Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms

Valkyrie—design and development of gaits for quadruped robot using particle swarm optimization

Over the past decades, developments and scientific breakthroughs in the field of robotics have shown the replacement of wheeled robots with legged robots, which are often inspired by the biological characteristics of legged animals. Many industries and urban-based applications promote quadruped robots because of their dexterous ability to efficiently handle multiple tasks in the working environment. Motivated from the recent works in the field of quadruped robots, this research aims to develop and investigate gaits for a 2 DoF mammal-inspired quadruped robot that incorporates 4 hip and 4 knee servo motors as its locomotion element. Forward and inverse kinematic techniques are used to determine the joint angle required for the locomotion and stability calculation are presented to determine the center of mass/center of gravity of the robot. Three types of gaits such as walk, trot, and pace are developed while keeping the center of mass inside the support polygon using a closed-loop control system. To minimize errors and improve the performance of the robot due to its non-linearity, a meta-heuristic algorithm has been developed and addressed in this work. The fitness function is derived based on the Euclidean distance between the target and robot’s current position and kinematic equations are used to obtain the relation between joints and coordinates. Based on the literature, particle swarm optimization (PSO) was found to be a promising algorithm for this problem and is developed using Python’s ‘Pyswarms’ package. Experimental studies are carried out quantitatively to determine the convergence characteristics of the control algorithm and to investigate the distance traveled by the robot for different target positions and gaits. Comparison between experimental and theoretical results prove the efficiency of the proposed algorithm and stability of the robot during various gait movements

A scoping review of determinants of performance in dressage

As a first step in achieving an evidence-based classification system for the sport of Para Dressage, there is a clear need to define elite dressage performance. Previous studies have attempted to quantify performance with able-bodied riders using scientific methods; however, definitive measures have yet to be established for the horse and/or the rider. This may be, in part, due to the variety of movements and gaits that are found within a dressage test and also due to the complexity of the horse-rider partnership. The aim of this review is therefore to identify objective measurements of horse performance in dressage and the functional abilities of the rider that may influence them to achieve higher scores. Five databases (SportDiscuss, CINAHL, MEDLINE, EMBASE, VetMed) were systematically searched from 1980 to May 2018. Studies were included if they fulfilled the following criteria: (1) English language; (2) employ objective, quantitative outcome measures for describing equine and human performance in dressage; (3) describe objective measures of superior horse performance using between-subject comparisons and/or relating outcome measures to competitive scoring methods; (4) describe demands of dressage using objective physiological and/or biomechanical measures from human athletes and/or how these demands are translated into superior performance. In total, 773 articles were identified. Title and abstract screening resulted in 155 articles that met the eligibility criteria, 97 were excluded during the full screening of articles, leaving 58 included articles (14 horse, 44 rider) involving 311 equine and 584 able-bodied human participants. Mean ± sd (%) quality scores were 63.5 ± 15.3 and 72.7 ± 14.7 for the equine and human articles respectively. Significant objective measures of horse performance (n = 12 articles) were grouped into themes and separated by gait/movement. A range of temporal variables that indicated superior performance were found in all gaits/movements. For the rider, n = 5 articles reported variables that identified significant differences in skill level, which included the postural position and ROM of the rider’s pelvis, trunk, knee and head. The timing of rider pelvic and trunk motion in relation to the movement of the horse emerged as an important indicator of rider influence. As temporal variables in the horse are consistently linked to superior performance it could be surmised that better overall dressage performance requires minimal disruption from the rider whilst the horse maintains a specific gait/movement. Achieving the gait/movement in the first place depends upon the intrinsic characteristics of the horse, the level of training achieved and the ability of the rider to apply the correct aid. The information from this model will be used to develop an empirical study to test the relative strength of association between impairment and performance in able-bodied and Para Dressage riders

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Machine-Learning-Powered Cyber-Physical Systems

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well

The use of surface electromyography within equine performance analysis

Equine athletes participate in a wide range of equestrian disciplines. Performance analysis in sport is the collection and subsequent analysis of data, or key information sets, related to facets of training and / or competition, to accelerate and improve athletic performance. Equine performance analysis research aims to optimise the potential competition success of the horse whilst concurrently promoting health and welfare and increasing career longevity. Despite the benefits associated with performance analysis, its application is limited in equine sport.Surface electromyography (sEMG) is a non-invasive technique which illustrates recruitment patterns of superficial skeletal muscle and can provide quantitative data on the activity within muscle during dynamic motion. sEMG has the potential to contribute to equine performance analysis particularly via assessment of muscle recruitment, activity and adaptation within training regimens and during competition. The critical commentary demonstrates the potential of surface electromyography (sEMG) as an effective performance analysis tool that could be used to assess the physiological response of muscle during field-based exercise in the horse and provides examples of how sEMG data obtained could guide improvements in the efficacy of training regimens for the equine athlete. Critical reflection on four peer-reviewed evidence sources was conducted to establish their contribution to equine performance research and to facilitate debate of future research directions for equine sEMG. The research demonstrates the validity of telemetric sEMG as an emerging technology that could be used to analyse muscle performance in the equine athlete for defined events, for example jumping a fence, and to assess performance over time, for example monitoring muscle activity during interval training. Opportunities also exist to determine the efficacy of muscle-related clinical and therapeutic interventions such as prophylactic dentistry or physiotherapy. The preliminary research presented suggests the use of equine sEMG as a performance analysis tool has most value to assess and compare muscle performance during exercise within individual horses. However further research is required to substantiate this. Future studies integrating larger sample sizes, horses selected from specific equestrian disciplines and breeds, and further exploration of the impact of coat length and sEMG sensor placement on data obtained would be worthwhile to standardise and validate the protocols employed here. Equine performance is a complex area; future work needs to focus on the individual characteristics that contribute to desired performance goals, but should also evaluate performance as a holistic entity. It is essential for progression in the performance field that research undertaken is shared with the equine industry to enable practical implementation. The use of sEMG in the equine athlete has the potential to increase understanding of how muscle responds to exercise and could help create an evidence-base to inform individual and discipline-specific training regimens. Increased efficacy in training should promote success, enhancing performance and extending career longevity for the equine athlete, whilst indirectly benefiting the horse’s health and welfare through improved management practices and injury reduction