Equine Wearables and Activity Sensors Explained

What equine wearables are designed to measure

Equine wearables are electronic devices worn by a horse, rider or piece of tack to record movement, physiological signals, location or environmental conditions. The device may store data locally, transmit it to a phone or gateway, or send it to a cloud platform for processing. In practical terms, the product is not just the hardware: it is a chain consisting of sensors, attachment method, firmware, algorithms, communications, app design and the interpretation placed on the resulting measurements.

The most common applications in Great Britain are training analysis, activity monitoring, remote observation, yard management, research and the collection of evidence to support discussions with veterinary or performance professionals. Wearables can make patterns visible that are difficult to estimate by eye, such as the duration of each gait, speed over a route, changes in daily movement or recovery of heart rate after exercise. They do not, by themselves, establish a diagnosis, prove soundness or replace skilled observation.

The distinction between a measurement and an interpretation is central. An accelerometer records changes in motion; software may convert those signals into “steps”, “trot minutes” or an activity score. An ECG records electrical cardiac activity; an app may display heart rate, rhythm events or recovery metrics. Each additional conversion introduces assumptions and possible error.

The principal sensor types

Accelerometers and inertial measurement units

An accelerometer measures acceleration, usually along three perpendicular axes. A triaxial device can detect changes in movement in three dimensions, while an inertial measurement unit (IMU) commonly combines accelerometers with gyroscopes and sometimes a magnetometer. Gyroscopes measure angular velocity; magnetometers estimate orientation relative to the Earth’s magnetic field.

These sensors are used to classify activity and gait, count movement cycles, estimate stride-related variables and identify periods of relative inactivity. Their raw output is a time series rather than a direct reading of “walk”, “trot” or “sleep”. Algorithms look for recurring patterns, frequencies, amplitudes and orientation changes. The quality of the classification depends on the sensor’s position, sampling frequency, attachment stability, the horse’s conformation and the activity being performed.

Research has demonstrated that placement matters. A peer-reviewed study found that an accelerometer mounted on a horse’s front leg produced more accurate step counts than alternative locations in the tested conditions, while horse height affected canter-related thresholds. This is a useful warning against treating a software result as location-independent: an algorithm trained on a leg-mounted sensor cannot automatically be assumed to perform equivalently when the device is placed on a girth, surcingle, headcollar or rider.

GPS and GNSS

GPS is part of the wider satellite-navigation family usually described as GNSS. A receiver estimates position from signals transmitted by satellites. Wearables use this information to calculate route, distance, speed, direction and sometimes altitude. A device may record a position periodically rather than continuously, in order to reduce battery consumption.

GPS is valuable for hacking routes, turnout movement, gallop work and outdoor training. It is less reliable indoors, under dense tree cover, beside buildings or where satellite reception is obstructed. Speed is normally derived from successive position fixes and can become unstable when fixes are sparse or noisy. A straight-looking track on an app is therefore not proof that every point was measured accurately.

GNSS data also creates a privacy issue. A route may reveal a rider’s home, a private yard, a competition venue or a business’s operating pattern. The Information Commissioner’s Office treats location information and data generated by connected objects as potentially personal data where an individual can be identified. Businesses should therefore consider access controls, retention periods, sharing permissions and whether precise location is genuinely necessary for the service.

Heart-rate and ECG sensors

Equine heart monitoring may use electrodes to record an electrocardiogram, or optical methods such as photoplethysmography (PPG), which detects changes in light absorption associated with blood volume. ECG is a direct electrical signal and is generally the more appropriate technology when rhythm information is important. Optical systems can be attractive because they may be easier to package, but their performance can be affected by movement, contact pressure, hair, sweat, dirt and placement.

Heart rate is useful for describing exercise intensity and recovery within the same horse, provided the measurements are collected consistently. Heart-rate variability is more demanding: it depends on accurate beat-to-beat timing and is sensitive to artefacts, movement and the analysis method. A headline “stress” or “recovery” score may combine heart rate with activity and proprietary assumptions. Buyers should ask what is actually recorded, at what sampling rate, how artefacts are removed and whether the output has been compared with a reference instrument.

The British Horseracing Authority’s work on in-race heart monitoring illustrates both the potential and the caution required. Its pilot involved a bespoke ECG-based device fitted through a surcingle and pad, with the data intended to complement existing knowledge and support research. The BHA has explicitly described the project as an evaluation of whether the equipment records reliable data under race conditions, not as proof that a wearable can independently determine a horse’s clinical status.

Temperature, respiration and other biosignals

Temperature sensors may measure the device’s local temperature, the skin surface or, in specialised systems, a body temperature proxy. These are not interchangeable. A sensor exposed to air, sunlight, sweat or a wet coat can change temperature without reflecting core body temperature.

Respiration may be estimated from movement, sound, pressure, airflow or changes in a sensor’s orientation. Emerging research has demonstrated wearable platforms capable of recording respiratory and cardiac signals, but a research prototype tested on a treadmill is not automatically equivalent to a durable commercial product used in a field, stable or lorry.

Other sensors include barometers for elevation change, microphones for sound patterns, pressure sensors in tack or pads, and ambient temperature or humidity sensors. Each can be useful, but the published specification should make clear whether the device measures the horse, the equipment or the surrounding environment.

How raw sensor data becomes an equine metric

A typical workflow has several stages:

1. Sampling: the sensor records values at a stated frequency, such as acceleration or electrical potential over time.
2. Pre-processing: firmware may filter noise, remove obvious artefacts and compress the data.
3. Feature extraction: software identifies signal characteristics such as peaks, frequency bands, changes in orientation or intervals between heart beats.
4. Classification or estimation: an algorithm labels periods as walk, trot, canter, rest or another category, or estimates distance, speed or steps.
5. Presentation: the app displays graphs, summaries, alerts, scores or comparisons with previous sessions.

Filtering is necessary because horses, tack and riders generate complex motion. However, filtering can also remove clinically or performance-relevant detail, or create a visually smooth output that appears more certain than the underlying signal. A responsible system should distinguish missing data, low-confidence data and a genuine zero. “No reading” is not the same as “no movement”.

Machine-learning models introduce another consideration: generalisation. A model trained on a small group of horses may perform well in the original study but less well on different breeds, heights, disciplines, surfaces, sensor positions or speeds. A recent rider-worn accelerometer study achieved strong gait-classification performance in its experimental conditions, while also noting that position, sampling frequency and the length of the analysis window materially affected results. Such findings support the value of validation, but do not justify universal accuracy claims.

What the main metrics can tell you

Placement, fit and construction

Attachment is part of the measurement system. A sensor that rotates, slips, rattles or is compressed inconsistently can produce a different signal from the same horse on another day. The manufacturer should specify the intended location, orientation, permitted tack combinations and any restrictions on use during turnout, washing, transport or ridden work.

Common mounting approaches include a leg band, surcingle or girth attachment, saddle-pad pocket, headcollar or bridle fitting, and a rider-worn unit. Leg-mounted products can capture local limb movement effectively but must be designed so that straps, edges and housings do not create avoidable rubbing, pressure or entanglement risks. A device fitted beneath tack must not create a hard pressure point or alter the fit of a saddle, girth or pad.

Materials should be assessed for cleanability, flexibility, abrasion resistance, sweat and water exposure, and the behaviour of fasteners under repeated use. “Water-resistant” is not a sufficiently precise performance claim. Buyers should look for an ingress-protection rating where relevant, instructions for charging and cleaning, and clear warnings about immersion, pressure washing, solvents and disinfectants. A robust enclosure does not make a poorly fitted strap safe.

Weight and profile matter more than marketing photographs suggest. The device should be small and stable enough not to interfere with natural movement, tack function or the horse’s behaviour. The horse should be observed after fitting, during the intended activity and after removal. Any sign of rubbing, repeated attempts to dislodge the equipment, altered stride, guarding or behavioural disruption warrants stopping the test and reassessing the setup.

Battery, communications and data handling

Battery claims should state the conditions behind them. Recording frequency, GPS use, cellular transmission, temperature, notifications and screen activity can all change runtime. A product that lasts several weeks in low-power daily monitoring may last only a fraction of that during continuous GPS and live heart-rate transmission.

Connectivity may use Bluetooth to a phone or gateway, Wi-Fi at the yard, cellular networks or a combination. Bluetooth-only products can be suitable for a training session but may not support remote monitoring when the horse is beyond phone range. Cellular systems can operate independently but introduce subscription costs, network coverage limitations and another point of failure. Offline storage is important where a ride passes through poor coverage.

Check how data is exported. A screenshot is not the same as a downloadable raw file. For professional use, useful questions include whether the service provides timestamps, sensor position, units, sampling information, confidence indicators, event annotations and an audit trail for edited records. A business buying equipment for multiple horses should also establish user roles, account ownership, data retention, service continuity and what happens if the supplier changes or closes the platform.

For GB businesses, wireless products placed on the market fall within the Radio Equipment Regulations 2017. Government guidance explains that manufacturers must address safety, electromagnetic compatibility and efficient spectrum use, prepare technical documentation and provide conformity information. A CE or UKCA mark is evidence of a relevant conformity route; it is not evidence that an equine algorithm is accurate or clinically validated.

Validation: the questions that separate evidence from claims

“Clinically validated”, “AI-powered”, “research-backed” and “up to 95% accurate” are not meaningful without context. A serious technical evaluation should describe:

• the reference method, such as video annotation, a laboratory ECG or a professional-grade GPS unit;
• the number and characteristics of horses, including height, breed, discipline and experience;
• the sensor location and attachment method;
• the surfaces, speeds, gaits and environmental conditions tested;
• the definition of success, such as accuracy, sensitivity, specificity, mean error or agreement;
• whether the test used independent horses not present in model training;
• the amount of missing, rejected or low-quality data; and
• the conditions under which the claim should not be relied upon.

Peer-reviewed research on equine activity monitoring has shown that wireless accelerometers can quantify locomotor activity in experimental settings. That is encouraging, but a controlled study does not establish performance across every yard, discipline and season. Likewise, a manufacturer’s technical documentation may be the best primary source for battery life, enclosure rating or communications protocol, but it is not necessarily independent evidence of the biological validity of a derived score.

Look for repeatability as well as accuracy. If the purpose is monitoring change within one horse, consistent placement and consistent routine may be more valuable than a universal comparison with other horses. Establishing a baseline under ordinary conditions can help identify deviations, but the baseline itself may shift with weather, turnout, workload, travel, tack, rider and management.

Selection guide for owners, yards and manufacturers

For horse owners

• Define the decision the device is meant to support before comparing features.
• Choose the least complicated sensor that can answer that question reliably.
• Ask where it must be fitted and whether the position is compatible with your tack and routine.
• Check battery, charging, subscription, offline storage and export arrangements.
• Read the cleaning, water, temperature and safety instructions.
• Use trend data and context, not a single score, to decide when a human check is needed.

For yards and equestrian businesses

Prepare a written operating procedure covering fitting, charging, cleaning, identification of horses, data access, staff training and escalation. If staff movement or rider location is recorded, explain the purpose and handle the information transparently. Location and telemetry may be personal data, and the ICO advises organisations to limit collection to what is necessary, provide suitable controls and consider privacy risks.

Do not allow dashboards to become a substitute for stable management. A device can fail because of a flat battery, detached mount, poor contact, bad satellite reception or software outage. Human observation, routine records and appropriate professional assessment remain essential parts of the system.

For manufacturers and importers

Design around the horse’s welfare and the real attachment environment rather than adapting a generic human fitness device. Publish the intended use, contraindications, sensor position, accuracy conditions, data-quality flags and update policy. Maintain traceable firmware and algorithm versions so that a change in software does not silently invalidate historical comparisons.

Manufacturers should also separate product compliance from performance validation. Radio conformity, electrical safety and electromagnetic compatibility are necessary market requirements, but they do not demonstrate that a gait classifier, recovery score or lameness-related alert is fit for a particular decision.

Common misconceptions

“More data always means better welfare decisions”

More data can expose patterns, but it can also create false confidence, alert fatigue and excessive attention to metrics that are easy to display rather than important to the horse. A small number of well-defined measurements, collected consistently and interpreted with context, may be more useful than a large dashboard of opaque scores.

“The wearable can detect lameness or diagnose illness”

Some systems may identify movement asymmetry or unusual activity and can be useful as screening or communication aids. That does not make them diagnostic instruments. A change in a metric should prompt appropriate observation and, where warranted, professional advice rather than an automatic conclusion about cause.

“A heart-rate number is objective, so it needs no interpretation”

Heart rate is a physiological measurement, but its meaning depends on exercise intensity, excitement, heat, fitness, pain, recovery stage, medication, measurement quality and the individual horse. A clean number can still be misinterpreted.

“A research paper proves the commercial product works everywhere”

The paper may concern a different sensor, software version, attachment position or population. Evidence should be mapped to the precise product and intended use. Independent validation under representative field conditions is stronger than a general reference to academic work.

Integrating wearables into digital equestrian technology

Wearables work best as one layer in a broader information system. Useful adjacent records include training plans, farriery dates, bodyweight or condition observations, tack changes, turnout, feed changes, travel, competition results and veterinary records. Linking these datasets can reveal context, but it also increases the need for disciplined data governance and clear ownership.

A practical approach is to begin with a baseline period, document unusual events, review data quality and agree in advance what constitutes a meaningful change. The goal is not to outsource horsemanship to a graph. It is to improve the timing and quality of questions asked by the owner, trainer, yard manager, researcher or veterinary professional.

For the GB market, the strongest products are likely to be those that are technically transparent, physically unobtrusive, properly validated for their stated use and honest about uncertainty. The most valuable feature is not necessarily the greatest number of sensors. It is a trustworthy connection between a well-defined measurement and a responsible equestrian decision.

Sources and further reading

• British Horseracing Authority: heart-rate monitor pilot under Rules of Racing
• British Horseracing Authority: wearables on the racecourse
• Journal of Equine Veterinary Science: Location on the Body of a Wearable Accelerometer Affects Accuracy of Data for Identifying Equine Gaits
• PubMed: Evaluation of a wireless activity monitoring system to quantify locomotor activity in horses
• Peer-reviewed study: Detecting Equine Gaits Through Rider-Worn Accelerometers
• Peer-reviewed study: Wearable smart textile band for continuous equine health monitoring
• UK Government: Radio Equipment Regulations 2017—Great Britain
• Information Commissioner’s Office: What information do IoT products use?
• Information Commissioner’s Office: Right to data portability
• British Horse Society: Horses in Society and the Five Domains approach to welfare

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