Adv. Zool.

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The transformative power of the Internet of Things (IoT) has reached the realms of movement ecology, revolutionizing the ways in which we monitor and understand animal movement patterns. This paper delves into the emergence and impact of animal wearables, devices equipped with advanced sensors that collect real-time data on an animal's location, physiological status, and surrounding environment. Historically, tracking animal movement relied heavily on manual methods, such as visual observations and rudimentary tagging. With the advent of IoT, these methods have evolved to incorporate real-time monitoring, improved accuracy, and the simultaneous tracking of multiple animals. Through a series of case studies, we highlight the novel insights into the behavior of migratory birds, marine life, and terrestrial mammals provided by IoT-enhanced wearables. Despite their evident advantages, these wearables also present challenges, from data management issues to concerns about animal welfare and data privacy. Looking forward, the potential for miniaturization, integration with AI, and enhanced connectivity promises even more refined tools for movement ecology research. This paper underscores the profound influence of IoT on movement ecology, heralding a new era of informed conservation and ecological understanding.

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IoT applications for monitoring companion animals: A systematic literature review

Cited by 11 publications

References 28 publications

Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

SCHeMoS -- Smart Cow Health Monitoring System: An IoT based Cow Hoof Detection and Healthcare Alert System by Using LSTM Network

Animal Wearables and the Transformation of Movement Ecology Through IoT

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