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Advances in Sensing-Based Animal Biomechanics
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Advances in Sensing-Based Animal Biomechanics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 8645

Special Issue Editor

Prof. Dr. Christian Peham

E-Mail Website
Guest Editor
Department for Companion Animals and Horses, University of Veterinary Medicine, Veterinärplatz 1, 1210 Vienna, Austria
Interests: equine biomechanics; motion analysis; canine biomechanics; muskolo-skeletal modelling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (3 papers)

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Research

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14 pages, 1089 KiB  
Article
Impact of Aging and Visual Input on Postural Stability in Dogs: Insights from Center-of-Pressure Analysis
by Christiane Lutonsky, Christian Peham, Nadja Affenzeller, Masoud Aghapour, Julia Wegscheider, Alexander Tichy and Barbara Bockstahler
Sensors 2025, 25(5), 1300; https://doi.org/10.3390/s25051300 - 20 Feb 2025
Viewed by 576
Abstract
This study investigates the impact of visual input and aging on postural stability (PS) in dogs by analyzing center-of-pressure (COP) parameters during static posturography under sighted (EO) and blindfolded (EC) conditions. Twenty adult (<50% of fractional lifespan) and 20 senior (>75% of fractional [...] Read more.
This study investigates the impact of visual input and aging on postural stability (PS) in dogs by analyzing center-of-pressure (COP) parameters during static posturography under sighted (EO) and blindfolded (EC) conditions. Twenty adult (<50% of fractional lifespan) and 20 senior (>75% of fractional lifespan) dogs, free from orthopedic, neurological, or visual impairments, were assessed using a pressure measurement plate. While no significant differences were found between adult and senior dogs under standard EO conditions, blindfolding revealed age-related disparities. Senior dogs exhibited significantly higher craniocaudal displacement and support surface values compared to adult dogs, indicating a greater reliance on visual input for sagittal stability. Conversely, adult dogs exhibited a reduction in postural sway during EC conditions, indicating an adaptive shift toward greater reliance on somatosensory input. These findings highlight diminished sensory integration and adaptability in senior dogs, correlating with aging-related declines in proprioception and sensory processing. This research underscores the critical role of vision in canine PS, particularly in older individuals, and emphasizes the need for targeted interventions, such as balance training, to enhance sensory integration and mitigate fall risk in aging dogs. Future studies should explore dynamic and multimodal challenges to further elucidate compensatory mechanisms. Full article
(This article belongs to the Special Issue Advances in Sensing-Based Animal Biomechanics)
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19 pages, 5999 KiB  
Article
Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data
by Md Ariful Islam Mozumder, Tagne Poupi Theodore Armand, Rashadul Islam Sumon, Shah Muhammad Imtiyaj Uddin and Hee-Cheol Kim
Sensors 2024, 24(23), 7436; https://doi.org/10.3390/s24237436 - 21 Nov 2024
Cited by 2 | Viewed by 1367
Abstract
The health, safety, and well-being of household pets such as cats has become a challenging task in previous years. To estimate a cat’s behavior, objective observations of both the frequency and variability of specific behavior traits are required, which might be difficult to [...] Read more.
The health, safety, and well-being of household pets such as cats has become a challenging task in previous years. To estimate a cat’s behavior, objective observations of both the frequency and variability of specific behavior traits are required, which might be difficult to come by in a cat’s ordinary life. There is very little research on cat activity and cat disease analysis based on real-time data. Although previous studies have made progress, several key questions still need addressing: What types of data are best suited for accurately detecting activity patterns? Where should sensors be strategically placed to ensure precise data collection, and how can the system be effectively automated for seamless operation? This study addresses these questions by pointing out whether the cat should be equipped with a sensor, and how the activity detection system can be automated. Magnetic, motion, vision, audio, and location sensors are among the sensors used in the machine learning experiment. In this study, we collect data using three types of differentiable and realistic wearable sensors, namely, an accelerometer, a gyroscope, and a magnetometer. Therefore, this study aims to employ cat activity detection techniques to combine data from acceleration, motion, and magnetic sensors, such as accelerometers, gyroscopes, and magnetometers, respectively, to recognize routine cat activity. Data collecting, data processing, data fusion, and artificial intelligence approaches are all part of the system established in this study. We focus on One-Dimensional Convolutional Neural Networks (1D-CNNs) in our research, to recognize cat activity modeling for detection and classification. Such 1D-CNNs have recently emerged as a cutting-edge approach for signal processing-based systems such as sensor-based pet and human health monitoring systems, anomaly identification in manufacturing, and in other areas. Our study culminates in the development of an automated system for robust pet (cat) activity analysis using artificial intelligence techniques, featuring a 1D-CNN-based approach. In this experimental research, the 1D-CNN approach is evaluated using training and validation sets. The approach achieved a satisfactory accuracy of 98.9% while detecting the activity useful for cat well-being. Full article
(This article belongs to the Special Issue Advances in Sensing-Based Animal Biomechanics)
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Review

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28 pages, 1069 KiB  
Review
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 13 | Viewed by 5971
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 [...] Read more.
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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