1
|
Alexeenko V, Jeevaratnam K. Artificial intelligence: Is it wizardry, witchcraft, or a helping hand for an equine veterinarian? Equine Vet J 2023; 55:719-722. [PMID: 37551620 DOI: 10.1111/evj.13969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 06/14/2023] [Indexed: 08/09/2023]
Affiliation(s)
- Vadim Alexeenko
- School of Veterinary Medicine, University of Surrey, Surrey, UK
| | | |
Collapse
|
2
|
Crecan CM, Peștean CP. Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6301. [PMID: 37514599 PMCID: PMC10386433 DOI: 10.3390/s23146301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/20/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023]
Abstract
Objective gait analysis provides valuable information about the locomotion characteristics of sound and lame horses. Due to their high accuracy and sensitivity, inertial measurement units (IMUs) have gained popularity over objective measurement techniques such as force plates and optical motion capture (OMC) systems. IMUs are wearable sensors that measure acceleration forces and angular velocities, providing the possibility of a non-invasive and continuous monitoring of horse gait during walk, trot, or canter during field conditions. The present narrative review aimed to describe the inertial sensor technologies and summarize their role in equine gait analysis. The literature was searched using general terms related to inertial sensors and their applicability, gait analysis methods, and lameness evaluation. The efficacy and performance of IMU-based methods for the assessment of normal gait, detection of lameness, analysis of horse-rider interaction, as well as the influence of sedative drugs, are discussed and compared with force plate and OMC techniques. The collected evidence indicated that IMU-based sensor systems can monitor and quantify horse locomotion with high accuracy and precision, having comparable or superior performance to objective measurement techniques. IMUs are reliable tools for the evaluation of horse-rider interactions. The observed efficacy and performance of IMU systems in equine gait analysis warrant further research in this population, with special focus on the potential implementation of novel techniques described and validated in humans.
Collapse
Affiliation(s)
- Cristian Mihăiță Crecan
- University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
| | - Cosmin Petru Peștean
- University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 400372 Cluj-Napoca, Romania
| |
Collapse
|
3
|
Jacques J, Samardžić S. Analysing cycling sensors data through ordinal logistic regression with functional covariates. J R Stat Soc Ser C Appl Stat 2022. [DOI: 10.1111/rssc.12563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
4
|
Eerdekens A, Deruyck M, Fontaine J, Damiaans B, Martens L, De Poorter E, Govaere J, Plets D, Joseph W. Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data. Animals (Basel) 2021; 11:ani11102904. [PMID: 34679925 PMCID: PMC8532712 DOI: 10.3390/ani11102904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/25/2021] [Accepted: 10/01/2021] [Indexed: 02/07/2023] Open
Abstract
Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.
Collapse
Affiliation(s)
- Anniek Eerdekens
- WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (M.D.); (L.M.); (D.P.); (W.J.)
- Correspondence:
| | - Margot Deruyck
- WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (M.D.); (L.M.); (D.P.); (W.J.)
| | - Jaron Fontaine
- IDLab-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (J.F.); (E.D.P.)
| | - Bert Damiaans
- VETMED, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium; (B.D.); (J.G.)
| | - Luc Martens
- WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (M.D.); (L.M.); (D.P.); (W.J.)
| | - Eli De Poorter
- IDLab-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (J.F.); (E.D.P.)
| | - Jan Govaere
- VETMED, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium; (B.D.); (J.G.)
| | - David Plets
- WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (M.D.); (L.M.); (D.P.); (W.J.)
| | - Wout Joseph
- WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium; (M.D.); (L.M.); (D.P.); (W.J.)
| |
Collapse
|
5
|
Mouloodi S, Rahmanpanah H, Gohari S, Burvill C, Tse KM, Davies HMS. What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research. J Mech Behav Biomed Mater 2021; 123:104728. [PMID: 34412024 DOI: 10.1016/j.jmbbm.2021.104728] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 07/15/2021] [Accepted: 07/17/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examples, and employ this accumulated knowledge to fulfil challenging tasks such as regression analysis, pattern classification, and prediction. The horse biomechanical models have been identified as an alternative tool to investigate the effects of mechanical loading and induced deformations on the tissues and structures in humans. Many reported investigations into bone fatigue, subchondral bone damage in the joints of both humans and animals, and identification of vital parameters responsible for retaining integrity of anatomical regions during normal activities in all species are heavily reliant on equine biomechanical research. Horse racing is a lucrative industry and injury prevention in expensive thoroughbreds has encouraged the implementation of various measurement techniques, which results in massive data generation. ML substantially accelerates analysis and interpretation of data and provides considerable advantages over traditional statistical tools historically adopted in biomechanical research. This paper provides the reader with: a brief introduction to AI, taxonomy and several types of ML algorithms, working principle of a feedforward artificial neural network (ANN), and, a detailed review of the applications of AI, ML, and ANN in equine biomechanical research (i.e. locomotory system function, gait analysis, joint and bone mechanics, and hoof function). Reviewing literature on the use of these data-driven tools is essential since their wider application has the potential to: improve clinical assessments enabling real-time simulations, avoid and/or minimize injuries, and encourage early detection of such injuries in the first place.
Collapse
Affiliation(s)
- Saeed Mouloodi
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia.
| | - Hadi Rahmanpanah
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia
| | - Soheil Gohari
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia
| | - Colin Burvill
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia
| | - Kwong Ming Tse
- Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Melbourne, Australia.
| | - Helen M S Davies
- Department of Veterinary Biosciences, The University of Melbourne, Melbourne, Australia
| |
Collapse
|
6
|
Pfau T, Persson-Sjodin E, Gardner H, Orssten O, Hernlund E, Rhodin M. Effect of Speed and Surface Type on Individual Rein and Combined Left-Right Circle Movement Asymmetry in Horses on the Lunge. Front Vet Sci 2021; 8:692031. [PMID: 34322537 PMCID: PMC8311175 DOI: 10.3389/fvets.2021.692031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 05/27/2021] [Indexed: 11/13/2022] Open
Abstract
Differences in movement asymmetry between surfaces and with increasing speed increase the complexity of incorporating gait analysis measurements from lunging into clinical decision making. This observational study sets out to quantify by means of quantitative gait analysis the influence of surface and speed on individual-rein movement asymmetry measurements and their averages across reins (average-rein measurements). Head, withers, and pelvic movement asymmetry was quantified in 27 horses, identified previously as presenting with considerable movement asymmetries on the straight, during trot in hand and on the lunge on two surfaces at two speeds. Mixed linear models (p < 0.05) with horse as the random factor and surface and speed category (and direction) as fixed factors analyzed the effects on 11 individual-rein and average-rein asymmetry measures. Limits of agreement quantified differences between individual-rein and average-rein measurements. A higher number of individual-rein asymmetry variables-particularly when the limb that contributed to movement asymmetry on the straight was on the inside of the circle-were affected by speed (nine variables, all p ≤ 0.047) and surface (three variables, all p ≤ 0.037) compared with average-rein asymmetry variables (two for speed, all p ≤ 0.003; two for surface, all p ≤ 0.046). Six variables were significantly different between straight-line and average-rein assessments (all p ≤ 0.031), and asymmetry values were smaller for average-rein assessments. Limits of agreement bias varied between +0.4 and +4.0 mm with standard deviations between 3.2 and 12.9 mm. Fewer average-rein variables were affected by speed highlighting the benefit of comparing left and right rein measurements. Only one asymmetry variable showed a surface difference for individual-rein and average-rein data, emphasizing the benefit of assessing surface differences on each rein individually. Variability in straight-line vs. average-rein measurements across horses and exercise conditions highlight the potential for average-rein measurements during the diagnostic process; further studies after diagnostic analgesia are needed.
Collapse
Affiliation(s)
- Thilo Pfau
- Department of Clinical Science and Services, The Royal Veterinary College, London, United Kingdom
| | - Emma Persson-Sjodin
- Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Harriet Gardner
- Department of Clinical Science and Services, The Royal Veterinary College, London, United Kingdom
| | - Olivia Orssten
- Department of Clinical Science and Services, The Royal Veterinary College, London, United Kingdom
| | - Elin Hernlund
- Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Marie Rhodin
- Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden
| |
Collapse
|
7
|
Steinke SL, Montgomery JB, Barden JM. Accelerometry-Based Step Count Validation for Horse Movement Analysis During Stall Confinement. Front Vet Sci 2021; 8:681213. [PMID: 34239913 PMCID: PMC8259880 DOI: 10.3389/fvets.2021.681213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/25/2021] [Indexed: 11/28/2022] Open
Abstract
Quantitative tracking of equine movement during stall confinement has the potential to detect subtle changes in mobility due to injury. These changes may warn of potential complications, providing vital information to direct rehabilitation protocols. Inertial measurement units (IMUs) are readily available and easily attached to a limb or surcingle to objectively record step count in horses. The objectives of this study were: (1) to compare IMU-based step counts to a visually-based criterion measure (video) for three different types of movements in a stall environment, and (2) to compare three different sensor positions to determine the ideal location on the horse to assess movement. An IMU was attached at the withers, right forelimb and hindlimb of six horses to assess free-movement, circles, and figure-eights recorded in 5 min intervals and to determine the best location, through analysis of all three axes of the triaxial accelerometer, for step count during stall confinement. Mean step count difference, absolute error (%) and intraclass correlation coefficients (ICCs) were determined to assess the sensor's ability to track steps compared to the criterion measure. When comparing sensor location for all movement conditions, the right-forelimb vertical-axis produced the best results (ICC = 1.0, % error = 6.8, mean step count difference = 1.3) followed closely by the right-hindlimb (ICC = 0.999, % error = 15.2, mean step count difference = 1.8). Limitations included the small number of horse participants and the lack of random selection due to limited availability and accessibility. Overall, the findings demonstrate excellent levels of agreement between the IMU's vertical axis and the video-based criterion at the forelimb and hindlimb locations for all movement conditions.
Collapse
Affiliation(s)
- Samantha L Steinke
- Biomedical Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada.,Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Julia B Montgomery
- Biomedical Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada.,Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - John M Barden
- Faculty of Kinesiology and Health Studies, University of Regina, Regina, SK, Canada
| |
Collapse
|
8
|
Darbandi H, Serra Bragança F, van der Zwaag BJ, Voskamp J, Gmel AI, Haraldsdóttir EH, Havinga P. Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach. SENSORS 2021; 21:s21030798. [PMID: 33530288 PMCID: PMC7865839 DOI: 10.3390/s21030798] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/08/2021] [Accepted: 01/20/2021] [Indexed: 12/13/2022]
Abstract
Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.
Collapse
Affiliation(s)
- Hamed Darbandi
- Pervasive Systems Group, Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands; (B.J.v.d.Z.); (P.H.)
- Correspondence:
| | - Filipe Serra Bragança
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584 CM Utrecht, The Netherlands;
| | - Berend Jan van der Zwaag
- Pervasive Systems Group, Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands; (B.J.v.d.Z.); (P.H.)
- Inertia Technology B.V., 7521 AG Enschede, The Netherlands
| | - John Voskamp
- Rosmark Consultancy, 6733 AA Wekerom, The Netherlands;
| | - Annik Imogen Gmel
- Equine Department, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland; (A.I.G.); (E.H.H.)
- Agroscope—Swiss National Stud Farm, Les Longs-Prés, 1580 Avenches, Switzerland
| | - Eyrún Halla Haraldsdóttir
- Equine Department, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland; (A.I.G.); (E.H.H.)
| | - Paul Havinga
- Pervasive Systems Group, Department of Computer Science, University of Twente, 7522 NB Enschede, The Netherlands; (B.J.v.d.Z.); (P.H.)
| |
Collapse
|
9
|
Marin F. Human and Animal Motion Tracking Using Inertial Sensors. SENSORS 2020; 20:s20216074. [PMID: 33114597 PMCID: PMC7662986 DOI: 10.3390/s20216074] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 10/22/2020] [Indexed: 01/09/2023]
Abstract
Motion is key to health and wellbeing, something we are particularly aware of in times of lockdowns and restrictions on movement. Considering the motion of humans and animals as a biomarker of the performance of the neuro-musculoskeletal system, its analysis covers a large array of research fields, such as sports, equine science and clinical applications, but also innovative methods and workplace analysis. In this Special Issue of Sensors, we focused on human and animal motion-tracking using inertial sensors. Ten research and two review papers, mainly on human movement, but also on the locomotion of the horse, were selected. The selection of articles in this Special Issue aims to display current innovative approaches exploring hardware and software solutions deriving from inertial sensors related to motion capture and analysis. The selected sample shows that the versatility and pervasiveness of inertial sensors has great potential for the years to come, as, for now, limitations and room for improvement still remain.
Collapse
Affiliation(s)
- Frédéric Marin
- Centre of Excellence for Human and Animal Movement Biomechanics (CoEMoB), Laboratoire de BioMécanique et BioIngénierie (UMR CNRS 7338), Université de Technologie de Compiègne (UTC), Alliance Sorbonne Université, 60200 Compiègne, France
| |
Collapse
|