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Dear Colleagues,

Sensors in animal biomechanics are used for clinical applications as well as for animal monitoring in all areas. In particular, inertial measurement units (IMU) are key elements in lameness evaluation, feedback systems, and motion analysis in animal biomechanics and can be combined with EMG systems (muscle activity) and ultrasound systems to detect muscle activity and tendon strains.

High-precision detection and feedback systems of biomechanical parameters in veterinary medicine, animal sports, research, and animal farming will be part of animal lives in the near future and essential in animal welfare. This growing progress in the performance of sensors leads to a steady approach to practical needs.

This Special Issue aims to highlight advances sensing in animal biomechanics covering the development, testing, and modeling of biomechanical sensors on the component level as well as within biomechanical systems. Topics include but are not limited to:

Accelerometers;
Gyroscopes;
Force sensors (strain gauge, piezo, etc.);
Pressure sensors (capacitive, optical, piezo, strain gauge, etc.);
Fibre optic sensors;
EMG electrodes (surface, needle, array, capacitive);
Ultrasound sensors;
Ultra-wide band radar;
Gonimeters;
Optical tracking systems;
Nanomaterial-based sensors;
Advanced sensor characterization techniques;
Sensor error modeling and online calibration;
Pattern recognition algorithm;
Deep learning.

Prof. Dr. Christian Peham
Guest Editor

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Review

28 pages, 1069 KiB

Open AccessReview

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

by Carlos Alberto Aguilar-Lazcano, Ismael Edrein Espinosa-Curiel, Jorge Alberto Ríos-Martínez, Francisco Alejandro Madera-Ramírez and Humberto Pérez-Espinosa

Sensors 2023, 23(12), 5732; https://doi.org/10.3390/s23125732 - 20 Jun 2023

Cited by 2 | Viewed by 3069

Abstract

The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts. Full article

(This article belongs to the Special Issue Advances in Sensing-Based Animal Biomechanics)

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