Data Quality and Limitations in Equine Sensor Technology

Equine sensor technology is now used to record movement, speed, stride characteristics, heart rate, respiratory rate, temperature, location, activity and behaviour. Some systems are designed for training feedback; others support research, yard management or veterinary assessment. The common attraction is continuity: a wearable or camera can collect observations for longer and more consistently than a person who is watching intermittently.

However, the presence of a number, graph or traffic-light score does not establish that the underlying measurement is accurate, clinically meaningful or suitable for the decision being made. Sensor outputs are estimates produced by a chain of hardware, attachment, signal processing, algorithms, software interfaces and human interpretation. Weakness at any stage can reduce data quality.

For equestrian use, the central question is therefore not simply “How accurate is this device?” It is “Accurate for what variable, under which conditions, compared with what reference method, and for which horse or population?” A system that is useful for comparing one horse’s routine over time may be unsuitable for diagnosing disease or comparing results between horses, yards or devices.

What counts as an equine sensor?

A sensor converts a physical or biological phenomenon into a signal that can be recorded. Equine systems commonly combine one or more of the following:

• Inertial measurement units (IMUs): accelerometers measure linear acceleration and gyroscopes measure angular velocity. Some units also include magnetometers. They may be fitted to the head, poll, pelvis, limbs, girth, saddle or rider.
• Cardiac sensors: chest belts and electrode systems detect electrical activity associated with the heartbeat. Consumer systems often calculate heart rate from detected beat intervals rather than recording a full diagnostic electrocardiogram.
• Location and speed systems: GNSS/GPS receivers estimate position and movement using satellite signals. Accuracy can change with satellite geometry, obstructions, update rate and the algorithm used to smooth the track.
• Optical and computer-vision systems: cameras analyse posture, locomotion, facial movement, activity or behaviour. These can be contact-free but depend on lighting, camera position, visibility and the training data used by the software.
• Thermal, acoustic and environmental sensors: infrared cameras, microphones, humidity sensors and other devices may estimate skin temperature, respiratory sounds, breathing patterns or environmental exposure.
• Smart textiles and pressure systems: conductive fabrics, pressure mats or force-sensitive elements may be used to estimate thoracic expansion, saddle pressure or interaction between horse and rider.

The sensor itself is only one component. A complete system may include analogue-to-digital conversion, wireless transmission, cloud storage, filtering, event detection, machine-learning classification and a mobile application. In practical terms, “data quality” must be assessed across the whole measurement system rather than the small component advertised on the product page.

Measurement quality: the terms that matter

Accuracy, precision and agreement

Accuracy describes closeness to a reference or accepted value. Precision describes how tightly repeated measurements cluster, whether or not they are close to the reference. A device can be precise but biased: it may give almost the same answer every time while consistently overestimating or underestimating the variable.

Repeatability concerns repeated measurements under the same conditions. Reproducibility concerns agreement when conditions change, for example between horses, operators, devices, surfaces or days. Agreement is not the same as correlation. Two systems can rise and fall together and therefore correlate well while still differing materially in absolute terms.

These distinctions are important in equestrian practice. A movement monitor may reliably identify that one session was more active than another without measuring absolute distance accurately. An IMU may detect an asymmetry pattern consistently while not agreeing closely with a force plate or expert clinical assessment. A heart-rate belt may be adequate for training-zone feedback but not equivalent to a clinical ECG.

Validity and validation

Validity asks whether the system measures the construct it claims to measure. A sensor can record acceleration, but acceleration is not itself lameness, pain, welfare or training readiness. The latter are interpretations that require evidence connecting the signal to the target outcome.

Validation should be specific. A credible study normally states the horse population, number of horses, age and type, equipment placement, gait or activity, surface, environmental conditions, reference method, outcome measures and statistical approach. A result obtained on a small group of sound horses in a controlled arena should not automatically be generalised to varied breeds, disciplines, surfaces or clinical cases.

Recent equine reviews consistently identify a gap between promising prototypes and field-ready evidence. IMU systems have shown useful performance in gait analysis, but published work also reports differences between sensor outputs and subjective clinical assessment. The appropriate conclusion is that such systems can strengthen objective observation, not that they replace veterinary examination or professional judgement.

Why field conditions reduce data quality

Attachment and placement

Placement is part of the measurement method. A device mounted a few centimetres away from the validated position may record a different movement pattern. Loose straps introduce relative motion; excessive tension can alter comfort and movement. Hair, sweat, rugs, tack, conformational variation and coat changes can all affect contact or stability.

For an IMU, orientation matters because the algorithm must interpret axes consistently. Rotation of the unit can change the relationship between recorded acceleration and the horse’s anatomical planes. A system may compensate for some placement variation, but the user should not assume that it compensates for all of it.

Heart-rate systems are especially vulnerable to poor electrode contact, movement artefact and intermittent signal. Sweat may improve electrical contact in some circumstances but can also contribute to slippage, contamination or unstable contact. A clean-looking trace is not proof that every beat has been detected correctly.

Motion artefact

Motion artefact is unwanted signal generated by movement of the sensor, strap, cable or electrode rather than by the physiological event of interest. In a moving horse, the problem is unavoidable to some degree. The system must distinguish the heartbeat from body movement, or respiratory expansion from movement of the girth and tack.

Algorithms may remove apparent artefacts, but filtering can also remove genuine events or create a smoother-looking signal than the raw data justify. Users should ask whether raw data, signal-quality indicators and excluded periods are available. A single summary score can conceal substantial data loss.

Environment

Equestrian data are collected in unusually variable conditions: indoor arenas, bright sunlight, darkness, rain, dust, mud, vegetation, fences, reflective surfaces, different footing, gradients and crowded warm-up areas. Computer vision can be affected by poor illumination, occlusion by other horses or riders, and changes in camera position. GNSS can be degraded by trees, buildings, indoor use or multipath reflections.

Temperature and humidity influence both the horse and the device. Skin temperature is affected by ambient conditions, circulation, sweating, hair coat, contact pressure and the measured body region. It should not be treated as a direct substitute for core body temperature. Infrared methods may be useful in defined settings but can become unreliable when environmental conditions or movement change.

Horse, rider and tack effects

Equine movement is not produced by the horse alone. Rider balance, rein contact, saddle fit, girth tension, fatigue, footwear, training surface and speed can all influence the signal. A change in a movement metric may represent a change in the horse, the rider, the equipment or the interaction between them.

That does not make the data useless. It means that interpretation should include context. A useful training record might include horse identity, rider, tack, surface, direction, gait, speed, weather, session duration and unusual events. Without these metadata, a later user may be unable to distinguish a genuine change from a change in test conditions.

Common data-quality problems in equine systems

Quality control should therefore include completeness checks, signal-quality flags, time synchronisation, calibration records and an audit trail of software versions. For commercial or research use, the original data should be retained where lawful and practical, rather than keeping only an interpreted score.

Sensor readings are not automatically welfare measures

Welfare is multidimensional. It includes physical health, behaviour, affective experience, environmental conditions and the horse’s ability to cope. A single variable such as activity, heart rate or time lying down cannot represent the whole of welfare.

For example, increased heart rate may reflect exercise, excitement, heat, pain, fear or anticipation. Reduced movement may indicate rest, fatigue, illness, restricted space or a sensor that has stopped recording. A behaviour classifier may identify a head position without establishing why the horse adopted it.

The same caution applies to claims about stress, pain or emotion. A model may identify patterns associated with a labelled state in a research dataset, but the output remains probabilistic and context-dependent. It should not be presented as a direct reading of the horse’s subjective experience unless the evidence supports that interpretation.

Automated systems are best used as part of a broader observation process: they can make change more visible, provide prompts for closer inspection and create a record for discussion with suitably qualified professionals. They should not be used to dismiss observations that conflict with the dashboard.

Interpreting trends responsibly

Longitudinal comparison is often the strongest practical use of consumer equine technology. Repeated measurements from the same horse, using the same device, placement, rider and protocol, can reveal a meaningful departure from that horse’s usual pattern. This is different from claiming that the value is universally “normal” or “abnormal”.

Before acting on a trend, check:

• Was the device worn correctly and in the validated position?
• Was the recording complete, and does the system show signal quality?
• Did the horse, rider, tack, surface, speed, weather or routine change?
• Is the change larger than the device’s stated or observed measurement variability?
• Has the software or firmware changed?
• Is the metric directly measured, derived from another signal, or inferred by a machine-learning model?
• Is the result being used for training description, welfare monitoring, research, or a health decision?

Repeated small changes may be more informative than an isolated extreme value, but only if the measurement process is stable. Conversely, a stable average can conceal short events that matter. The appropriate sampling interval depends on the question: daily activity, stride asymmetry, heart-rate recovery and behaviour require different time resolutions.

How to assess a product before purchase

Ask what is actually measured

Manufacturers should distinguish sensor measurements from calculated or inferred outputs. “Acceleration”, “beat interval”, “position” and “skin temperature” are measurements or near-measurements. “Balance”, “effort”, “stress”, “readiness” and “lameness risk” are interpretations that require an evidential chain. The product documentation should explain that chain in plain language.

Look for useful validation detail

Strong technical documentation identifies the reference method, test conditions, sample size, inclusion criteria and limitations. A headline percentage without a confidence interval, error range or description of the test population is difficult to interpret. Independent peer-reviewed validation is preferable to an unqualified claim based only on internal testing.

Check operational robustness

• How is correct placement shown and checked?
• What happens when the signal is poor or data are missing?
• Is raw data export available?
• Are timestamps, units and sampling rates documented?
• Can the user see firmware and algorithm changes?
• What are the battery, charging, waterproofing and temperature specifications?
• What happens to data if the subscription ends?
• Can multiple horses and users be separated reliably?
• Is there a documented calibration or quality-control procedure?

A product that reports uncertainty and displays “insufficient quality” may be more trustworthy than one that always produces a confident score. In safety-critical or welfare-sensitive contexts, a transparent limitation is a quality feature.

Data governance and professional use in Great Britain

Equine sensor records may include personal data when they are linked to an identifiable owner, rider, employee, client, yard or location. Businesses should establish who owns or controls the data, who can view it, how long it is retained, whether it is shared with manufacturers or third parties, and how it can be exported or deleted. Contractual arrangements should be clear where a yard collects data from horses belonging to different clients.

Data security also matters operationally. Location histories can reveal when premises are occupied, competition schedules or transport patterns. Access controls, strong authentication, software updates and sensible retention periods reduce avoidable risk. These governance questions are separate from measurement accuracy, but poor governance can still undermine confidence in a technology programme.

Where a sensor is marketed in connection with diagnosis, treatment or clinical decision-making, purchasers should establish its intended regulatory status and the role of a veterinary professional. A training or welfare dashboard should not be represented as a veterinary diagnostic instrument unless the manufacturer can substantiate that claim and the relevant requirements have been met.

What good practice looks like

For owners and riders, the most defensible approach is to establish a baseline during ordinary, well-documented sessions; use the same equipment and routine where possible; record relevant context; inspect obvious artefacts; and treat unusual outputs as prompts for observation rather than conclusions.

For yards and equestrian businesses, introduce written procedures for fitting, charging, cleaning, device allocation, data access and incident reporting. Staff should know how to recognise a failed recording and how to escalate an observation without relying on the device to make a diagnosis.

For manufacturers, publish validation against an appropriate reference, disclose the tested population and operating envelope, preserve raw or minimally processed data where possible, report uncertainty, document algorithm updates and test performance under realistic field conditions. Independent replication and transparent negative findings are valuable indicators of maturity.

For researchers and professionals, distinguish analytical validity from clinical or welfare utility. A statistically significant association may have little practical value if the effect is smaller than normal measurement variation. Conversely, a modest but repeatable signal may be useful as an early prompt when integrated with other information.

Conclusion

Equine sensors are neither inherently reliable nor inherently unreliable. Their value depends on the measurement question, the design of the device, the quality of attachment and recording, the validation evidence, the transparency of the algorithm and the competence of the person interpreting the result.

The most credible use of sensor technology is cumulative and contextual: objective measurements are combined with direct observation, training records, environmental information and appropriate professional assessment. The least credible use is to treat a single unqualified score as an unquestionable statement about a horse’s health, welfare or performance.

In an increasingly digital equestrian sector, data quality should be treated as part of horse-care quality. Better sensors are useful, but better questions, better protocols and more honest communication about uncertainty are just as important.

Sources and further reading

• Inertial Sensor Technologies—Their Role in Equine Gait Analysis: A Review, Animals, 2023.
• Technologies for equine welfare and performance monitoring under field conditions—Where do we stand?, Equine Veterinary Journal, 2026.
• Impact of the technology to monitor horse behaviour and health: a scoping review, Journal of Equine Veterinary Science, 2025.
• Activity Time Budgets—A Potential Tool to Monitor Equine Welfare?, peer-reviewed review article.
• ISO/TR 13587:2012: Three statistical approaches for the assessment and interpretation of measurement uncertainty, International Organization for Standardization.
• ISO/TS 22704:2022: Uncertainty of measurement and evaluation, International Organization for Standardization.
• UK GDPR guidance and resources, Information Commissioner’s Office.

Related research

Connected Stable Monitoring SystemsContinue research →GPS Tracking Technologies in Equestrian UseContinue research →Equine Wearables and Activity Sensors ExplainedContinue research →