System and method for determining a status of an equine animal

Abstract

A system, device and method 200 for determining a status of an equine. The system includes a local signal provider in an environment and an electronic device arranged to receive location signals from

Description

TECHNICAL FIELD Embodiments described herein relate to systems and methods for determining status of an equine animal, as well as an electronic device for use in the system. BACKGROUND Equestrian sports such as dressage typically requires combined work of a horse and its rider to perform a series of actions or movements in a dressage arena. In dressage, the performance of the actions or movements is judged based on, among other factors, the gait of the equine animal in executing the actions or movements. It would be helpful to monitor the gait of the equine animal to determine the performance of the rider and the horse, for example during training. In one example, the gait of the horse can be determined based solely on manual (visual) inspection by an experienced individual, which, however, may be susceptible to error. In another example, the gait of the horse can be determined using imaging systems to capture and analyse images / video of the movement of the horse. One problem associated with this approach is that the processing can be rather slow and complicated. In yet another example, the gait of the horse can be determined using multiple sensor devices operable to detect physiological parameters and movement of the horse. Often, these sensor devices are directly attached to the equine animal. This may cause discomfort to and / or affect natural movement of the equine animal, especially when a large amount of different types of sensor devices are used. SUMMARY In a first aspect, there is provided a system for determining a status of an equine animal. The system comprises a local signal provider arranged in an environment and an electronic device. The electronic device is arranged to: receive or obtain movement data associated with movement of the equine animal in the environment; receive location signals from the local signal provider; determine a movement characteristic of the movement of the equine animal based at least in part on the movement data; determine a location of the equine animal in the environment based at least in part on the received location signals; and determine, based at least in part on the determined movement characteristic and the determined location, a status of the equine animal. The electronic device may be operable to perform a movement characteristic determination operation, a location determination operation, and a status determination operation based at least in part on the movement characteristic determination operation and the location determination operation. The electronic device is arranged to be carried by a rider of the equine animal or carried by the equine animal so that these operations are performed when the rider or the equine animal performs these operations. For example, the electronic device is a mobile (smart) phone. The environment may be an arena, a stadium, a pitch, or a field. The environment may be an outdoor environment or an indoor environment. The equine animal may be a horse. In one embodiment, the environment comprises an area over which the equine animal can traverse, and the local signal provider is provided within the area and arranged to provide location signals over the area. The local signal provider may be placed on or under the ground of the environment. Or the local signal provider may be coupled to (e.g., mounted to or placed on) a fixed support structure in the environment. In one embodiment, the local signal provider comprises a plurality of beacons. The plurality of beacons are operable to provide location signals to the electronic device using a wireless communication protocol. The wireless communication protocol may include a Bluetooth® communication protocol, such as a BLE communication protocol. The plurality of beacons are arranged in different locations in the environment. In one embodiment, the plurality of beacons are arranged around the perimeter of the area. In one embodiment, the electronic device is arranged to: compare the determined status of the equine animal with a reference status of the equine animal, and provide an output to indicate a mismatch if it is determined that the determined status does not match the reference status. The output may include an audio output (e.g., alarm, alert, speech), a visual output (e.g., images, icons, text), and / or a haptic output (e.g., vibration). The determined status may reflect the health state of the equine animal. The reference status may be an expected status of the equine animal. In one embodiment, the electronic device is arranged to: determine, based at least in part on the determined location, a reference movement characteristic associated with a reference movement of the equine animal at the determined location; compare the determined movement characteristic with the reference movement characteristic to determine whether the movement matches the reference movement; and provide an output to facilitate performing of the reference movement if it is determined that the movement does not match the reference movement. The output may include an audio output (e.g., alarm, alert, speech), a visual output (e.g., images, icons, text), and / or a haptic output (e.g., vibration), which provide notification or instruction for facilitating performing of the reference movement. The reference movement may be an expected movement, i.e., movement expected to the performed by the equine animal. In one embodiment, the electronic device is arranged to: determine an extent of deviation of the determined movement characteristic from the reference movement characteristic, and determine that the movement does not match the reference movement if the extent of deviation is beyond a reference deviation value or range. In one embodiment, the electronic device is arranged to: receive user input for setting the reference deviation value or range, and set the reference deviation value or range based at least in part on the user input. In one embodiment, the electronic device is arranged to: determine, based at least in part on the determined location, a movement routine for the equine animal at the determined location; and provide an output to facilitate execution of the movement routine. The output may include an audio output (e.g., alarm, alert, speech), a (e.g., images, icons, text), and / or a haptic output (e.g., vibration), which provide notification or instruction for facilitating execution of the movement routine. In one embodiment, the plurality of beacons comprises at least three beacons, and the electronic device is arranged to: receive respective location signal from at least three of the beacons in the environment, each respective location signal comprises an identifier of the corresponding beacon; and perform a trilateration based localisation operation based at least in part on the received location signals to determine the location of the equine animal in the environment. In one embodiment, each respective location signal comprises, in addition to or as part of the identifier, information associated with the location of the corresponding beacon in the environment. In one embodiment, the electronic device is arranged to: perform the trilateration based localisation operation each time after a further location signal is received from one of the beacons in the environment, so as to update the location of the equine animal in the environment. The electronic device is arranged to: determine the beacon corresponding to the further location signal, and replace the previous location signal received from that beacon with the further location signal. The electronic device may track a travel path of the equine animal based at least in part on two or more determined I updated locations. In one embodiment, the trilateration based localisation operation comprises, for each of at least three most recently received location signals received from different beacons: determining, based at least in part on the corresponding identifier and a strength (e.g., power level) of the received location signal, a respective estimated distance between the equine animal and the corresponding beacon; and determining, based at least in part on the estimated distances and information associated with locations of the beacons in the environment, the location of the equine animal in the environment. The information associated with locations of the beacons in the environment may be included in the location signals. Alternatively, the information associated with locations of the beacons in the environment may be stored in or otherwise obtainable by the electronic device. For example, the respective estimated distance is determined by processing the received signal strength indicator (RSSI) value of the received location signal using a free space path loss model. In one embodiment, the trilateration based localisation operation comprises: identifying, based at least in part on the identifier in the most recently received location signal, the corresponding beacon; determining, from the beacons, all three-beacon sets, each of the three-beacons set respectively consists of a combination of the identified beacon and any two other beacons; determining, for each of respective three-beacon set, a respective estimated location of the equine animal in the environment and a respective mean received time of the signals used for determining the corresponding estimated location; and determining, based at least in part on the estimated locations and the mean received times of all the all three-beacon sets, the location of the equine animal in the environment. In one embodiment, the trilateration based localisation operation comprises: determining an average of the estimated locations to determine the location of the equine animal in the environment. In one embodiment, the trilateration based localisation operation comprises: determining a weighted average of the estimated locations to determine the location of the equine animal in the environment. In one embodiment, the determination of the weighted average of the estimated locations comprises: weighting the estimated locations based at least in part on the mean received times; and averaging the weighted estimated locations to determine the location of the equine animal in the environment. For example, a higher weight may be applied to the estimated location with a more recent mean received time than the estimated location with a less recent mean received time. In one embodiment, the determination of the weighted average of the estimated locations comprises: weighting the estimated locations based at least in part on proximity measures of the three-beacon sets, the proximity measure of each respective three-beacon set indicates how close (level of proximity) the corresponding three beacons are arranged in the environment; and averaging the weighted estimated locations to determine the location of the equine animal in the environment. For example, a lower weight may be applied to the three-beacon set with a higher proximity than the three-beacon set with a lower proximity. In one embodiment, the electronic device comprises a motion sensor arranged to obtain the movement data. For example, the motion sensor comprises an inertial measurement unit (IMU). For example, the motion sensor comprises a gait sensor. For example, the motion sensor comprises an accelerometer (e.g., triaxial accelerometer). For example, the motion sensor comprises a gyroscope (e.g., triaxial gyroscope). For example, the motion sensor comprises a magnetometer. In one embodiment, the electronic device is arranged to receive the movement data obtained by a motion sensor that does not belong to part of the electronic device (i.e., not arranged in the electronic device) but is operable to communicate data with the electronic device. In one embodiment, the motion sensor comprises an accelerometer and a gyroscope, and the movement data comprises accelerometer data and gyroscope data. In one embodiment, the electronic device is arranged to: perform a sensor fusion operation on at least the accelerometer data and the gyroscope data to obtain processed movement data; and determine the movement characteristic of the equine animal based at least in part on the processed movement data. The sensor fusion operation may be performed using at least a Kalman filter. In one embodiment, the movement characteristic comprises step rate, step pattern, and / or gait. The steps correspond to hoof impacts. The step rate may be number of steps per minute. In one embodiment, the electronic device is arranged to determine the gait as one of: walk, trot, canter, or gallop. In one embodiment, the electronic device is arranged to determine the gait as one of: walk, trot, or canter. In one embodiment, the electronic device is arranged to determine the step pattern as one of: a two-beat pattern, a three-beat pattern, or a four-beat pattern. In one embodiment, the electronic device is arranged to: process the movement data to identify steps taken by the equine animal in the movement; determine the step rate and / or the step pattern based at least in part on the identified steps; and process at least the step rate and the step pattern using a model to determine the gait. For example, the electronic device is arranged to perform a peak detection operation on the movement data to identity peaks in acceleration in the movement data, the peaks correspond to the steps. For example, the electronic device is arranged to perform a time-series analysis operation (such as autocorrelation operation or frequency domain analysis) on the movement data to identify the steps. In one embodiment, the electronic device is arranged to: compare the movement data with each of a plurality of sets of reference movement data by applying dynamic time warping to determine a respective similarity measure between the movement data and the corresponding reference movement data, each of the plurality of sets of reference movement data is associated with a respective gait; and determine the gait based at least in part on the similarity measures. In one embodiment, the electronic device is arranged to: process the movement data using a machine learning based model to determine the gait. The machine learning based model may comprise a recurrent neural network. The machine learning based model may comprise a bidirectional long short-term memory model. In one embodiment, the electronic device is arranged to: extract one or more features from the movement data; and apply the one or more extracted features to a classification model to determine the gait. The one or more features may be related to steps taken by the equine animal in the movement. The one or more features may include one or more of: average time between the steps, deviation in times between the steps, number of clusters of step types, signal magnitude area associated with the movement data, correlation coefficients between movement data associated with different axes of the motion sensor (e.g., IMU), or differences in correlated peaks between movement data associated with different axes of the motion sensor. The classification model may comprise K nearest neighbour (KNN), support vector machine (SVM), random forest, etc. In a second aspect, there is provided an electronic device configured for use as the electronic device in the system of the first aspect. In one embodiment, the electronic device comprises a motion sensor arranged to obtain movement data associated with movement of the equine animal in an environment, a communication unit arranged to receive location signals from a local signal provider arranged in the environment, and one or more processors. The one or more processors are arranged to: determine a movement characteristic of the movement of the equine animal based at least in part on the movement data; determine a location of the equine animal in the environment based at least in part on the received location signals; and determine, based at least in part on the determined movement characteristic and the determined location, a status of the equine animal. In a third aspect, there is provided a local signal provider configured for use as the local signal provider in the system of the first aspect. In a fourth aspect, there is provided a kit comprising the electronic device of the second aspect and the local signal provider of the third aspect. In a fifth aspect, there is provided a method for determining a status of an equine animal. The method comprises: receiving movement data associated with movement of the equine animal in an environment, the movement data being obtained by a motion sensor; determining a movement characteristic of the movement of the equine animal based at least in part on the movement data; determining a location of the equine animal in the environment based at least in part on location signals received from a local signal provider arranged in the environment; and determining, based at least in part on the determined movement characteristic and the determined location, a status of the equine animal. In one embodiment, the method comprises: comparing the determined status of the equine animal with a reference status of the equine animal, and providing an output to indicate a mismatch if it is determined that the determined status does not match the reference status. In one embodiment, the method comprises: determining, based at least in part on the determined location, a reference movement characteristic associated with a reference movement of the equine animal at the determined location; comparing the determined movement characteristic with the reference movement characteristic to determine whether the movement matches the reference movement; and providing an output to facilitate performing of the reference movement if it is determined that the movement does not match the reference movement. In one embodiment, comparing the determined movement characteristic with the reference movement characteristic comprises: determining an extent of deviation of the determined movement characteristic from the reference movement characteristic, and determining that the movement does not match the reference movement if the extent of deviation is beyond a reference deviation value or range. In one embodiment, the method comprises: receiving user input for setting the reference deviation value or range, and setting the reference deviation value or range based at least in part on the user input. In one embodiment, the method comprises: determining, based at least in part on the determined location, a movement routine for the equine animal at the determined location; and providing an output to facilitate execution of the movement routine. In one embodiment, the plurality of beacons comprises at least three beacons, and the method comprises: receiving respective location signal from at least three of the beacons in the environment, each respective location signal comprises an identifier of the corresponding beacon; and performing a trilateration based localisation operation based at least in part on the received location signals to determine the location of the equine animal in the environment. In one embodiment, the method comprises: performing the trilateration based localisation operation each time after a further location signal is received from one of the beacons in the environment, so as to update the location of the equine animal in the environment. The method comprises: determining the beacon corresponding to the further location signal, and replacing the previous location signal received from that beacon with the further location signal. For example, the method comprises tracking a travel path of the equine animal based at least in part on two or more determined I updated locations. In one embodiment, the trilateration based localisation operation comprises, for each of at least three most recently received location signals: determining, based at least in part on the corresponding identifier and a strength (e.g., power level) of the received location signal, a respective estimated distance between the equine animal and the corresponding beacon; and determining, based at least in part on the estimated distances and information associated with locations of the beacons in the environment, the location of the equine animal in the environment. In one embodiment, the trilateration based localisation operation comprises: identifying, based at least in part on the identifier in the most recently received location signal, the corresponding beacon; determining, from the beacons, all three-beacon sets, each of the three-beacons set respectively consists of a combination of the identified beacon and any two other beacons; determining, for each of respective three-beacon set, a respective estimated location of the equine animal in the environment and a respective mean received time of the signals used for determining the corresponding estimated location; and determining, based at least in part on the estimated locations and the mean received times of all the all three-beacon sets, the location of the equine animal in the environment. In one embodiment, the trilateration based localisation operation comprises: determining an average of the estimated locations to determine the location of the equine animal in the environment. In one embodiment, the trilateration based localisation operation comprises: determining a weighted average of the estimated locations to determine the location of the equine animal in the environment. In one embodiment, the determination of the weighted average of the estimated locations comprises: weighting the estimated locations based at least in part on the mean received times; and averaging the weighted estimated locations to determine the location of the equine animal in the environment. In one embodiment, the determination of the weighted average of the estimated locations comprises: weighting the estimated locations based at least in part on proximity measures of the three-beacon sets, the proximity measure of each respective three-beacon set indicates how close the corresponding three beacons are arranged in the environment; and averaging the weighted estimated locations to determine the location of the equine animal in the environment. In one embodiment, the motion sensor comprises an accelerometer and a gyroscope, and the movement data comprises accelerometer data and gyroscope data. In one embodiment, the method comprises: performing a sensor fusion operation on at least the accelerometer data and the gyroscope data to obtain processed movement data; and determining the movement characteristic of the equine animal based at least in part on the processed movement data. In one embodiment, the movement characteristic comprises step rate, step pattern, and / or gait. The steps correspond to hoof impacts, and the step rate may be number of steps per minute. In one embodiment, the gait is determined (e.g., classified) as one of: walk, trot, canter, or gallop. In one embodiment, the gait is determined (e.g., classified) as one of: walk, trot, or canter. In one embodiment, the step pattern is determined (e.g., classified) as one of: a two-beat pattern, a three-beat pattern, or a four-beat pattern. In one embodiment, determining the movement characteristics comprises: processing the movement data to identify steps taken by the equine animal in the movement; determining the step rate and / or the step pattern based at least in part on the identified steps; and processing at least the step rate and the step pattern using a model to determine the gait. In one embodiment, determining the movement characteristics comprises: comparing the movement data with each of a plurality of sets of reference movement data by applying dynamic time warping to determine a respective similarity measure between the movement data and the corresponding reference movement data, each of the plurality of sets of reference movement data is associated with a respective gait; and determining the gait based at least in part on the similarity measures. In one embodiment, determining the movement characteristics comprises: processing the movement data using a machine learning based model to determine the gait. In one embodiment, determining the movement characteristics comprises: extracting one or more features from the movement data; and applying the one or more extracted features to a classification model to determine the gait. In a sixth aspect, there is provided a system comprising one or more processors and memory. The memory stores instructions which, when executed by the one or more processors, cause the one or more processors to perform the method of the fifth aspect. In a seventh aspect, there is provided a carrier medium carrying computer readable instructions adapted to cause one or more processors to perform the method of the fifth aspect. Other features and aspects of embodiments of the invention will become apparent by consideration of the detailed description and accompanying drawings. Any feature(s) described herein in relation to one aspect or embodiment may be combined with any other feature(s) described herein in relation to another aspect or embodiment as appropriate and applicable. Unless otherwise specified, terms of degree such as “generally”, “about”, “substantially”, or the like, are used, depending on context, to account for one or more of the following: manufacture tolerance, degradation, trend, tendency, imperfect practical condition(s), etc. BREIF DESCRIPTION OF THE DRAWINGS Embodiments of the invention will now be described, by way of example, with reference to the accompanying drawings in which: Fig. 1 is a schematic diagram illustrating a system for determining a status of an equine animal in an environment in one embodiment of the invention; Fig. 2 is a flowchart illustrating a method for determining a status of an equine animal in one embodiment of the invention; Fig. 3 is a schematic diagram illustrating a system for determining a status of a horse in a dressage arena in one embodiment of the invention; Fig. 4 is a flowchart illustrating an operation that can be performed by the beacon in the system of Fig. 3 in one embodiment of the invention; Fig. 5 is a flowchart illustrating an operation that can be performed at least partly by the electronic device in the system of Fig. 3 in one embodiment of the invention; Fig. 6 is a flowchart illustrating an operation that can be performed at least partly by the electronic device in the system of Fig. 3 in one embodiment of the invention; Fig. 7 is a flowchart illustrating an operation that can be performed at least partly by the electronic device in the system of Fig. 3 in one embodiment of the invention; Fig. 8 is a flowchart illustrating an operation that can be performed at least partly by the electronic device in the system of Fig. 3 in one embodiment of the invention; Fig. 9 is a simplified block diagram of an information handling system in one embodiment of the invention; and Fig. 10 is a simplified block diagram of a beacon in one embodiment of the invention. DETAILED DESCRIPTION Fig. 1 shows an environment E and a system 100 for determining a status of an equine animal in the environment E in one embodiment of the invention. In this embodiment, the equine animal is a horse H and a rider R is on the horse H. The environment E defines an area over which the horse H can traverse. The system 100 includes an electronic device 102 and a local signal provider 104 arranged in an environment E. The electronic device 102 may be a smart phone, a tablet or pad-type computer, or a wearable device, and it may be carried by the horse H or by the rider R. The electronic device 102 may be installed with an application (APP) or a program (software) for determining status of the horse H in the environment E. Additionally or alternatively, the electronic device 102 may access a web application (web app) arranged to determine status of the horse H in the environment E. The electronic device 102 is arranged to process movement data associated with movement of the horse H in the area to determine a movement characteristic (e.g., step rate, step pattern, or gait) of the horse H as it traverses in the area. The movement data may be obtained by a motion sensor of the electronic device, or it may be obtained by an external motion sensor (e.g., carried by the rider R or the horse H) operably connected with the external device. The local signal provider 104 is provided within the area of the environment E. The local signal provider 104 may be placed on or under the ground in the area, or supported by a fixed support structure in the area. The local signal provider 104 is arranged to provide location signals over the area (e.g., using one or more wireless communication protocol), and the electronic device 102 is arranged to receive the location signals provided by the local signal provider 104 for determining a location of the horse H (or the electronic device 102 carried by the horse H or the rider R) in the area. The location may be a relative location, e.g., a location relative to the area or to the local signal provider 104. The electronic device 102 is further arranged to determine a status of the horse H based on the determined movement characteristic and the determined location. Fig. 2 illustrates a method 200 for determining a status of an equine animal (e.g., horse H) in one embodiment of the invention. The method 200 may be implemented using a suitable system (e.g., system 100) and in a suitable environment (e.g., environment E). The method 200 may be implemented, partly or entirely, as an application (APP) or a program that operates on a suitable electronic device (e.g., electronic device 102). Additionally or alternatively, the method 200 may be implemented, partly or entirely, as a web application (web app), which can be accessed using a suitable electronic device (e.g., electronic device 102). The method 200 includes, in 202, performing a movement characteristic determination operation, in 204, performing a location determination operation, and in 206, performing a status determination operation. The movement characteristic determination operation in 202 includes processing movement data (obtained by a motion sensor and is associated with movement of the equine animal in an environment as the equine animal traverses the environment) and optionally other data (e.g., physical properties (e.g. type / breed / size / weight) of the equine animal) to determine the movement characteristic of the movement of the equine animal. The location determination operation in 204 includes processing location signals received from a local signal provider arranged in the environment to determine the location (e.g., relative location) of the equine animal in the environment. The status determination operation in 206 includes processing the determined movement characteristics obtained in 202 and the determine location in 204 to determine the status of the equine animal. The operations in 202 and 204 may be performed simultaneously or sequentially (in any order). Fig. 3 shows a dressage arena and a system 300 for determining a status of a horse in the dressage arena in one embodiment of the invention. The system 300 can be considered as an example implementation of the system 100. In this embodiment, the dressage arena is a standard dressage arena, which has a rectangular area of about 60m in length and about 20m in width. The letters A, K, V, E, S, H, C, M, R, B, P, F designate different locations along the perimeter of the area. The letters D, L, X, I, G designate different locations within the area and along a centre line (long axis) of the area. Cones, flags, markers, or structures (preferably with the letters) may be placed in the arena to mark the locations corresponding to the letters. In this embodiment, the system 300 includes an electronic device 302 carried by the rider of the horse and a local signal provider with six beacons 304 arranged around the perimeter of the area at locations C, S, R, V, P, A. The beacons 304 are Bluetooth® (Bluetooth® low energy, BLE) beacons arranged to provide (e.g., broadcast) location signals using Bluetooth® communication protocol. Each beacon 304 may include a respective label, marker, or like indicator (e.g., on its housing) for indicating the location (C / S / R / V / P / A) the beacon 304 is arranged to be placed in the dressage arena. The positions (e.g., relative positions) of the beacons 304 in the dressage arena may be known (in accordance with the indicators) and this positional information may be stored in the electronic device 302 before the setup of the beacons 304 is completed. The stored positional information can be updated based on the application of the beacons 304 (e.g., based on whether the beacons 304 are arranged, or are to be arranged, in a standard dressage arena or a small dressage arena). In this embodiment, the electronic device 302 is a smart phone with a motion sensor for detecting movement (obtaining movement data) of the horse. The motion sensor may include an inertial measurement unit (IMU). The motion sensor may include a gait sensor. The motion sensor may include an accelerometer (e.g., triaxial), a gyroscope (e.g., triaxial), a magnetometer, or any of their combination. The electronic device 302 is installed with an application (APP) or a program for determining status of the horse. The electronic device 302 is arranged to obtain the movement data using the motion sensor and process the movement data to determine a movement characteristic (e.g., step rate, step pattern, or gait) of the horse. The electronic device 302 is further arranged to receive the location signals provided (e.g., broadcasted) by the beacons 304 and process the received location signals to determine a location of the horse H (or the electronic device 302 carried by the horse H or the rider R) in the area. The location may be a relative location, which may be expressed in coordinate or relative coordinate. The electronic device 302 is further arranged to determine a status of the horse based on the determined movement characteristic and the determined location. The system 300 is operable to perform a location determination operation, which may correspond to the one in 204 in method 200. In the location determination operation of this embodiment, the electronic device 302 is arranged to receive (e.g., pick up) location signals transmitted (e.g., broadcasted) by the beacons 304 and perform a trilateration based localisation operation based on the received location signals to determine the location of the horse in the area. Each location signal includes an identifier of the corresponding beacon 304, and optionally, information associated with the location of the corresponding beacon 304 in the area. The trilateration based localisation operation may be performed each time after a location signal is received from one of the beacons 304, so as to update the location of the horse in the area and hence facilitate tracking of the path (or trajectory) of travel of the horse in the area. For example, the trilateration based localisation operation may include, for each of the three most recently received location signals received from different beacons 304: determining, based on the corresponding identifier and a strength of the corresponding received location signal, a respective estimated distance between the horse and the corresponding beacon 304, and determining, based on the estimated distances and information associated with locations of the beacons 304 in the area, the location of the horse in the area. In one example, the trilateration based localisation operation is a multi-range variant trilateration operation, which includes: processing the most recently received location signal to determine the beacon 304 from which the location signal originates, and determining all three-beacon sets that include the identified beacon 304 (i.e., each of the three-beacons set respectively consists of a combination of the identified beacon 304 and two other beacons 304). The multi-range variant trilateration operation further includes, for each of the three-beacon set, determining a respective estimated location of the horse in the area and a respective mean received time of the location signals used for determining the corresponding estimated location. The multi-range variant trilateration operation further includes determining the location of the horse in the area based on the estimated locations and the mean received times of all the three-beacon sets. For example, an average, moving average, weighted average, or the like may be applied to the estimated locations to determine the location of the horse in the area. For example, the weighted average may be weighted based on the mean received times and / or the proximity measures of the three-beacon sets (a measure that indicates how close the three beacons 304 in the three-beacon set are arranged in the area). Fig. 4 shows an operation 400 that can be performed by a beacon 304 in the system 300 of Fig. 3 (in which the beacons 304 are Bluetooth® Low Energy beacons placed around the dressage arena) in one embodiment of the invention. In this embodiment, all of the beacons 304 are each respectively operable to perform operation 400. In operation 400, the beacon 304 transmits a location signal (advertisement packet) in 402, and then enters into a reduced power or sleep state for a duration (e.g., n seconds) in 404. The beacon 304 then powers up and transmits another location signal (advertisement packet) in 402, and return to the reduced power or sleep state in 404. The process can be performed repeatedly. More specifically, in 404, the processor of the beacon 304 initiates a timer and enters into a reduced power or sleep state for a predetermined duration, e.g., a few hundred milliseconds to a few seconds. The duration may be selected to optimize power consumption of the beacon 304 and location update frequency. When or after the timer expires, the beacon 304 enters into a normal or increased power state, and the radio module of the beacon 304 generates and transmits a location signal (advertisement packet) using Bluetooth® Low Energy (BLE) communication protocol. The location signal includes a unique identifier of the corresponding beacon 304 and optionally other data such as the letter-position where the corresponding beacon 304 is arranged. The electronic device 302 is arranged to operate a corresponding operation that complements operation 400. Specifically, the electronic device 302 is arranged to operate a dedicated Bluetooth® Low Energy scanning process to continuously monitor for (detect and receive) the location signals (advertisement packets) transmitted by the beacons 304. The electronic device 302 is arranged to process the received location signals, to determine, from each respective location signal, the identifier of the corresponding beacon 304, the received signal strength indicator (RSSI) value of the location signal, the estimated distance to the corresponding beacon 304 (i.e., between the electronic device 302 and the beacon 304) and the time of receipt of the location signal (e.g., the beacon-distance map). The estimated distance to the corresponding beacon 304 can be calculated using a path loss model or other estimator based on the RSSI value of the received location signal. In this embodiment, the RSSI value is used as an input to a free space path loss model to estimate the relative distance to corresponding beacon 304. This embodiment does not require accurate absolute distance to function / work hence the model does not require calibration based on the antenna gain of the electronic device 302. In this embodiment it is assumed that all electronic devices 302 in the system 300 have the same nominal antenna gain for the purposes of the calculation. By using at least three estimated distances between the electronic devices 302 and each of at least three beacons 304, and information associated with the locations of the corresponding beacons 304 in the area, the location of the electronic device 302 (hence the horse and the rider) in the area can be determined based on trilateration. In this embodiment, location of the beacon 304 is defined in the unique identifier of the beacon 304 so the electronic device 302 can determine the location of the beacon 304 based on the received location signal. In another embodiment, the location of the beacon 304 is not defined in the location signal, and a table or map containing locations of the beacons 304 and their corresponding unique identifiers is stored in or otherwise accessible by the electronic device 302. The electronic device 302 may be arranged to determine (or update) the location of the electronic device 302 hence the horse and the rider) after new location signals are received from at least three different beacons 304. However, if the new location signals are not received frequent enough, the electronic device 302 may need to wait for a relatively long time to determine (or update) the location, which may be undesirable or even problematic in some applications. For example, if the location signals are transmitted n seconds apart, it may be necessary to wait for up to 2n seconds before the location can be determined or updated. To address the problem, the electronic device 302 may be arranged to determine (or update) the location of the electronic device 302 (hence the horse and the rider) in the area each time after a new location signal is received. This may increase the determination / update frequency of the location and may enable smoother travel path or trajectory tracking over time. To this end, the electronic device 302 is arranged to a multi-range variant trilateration operation as follows. First, a set of three-combinations of all beacons is taken, and the set is filtered to include only the combinations including the beacon from which the new location signal originates. For example, for the six beacons 304 (beacon-1 to beacon-6), the set of three-combinations s of all beacons include: beacons 1-2-3, beacons 1-2-4, beacons 1-2-5, beacons 1-2-6, beacons 1-3-4, beacons 1-3-5, beacons 1-3-6, beacons 1-4-5, beacons 1-4-6, beacons 1-5-6, beacons 2-3-4, beacons 2-3-5, beacons 2-3-6, beacons 2-4-5, beacons 2-4-6, beacons 2-5-6, beacons 3-4-5, beacons 3-4-6, beacons 3-5-6, and beacons 4-5-6. For example, if the new location signal originates from beacon-1, then the set is filtered to include only the following (which includes beacon-1): beacons 1-2-3, beacons 1-2-4, beacons 1-2-5, beacons 1-2-6, beacons 1-3-4, beacons 1-3-5, beacons 1-3-6, beacons 1-4-5, beacons 1-4-6, beacons 1-5-6. Then, for each of the remaining combinations (obtained after filtering the set), the respective location of the electronic device 302 (hence the horse and the rider) in the area is determined based on the corresponding three distances (between each of the three beacons 304 and the electronic device 302). The determined location (e.g., estimated position coordinate) for each remaining combination and the mean time of receipt of the location signals for each remaining combination (e.g., combinationlocation map) may be stored. The combination-position map may be shared between cycles of the process and may include estimated distances based on all three-combinations of all beacons 304 after 2n seconds. For each combination, the most recently determined (e.g., updated) location may overwrite a prior determined location. Afterwards, an exponential moving average, which is arranged to decay based on the mean received time for each of the three-beacon sets, may be applied to the determined locations to determine the location of the electronic device 302 (hence the horse and the rider) in the area. By determining multiple locations of the electronic device 302 in this way, movement of the electronic device 302 (hence the horse and the rider) can be tracked. In this operation, the average may be weighed against the contribution of determined locations involving very far or very close beacons 304 as these determined locations may be more likely to introduce error due to weak (or lost) location signal or due to obstruction / occlusion by the body of the rider. Additionally or alternatively, the average may be weighed against combinations of beacons 304 that feature similar vectors (i.e., close together beacons 304) from each beacon 304 in the combination to the predicted position, or the average may be weighed in favour of combinations that feature more orthogonal vectors (i.e., further apart beacons). If location signals from close together beacons 304 are unintentionally time-correlated within the n second update window relative to further apart beacons 304 (e.g. beacons 304 arranged at one end of the area update together, and then beacons 304 arranged at the other end of the area update together), this may result in jitter or error that reduces accuracy. Fig. 5 shows an operation 500 that can be performed at least partly by the electronic device 302 in the system 300 of Fig. 3 in one embodiment of the invention. The operation 500 may be a corresponding operation that complements operation 400. In operation 500, the electronic device 302 receives a new location signal from one of the beacons 304 in 502, and updates a distance map based on the received location signal in 504. The distance map may include respective distance between the electronic device 302 and each respective beacon 304 (based on the latest location signal received from the beacon 304) and optionally times of receipt of the latest location signals. The update of the distance map in 504 may include determining, based on the newly received location signal, a distance between the electronic device 302 and the beacon 304 providing the newly received location signal, as well as replacing the previously-determined distance for that beacon 304 with the newly determined distance. The distance between the electronic device 302 and the beacon 304 can be performed using the example method disclosed herein. The operation 500 then determines a subset of three-combinations of all beacons 304 in 506 (e.g., by filtering the set of three-combinations of all beacons 304 to include only the combinations that include the beacon 304 that provides the new location signal, as discussed above), determines the location of the electronic device 302 (hence the horse and the rider) in the area by applying exponential moving average to the determined locations of each of the three-beacon combinations in 508, and outputs the determined location in 510. The determined location may be presented to the rider. The determination of the location of the electronic device 302 can be performed using the example method disclosed herein. After 508, the operation waits for an update of the distance map (e.g., which may be triggered by the receipt of a new location signal in 502) in 512, and returns to 506 when or after the distance map is updated in 504. The system 300 is further operable to perform a movement characteristic determination operation, which may correspond to the one in 202 in method 200, for determining movement characteristic of the horse. The movement characteristic may include step rate (e.g., number of steps per minute), step pattern, and / or gait of the horse (the steps correspond to hoof impacts). In the movement characteristic determination operation of this embodiment, the electronic device 302 includes an accelerometer and a gyroscope (they may be part of an inertial measurement unit) arranged to obtain movement data (includes accelerometer data and gyroscope data). The electronic device 302 is arranged to operate a high-frequency sampling process to sample the accelerometer and gyroscope data, e.g., at 50Hz to 200 Hz, to capture the movement of the electronic device 302 (hence the movement of the horse). The raw (sampled) accelerometer and gyroscope data are collected in three-dimensional space. The electronic device 302 is arranged to perform a sensor fusion operation, e.g., using a Kalman filter, on the movement data to obtain pre-processed movement data. The sensor fusion operation can help to remove gravity effects and correct for device orientation. The electronic device 302 is arranged determine the movement characteristic of the horse based on the pre-processed movement data. For example, the movement characteristic is gait and the electronic device 302 is arranged to determine the gait as one of: walk, trot, canter, or gallop, or as one of: walk, trot, or canter. For example, the movement characteristic is step pattern and the electronic device 302 is arranged to determine the step pattern as one of: a two-beat pattern, a three-beat pattern, or a four-beat pattern. The electronic device 302 is arranged to process the pre-processed movement data to identify steps (hoof impacts) and / or step pattern. For example, a peak detection algorithm may be applied to identify characteristic spikes in vertical acceleration, which corresponds to the steps. For example, a time-series analysis method such as autocorrelation or frequency domain analysis may be used to identify steps (hoof impacts) and / or step pattern. The electronic device 302 may determine the step rate and / or the step pattern based on the identified steps. For example, based on the identified steps (hoof impacts), the electronic device 302 can calculate the step rate by measuring the time intervals between the identified steps. The step rate may be expressed in steps per minute. The electronic device 302 may be further arranged to analyse the step rate and the step pattern using a model to determine the gait. The model may include a machine learning based model, such as decision tree or neural network, that has been trained on labelled datasets of different gaits, for determining a gait of the horse based on both the step rate and the pattern of steps (hoof impacts). For example, a trot might be identified by its two-beat rhythm whereas a canter would show a three-beat pattern. For example, the electronic device 302 may be arranged to process the movement data based on dynamic time warping operation to determine the gait. For example, the electronic device 302 may be arranged to process the movement data using a machine learning based model (e.g., recurrent neural network) to determine the gait. For example, the electronic device 302 may be arranged to process the movement data by performing feature extraction operation (to extract one or more features from the movement data) and applying the one or more extracted features to a classification model (e.g., machine learning based model) to determine the gait. The determination of the movement characteristic may be further based on the type, breed, and / or size of the horse. Fig. 6 shows an operation 600 that can be performed at least partly by the electronic device 302 in the system 300 of Fig. 3 in one embodiment of the invention. In operation 600, the electronic device 302 obtains movement data using the inertial measurement unit in 602, and processes the movement data to detect steps (hoof impacts) in 604, e.g., using the example processing method disclosed herein. The movement data may be pre-processed by performing a sensor fusion operation on the data before 604. Then, based on the detected steps (hoof impacts), the step rate is determined in 606 and the determined step rate is outputted in 608. Also, based on the detected steps (hoof impacts), the gait of the horse is determined (classified) in 610 and the determined gait is outputted in 612. After the gait and the step rate are determined, the operation waits for a set period of time (e.g., n milliseconds) in 614 and then returns to obtain and process further movement data in 602. The step rate and the gait of the horse may be tracked over time (as the horse traverses the area). The electronic device 302 may be arranged to determine the gait of the horse using any suitable method. The following provides three method embodiments that can be used. Other method may also be possible. In one embodiment, the method is based on the disclosure in UK patent application publication number GB2590502A, which can be adapted for use in gait determination. In the method of this embodiment, a set of training samples of identical dimension (one or more samples for each gait that needs to be classified) is obtained. The training samples may be pre-processed by filtering method and / or barycentering algorithm. The training samples may be in a simple array (for small number of samples) or in a KD-tree. The new test buffer of movement data (movement data test sample) is obtained and filtered. A lower bounding metric such as LB-Keogh may then be applied to prune nodes in the k-dimensional tree (k-d tree) or samples in the array. A full (optionally constrained) dimension-dependent dynamic time warping is then calculated based on the test sample and the non-pruned training samples to determine the warping path. If the minimum warping path exceeds the best path available so far in distance, the dynamic time warping can be ended early (e.g., before all training samples are processed). Fig. 7 illustrates a simplified example operation 700 of the method in this embodiment. In operation 700, the movement data sample is obtained, and the sample and a training sample N are processed to calculate a lower bound 1 in 702. The lower bound 1 is then processed to determine whether it is worse than the best-so-far result in 704. If yes, the training sample N will be replaced with another training sample N+1 and the process goes back to 702. If no, then the sample and the training sample N are processed to calculate a lower bound 2 in 706. The lower bound 2 is then processed to determine whether it is worse than the best-so-far result in 708. If yes, the training sample N will be replaced with another training sample N+1 and the process goes back to 702. If no, then a full (optionally constrained) dimension-dependent dynamic time warping is calculated, in 710. The result obtained is then processed to determine whether it is worse than the best-so-far result in 712. If yes, the training sample N will be replaced with another training sample N+1 and the process goes back to 702. If no, then a determination of whether further samples exist is performed in 714. If yes, the best-so-far distance is updated in 716 and the process returns to 704. If no, the sample N is returned as the result (the classified gait) in 718. Further details of the calculations and processing can be found in the disclosure of GB2590502A. In another embodiment, a bidirectional long short-term memory (LSTM) model is used to process test samples of movement data for gait classification. In yet another embodiment, a feature extraction operation is performed by processing the movement data to extract features from the data. In this embodiment, the movement data is processed to determine step times, e.g., using method such as detecting sufficient spike in the 1st or 2nd derivative of the movement data. The features extracted from the movement data may include one or more of the following: average time between steps, deviation in time between steps, number of clusters of step-types based on force direction at each step (e.g. 2 vs 4 clusters), signal magnitude area (SMA), correlation coefficients between different axes of the motion sensor (e.g., the IMU), lag in correlated peaks between different axes of the motion sensor (e.g., the IMU), etc. These features can be used to perform classification based on a classification model such as k nearest neighbour (kNN), support vector machine, random forest, etc. The system 300 is further operable to perform a status determination operation, which may correspond to the one in 206 in method 200, for determining a status of the horse based on the determined location and the determined movement characteristic. In the status determination operation of this embodiment, the electronic device 302 is arranged to determine, based on the determined location, a reference (e.g., expected) movement characteristic associated with a reference (e.g., expected) movement of the horse at the determined location. The electronic device 302 may store or access a reference movement table or map, which contains data on the location / region of the area and the reference movement characteristic of reference movement of the horse in each of the location / region. The electronic device 302 is arranged to compare the determined movement characteristic with the reference movement characteristic to determine whether the movement matches the reference movement. To this end, the electronic device 302 may determine whether the determined movement characteristic differs from the reference movement characteristic or an extent of such difference. For example, the electronic device 302 may determine whether a determined gait of the horse at a particular location differs from an expected gait of the horse at that location. For example, the electronic device 302 may determine whether a determined step pattern of the horse at a particular location differs from an expected step pattern of the horse at that location, or determine whether the difference is tolerable (by comparing with reference value / range). For example, the electronic device 302 may determine whether a determined step rate of the horse at a particular location differs from an expected step rate of the horse at that location, or determine when the difference is tolerable (by comparing with reference value / range). If it is determined that the determined movement characteristic is different from the reference movement characteristic, or such difference exceeds a threshold (e.g., by comparing with reference value or range), the electronic device 302 may determine that the movement does not match the reference movement. In response, the electronic device 302 is arranged to provide an output (audio, visual, and / or haptic output) containing a corresponding notification or instruction for facilitating performing of the reference movement, to assist the rider of the horse. In one example, the reference value / range for determining whether the difference is tolerable can be set or adjusted by a user of the electronic device 302 (e.g., by providing a user input to the device 302). The electronic device 302 is arranged to compare the determined status of the horse with a reference (expected) status of the horse to determine whether there is a mismatch, and, if a mismatch is determined to exist, provide an output (audio, visual, and / or haptic output) containing a corresponding notification or suitable response instruction, to assist the rider of the horse. For example, a mismatch exists when the determined status of the horse is that the horse has been walking whereas the reference status indicates that the horse should be running at that time and / or location in the area. For example, a mismatch exists when the determined status of the horse is that the horse has been resting whereas the reference status indicates that the horse should be moving at that time and / or location in the area. In some cases, the determined status can reflect the health state of the horse (e.g., whether it is sick). The electronic device 302 is also arranged to determine, based on the determined location, a movement routine for the horse at the determined location. The electronic device 302 may store or access a reference routine table or map, which contains data on the movement routine (expected movement such as gait, step rate, step pattern, etc.) in each of the location / region in the area. The electronic device 302 is arranged to provide an output (audio, visual, and / or haptic output) containing a corresponding notification or suitable response instruction, to assist the rider of the horse in executing the reference movement routine at the determined location. In some cases, the electronic device 302 may, based on the determined location and / or travel path of the horse, determine a location the horse will move to, and determine the upcoming movement routine for the horse at that future location, and provide corresponding output to assist the rider of the horse in executing the reference movement routine at the future location. In one application, the electronic device 302 is arranged to use the determined location (e.g., obtained in operation 204, 500) and the determined gait and / or step rate (e.g., obtained in operation 600, 700) to monitor dressage performance. To this end, the electronic device 302 may include a routine tracking module operable as a coordinator. The electronic device 302 may store a digital representation of the dressage routine, including the sequence of movements, their spatial requirements, and the expected gaits of the horse at different locations in the area. In one example, the digital representation can be implemented as a state machine, with each state representing a segment of the dressage routine. The electronic device 302 may continuously poll the for updates on the determined location of the electronic device 302 (hence the horse) in the area, and may employ geo-fencing technique to define virtual boundaries or waypoints within the area. The electronic device 302 may, based on the determined location of the electronic device 302 (hence the horse), determine that the location of the device 302 is in a predefined location / region of the area to trigger a state transition in the routine. For each state, the electronic device 302 may determine whether the rider requires guidance, based on factors such as complexity of the expected / required horse movement, the experience level of the rider (a parameter that can be input to the electronic device 302), preference for guidance verbosity, and / or detected deviations from the expected path / movement. If electronic device 302 determines that guidance is needed, it generates and provides output containing appropriate instructions to the rider. The instructions may be in the form of text-to-speech audio cues (e.g., output by the speaker of the electronic device 302), visual indicators (e.g., shown on a display of the electronic device 302 or an indicator device operably connected with the electronic device 302), or haptic feedback (e.g., provided by the electronic device 302 or by a device operably connected with the electronic device 302). The electronic device 302 may regularly check the current determined gait and / or step rate against the expected values or ranges for the current routine segment at the determined area / location. Tolerance (e.g., range) may be defined for the parameters to allow for some natural variation / deviation in the movement of the horse. These tolerances may be adjusted based on the level of a test and / or the preference of the rider. If it is determined that the determined gait or step rate differs from or deviates from an acceptable gait or step rate by a set level, a corrective feedback may be generated and provided to the rider via the electronic device 302. The corrective feedback may be in the form of text-to-speech audio cues, visual indicators, or haptic feedback, like the output of the instruction described above. The corrective feedback may include instruction to increase speed, decrease speed, transition to a different gait, or adjust a frame or balance of the horse. Fig. 8 shows an operation 800 that can be performed at least partly by the electronic device 302 in the system 300 of Fig. 3 in one embodiment of the invention. Operation 800 is for tracking a dressage routine performed by a horse in the dressage arena. In operation 800, the electronic device 302 obtains the latest determined location of the electronic device 302 (hence the horse) in the arena in 802, and performs a destination intersection test in 804 to determine whether the device 302 has moved to a different region of the arena (e.g., whether the device 302 has crossed a boundary of a defined region). If it is determined that the device 302 has moved to a different region of the arena, the routine segment (movement routine) is updated in 806. If it is determined that the device has not moved to a different region of the arena, then a determination is made in 808 to determine whether an instruction (instruction for executing the movement routine) needs to be provided to the rider. If it is determined that instruction is needed, then the electronic device 302 outputs and provides the corresponding instruction to the rider in 810. If it is determined that instruction is not needed, then device 302 awaits location update in 812 and returns to 802 when there is an update. Operation 800 also includes obtaining the determined gait and the determined step rate of the horse in 814 and 816. The determined gait and the determined step rate are processed (e.g., compare with reference value / range) to determine whether they are within tolerance in 818. If it is determined that the determined gait and / or step rate are within tolerance, then operation awaits update of the determination of the gait and / or the step rate in 822, and returns to 814 and 816. If it is determined that the determined gait and / or step rate are not within tolerance, then the electronic device 302 outputs and provides corresponding notification or instruction to the rider in 820, to assist the rider in taking suitable response or correction. Fig. 9 shows an information handling system 900 in one embodiment of the invention. The information handling system 900 can be used to perform one or more methods and one or more operations in various embodiments of the invention. For example, the information handling system 900 can be used to implement the electronic device 102, 302. For example, the information handling system 900 can be used to perform one or more of: the movement characteristic determination operation 202, the location determination operation 204, the status determination operation 206, operation 500, 600, 700, 800, etc. The information handling system 900 generally comprises suitable components necessary to receive, store, and execute appropriate computer instructions, commands, and / or codes. The information handling system 900 includes a processor 902 and a memory 904. The processor 902 may include one or more: CPll(s), MCU(s), MPll(s), TPU(s), NPU(s), GPU(s), logic circuit(s), Raspberry Pi chip(s), digital signal processor(s) (DSP), application-specific integrated circuit(s) (ASIC), field-programmable gate array(s) (FPGA), or digital circuitry / circuitries, and / or analog circuitry / circuitries. The processor 902 is configured to interpret program instructions, execute program instructions, and / or process signals / information / data. The memory 904 may include one or more volatile memory (such as RAM, DRAM, SRAM, etc.), one or more non-volatile memory (such as ROM, PROM, EPROM, EEPROM, FRAM, MRAM, FLASH, SSD, NAND, NVDIMM, etc.), or any of their combinations. Appropriate computer instructions, commands, codes, information and / or data may be stored in the memory 904. Computer instructions for executing or facilitating executing the method embodiments of the invention may be stored in the memory 904. The processor 902 and memory 904 may be integrated or separated (and operably connected). The information handling system 900 may further include one or more input devices 906. The input device 906 may include: keyboard, mouse, stylus, image scanner, microphone, tactile / touch input device (e.g., touch sensitive screen), image / video input device (e.g., camera), etc. The information handling system 900 may further include one or more output devices 908 operable to provide output (e.g., visual, audio, and / or haptic output containing notification or instruction). The output device 908 may include: display (e.g., monitor, screen, projector, etc.), speaker, headphone, earphone, haptic motor, etc. The display may include a LCD display, a LED / OLED display, or other suitable display, which may or may not be touch sensitive. The information handling system 900 may further include one or more disk drives 912 which may include one or more of: solid state drive, hard disk drive, optical drive, flash drive, magnetic tape drive, etc. A suitable operating system may be installed in the information handling system 900, e.g., on the disk drive 912 or in the memory 904. The memory 904 and the disk drive 912 may be operated by the processor 902. The information handling system 900 also includes a communication device 910 for establishing one or more communication links (not shown) with one or more external devices. The external devices may be a local signal transmitter, beacons, or computing devices such as servers, personal computers, terminals, tablet or pad-type computers, smart phones, watches, loT devices. The communication device 910 may include one or more of: a modem, a Network Interface Card (NIC), an integrated network interface, a NFC transceiver, a ZigBee transceiver, a Wi-Fi transceiver, a Bluetooth® transceiver, a radio frequency transceiver, a cellular (2G, 3G, 4G, 5G, 6G, or the like) transceiver, an optical port, an infrared port, a USB connection, or other wired and / or wireless communication interfaces. Transceiver may be implemented by one or more devices (integrated transmitter(s) and receiver(s), separate transmitter(s) and receiver(s), etc.). The communication link(s) may be wired or wireless for communicating commands, instructions, information and / or data. For example, the processor 902, the memory 904 (optionally the input device(s) 906, the output device(s) 908, the communication device(s) 910 and the disk drive(s) 912, if present) are connected with each other, directly or indirectly, through a bus, a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), an optical bus, or other like bus structure. In one embodiment, at least some of these components may be connected wirelessly, e.g., through a network, such as the Internet or a cloud computing network. A person skilled in the art would appreciate that the information handling system 900 is merely an example and that the information handling system 900 can have different configurations (e.g., include additional components, has fewer components, etc.) in other embodiments. Fig. 10 is a simplified block diagram of a beacon 1000 in one embodiment of the invention. For example, the beacon 1000 can be used to implement the beacon 304. For example, the beacon 1000 can be used to perform operation 400. The beacon 1000 may be operable to communicate signal (e.g., location signal) with another beacon or an electronic deice (e.g., electronic device 102, 302). In this embodiment, the beacon 1000 includes a processor 1002, a power control unit 1004, a timer 1006, and a communication unit 1008. The processor 1002 may include one or more: CPU(s), MCU(s), MPU(s), TPU(s), NPU(s), GPU(s), logic circuit(s), Raspberry Pi chip(s), digital signal processor(s) (DSP), application-specific integrated circuit(s) (ASIC), field-programmable gate array(s) (FPGA), or digital circuitry / circuitries, and / or analog circuitry / circuitries. The processor 902 is configured to interpret program instructions, execute program instructions, and / or process signals / information / data. The power control unit 1004 is arranged to control a power state of the beacon. For example, the power control unit 1004 may be arranged to operate the beacon 1000 at a normal power state, a reduced power state (sleep / low / hibernate state), a throttled power state. The timer 1006 is operable to time the operation of the beacon 1000 in a particular power state. The communication unit 1008 may include one or more of: a modem, a Network Interface Card (NIC), an integrated network interface, a NFC transceiver, a ZigBee transceiver, a Wi-Fi transceiver, a Bluetooth® transceiver, a radio frequency transceiver, a cellular (2G, 3G, 4G, 5G, 6G, or the like) transceiver, an optical port, an infrared port, a USB connection, or other wired and / or wireless communication interfaces. Transceiver may be implemented by one or more devices (integrated transmitter(s) and receiver(s), separate transmitter(s) and receiver(s), etc.). The communication link(s) may be wired or wireless for communicating commands, instructions, information and / or data. A person skilled in the art would appreciate that the beacon 1000 is merely an example and that the beacon 1000 can have different configurations (e.g., include additional components, has fewer components, etc.) in other embodiments. The beacon 1000 may include a housing that receives the processor 1002, the power control unit 1004, the timer 1006, and the communication unit 1008. A marker, label, or like indicator may be arranged on the housing to indicate the location the beacon 1000 is arranged to be placed in each of one or more environment. For example, a beacon arranged to be placed at location V of a standard dressage arena may have a label V. For example, a beacon arranged to be placed at location K of a small dressage arena may have a label K. For example, a beacon arranged to be placed at location V of a standard dressage arena and at location K of a small dressage arena may have a label V / K. Although not required, one or more embodiments described with reference to the Figs, can be implemented as an application programming interface (API) or as a series of libraries for use by a developer or can be included within another software application, such as a terminal or computer operating system or a portable computing device operating system. In one or more embodiments, as program modules include routines, programs, objects, components, and data files assisting in the performance of particular functions, the skilled person will understand that the functionality of the software application may be distributed across a number of routines, objects and / or components to achieve the same functionality desired herein. It will also be appreciated that where the methods and systems of the invention are either wholly implemented by computing system or partly implemented by computing systems then any appropriate computing system architecture may be utilized. This will include stand-alone computers, network computers, dedicated or non-dedicated hardware devices. Where the terms “computing system” and “computing device” are used, these terms are intended to include any appropriate arrangement of computer or information processing hardware capable of implementing the function described. It will be appreciated by a person skilled in the art that variations and / or modifications may be made to the described and / or illustrated embodiments of the invention to provide other embodiments of the invention. Thus the described and / or illustrated embodiments of the invention should be considered in all respects as illustrative and not restrictive. Some example variations and / or modifications are as follows. For example, the environment may be indoor or outdoor environment. The environment may be an arena, a stadium, a pitch, a field, etc. The environment may be a dressage arena, such as a standard dressage arena (20m x 60m) or a small dressage arena (20m x 40m). The relative locations of the beacons can be different in different environments. The equine animal is not necessarily a horse and can be a donkey, a mull, etc. The local signal provider can include three or more beacons, but it is not necessarily implemented using beacons. The beacons may be arranged to provide (e.g., broadcast) location signals using a suitable wireless communication protocol, such as ZigBee, LoRa, Wi-Fi, etc. The data processing steps in the location determination operation, the movement characteristic determination operation and / or status determination operation (e.g., operations 500, 600, 700, 800) need not be performed entirely at a single electronic device and can be performed at least partly at one or more devices (e.g., a server) operably connected with the single electronic device. It is envisaged that the system, method, and device in this disclosure can be used for monitoring other animal that is not equine animal. For example, the system, method, and device in this disclosure may be used for monitoring animal or human in a nondressage-related environment.

Claims

1. A system for determining a status of an equine animal, comprising: a local signal provider arranged in an environment; and an electronic device arranged to:receive or obtain movement data associated with movement of the equine animal in the environment;receive location signals from the local signal provider;determine a movement characteristic of the movement of the equine animal based at least in part on the movement data;determine a location of the equine animal in the environment based at least in part on the received location signals; anddetermine, based at least in part on the determined movement characteristic and the determined location, a status of the equine animal.

2. The system of claim 1, wherein the environment comprises an area over which the equine animal can traverse; andwherein the local signal provider is provided within the area and arranged to provide location signals over the area.

3. The system of claim 2, wherein the local signal provider comprises a plurality of beacons.

4. The system of claim 3, wherein the plurality of beacons are arranged around the perimeter of the area.

5. The system of any one of claims 1 to 4, wherein the environment is selected from an arena, a stadium, a pitch, or a field.

6. The system of any one of claims 1 to 5, wherein the electronic device is arranged to:compare the determined status of the equine animal with a reference status of the equine animal; andprovide an output to indicate a mismatch if it is determined that the determined status does not match the reference status.

7. The system of any one of claims 1 to 6, wherein the electronic device is arranged to:determine, based at least in part on the determined location, a reference movement characteristic associated with a reference movement of the equine animal at the determined location;compare the determined movement characteristic with the reference movement characteristic to determine whether the movement matches the reference movement; andprovide an output to facilitate performing of the reference movement if it is determined that the movement does not match the reference movement.

8. The system of any one of claims 1 to 7, wherein the electronic device is arranged to:determine, based at least in part on the determined location, a movement routine for the equine animal at the determined location; andprovide an output to facilitate execution of the movement routine.

9. The system of any one of claims 3 to 8,wherein the plurality of beacons comprises at least three beacons; andwherein the electronic device is arranged to:receive respective location signal from at least three of the beacons in the environment, each respective location signal comprises an identifier of the corresponding beacon; andperform a trilateration based localisation operation based at least in part on the received location signals to determine the location of the equine animal in the environment.

10. The system of claim 9, wherein the electronic device is arranged to:perform the trilateration based localisation operation each time after a further location signal is received from one of the beacons in the environment, so as to update the location of the equine animal in the environment.

11. The system of claim 9 or 10, wherein the trilateration based localisation operation comprises, for each of at least three most recently received location signals received from different beacons:determining, based at least in part on the corresponding identifier and a strength of the received location signal, a respective estimated distance between the equine animal and the corresponding beacon; anddetermining, based at least in part on the estimated distances and information associated with locations of the beacons in the environment, the location of the equine animal in the environment.

12. The system of any one of claims 9 to 11, wherein the trilateration based localisation operation comprises:identifying, based at least in part on the identifier in the most recently received location signal, the corresponding beacon;determining, from the beacons, all three-beacon sets each respectively consists of a combination of the identified beacon and any two other beacons;determining, for each of respective three-beacon set, a respective estimated location of the equine animal in the environment and a respective mean received time of the signals used for determining the corresponding estimated location; and determining, based at least in part on the estimated locations and the mean received times of all the all three-beacon sets, the location of the equine animal in the environment.

13. The system of claim 12, wherein the trilateration based localisation operation comprises:determining a weighted average of the estimated locations to determine the location of the equine animal in the environment14. The system of claim 13, wherein the determination of the weighted average of the estimated locations comprises:weighting the estimated locations based at least in part on the mean received times; andaveraging the weighted estimated locations to determine the location of the equine animal in the environment.

15. The system of claim 13, wherein the determination of the weighted average of the estimated locations comprises:weighting the estimated locations based at least in part on proximity measures of the three-beacon sets, the proximity measure of each respective three-beacon set indicates how close the corresponding three beacons are arranged in the environment; andaveraging the weighted estimated locations to determine the location of the equine animal in the environment.

16. The system of any one of claims 1 to 15, wherein the electronic device comprises a motion sensor arranged to obtain the movement data.

17. The system of claim 16,wherein the motion sensor comprises an accelerometer and a gyroscope;wherein the movement data comprises accelerometer data and gyroscope data; andwherein the electronic device is arranged to:perform a sensor fusion operation on at least the accelerometer data and the gyroscope data to obtain processed movement data; anddetermine the movement characteristic of the equine animal based at least in part on the processed movement data.

18. The system of any one of claims 1 to 17, wherein the movement characteristic comprises step rate, step pattern, and / or gait.

19. The system of claim 18,wherein the electronic device is arranged to determine the gait as one of: walk, trot, canter, or gallop; and / orwherein the electronic device is arranged to determine the step pattern as one of: a two-beat pattern, a three-beat pattern, or a four-beat pattern.

20. The system of any one of claim 18 or 19, wherein the electronic device is arranged to:process the movement data to identify steps taken by the equine animal in the movement;determine the step rate and / or the step pattern based at least in part on the identified steps; andprocess at least the step rate and the step pattern using a model to determine the gait.

21. The system of claim 18, wherein the electronic device is arranged to:compare the movement data with each of a plurality of sets of reference movement data by applying dynamic time warping to determine a respective similarity measure between the movement data and the corresponding reference movement data, each of the plurality of sets of reference movement data is associated with a respective gait; anddetermine the gait based at least in part on the similarity measures.

22. The system of claim 18, wherein the electronic device is arranged to: process the movement data using a machine learning based model to determine the gait.

23. The system of claim 18, wherein the electronic device is arranged to: extract one or more features from the movement data; andapply the one or more extracted features to a classification model to determine the gait.

24. An electronic device configured for use in the system of any one of claims 1 to 23, comprising:a motion sensor arranged to obtain movement data associated with movement of the equine animal in an environment;a communication unit arranged to receive location signals from a local signal provider arranged in the environment; andone or more processors arranged to:determine a movement characteristic of the movement of the equine animal based at least in part on the movement data;determine a location of the equine animal in the environment based at least in part on the received location signals; anddetermine, based at least in part on the determined movement characteristic and the determined location, a status of the equine animal.

25. A method for determining a status of an equine animal, comprising:receiving movement data associated with movement of the equine animal in an environment, the movement data being obtained by a motion sensor;determining a movement characteristic of the movement of the equine animal based at least in part on the movement data;determining a location of the equine animal in the environment based at least in part on location signals received from a local signal provider arranged in the environment; anddetermining, based at least in part on the determined movement characteristic and the determined location, a status of the equine animal.31

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