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Most earbuds that estimate heart rate use optical photoplethysmography (PPG): LEDs shine light into the ear, a photodiode measures changing reflected light as blood volume rises and falls with each pulse, and software converts that waveform into beats per minute. The approach can be useful for fitness tracking, but accuracy depends on the exact model, fit, activity, exercise intensity and signal-quality filtering. It is not an ECG, and no single accuracy figure applies to every earbud.
What an earbud heart-rate sensor actually measures
An in-ear monitor estimates pulse rate from blood-flow changes rather than recording the heart’s electrical activity. An ECG detects electrical signals through electrodes; an earbud’s PPG system observes how blood volume changes alter the absorption and reflection of light in tissue. Because the measurement is optical and indirect, the result is best understood as an estimate of beats per minute.
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The ear can be a useful measurement site because the sensor can sit against tissue in or near the ear canal, where a stable fit may be possible during exercise. That does not make every in-ear design equally reliable. Sensor geometry, ear anatomy, insertion depth, contact pressure and movement all affect the optical signal.
How the optical system turns light into beats per minute
- Illumination: One or more LEDs emit light toward the ear canal or nearby tissue. The exact wavelength and optical arrangement vary by product.
- Blood-volume modulation: Each heartbeat changes the amount of blood in the illuminated tissue. Blood absorbs some of the emitted light, so the amount of light returned to the sensor changes rhythmically.
- Detection: A photodiode records those changes in reflected light as a continuous waveform.
- Signal processing: Algorithms identify the repeating pulse pattern, reject implausible intervals and calculate a heart-rate estimate in beats per minute.
- Motion and quality checks: Accelerometer or gyroscope data can help identify movement noise. Some devices also score signal quality and suppress readings that do not meet a reliability threshold.
A clinical evaluation of the cosinuss One describes this arrangement as a PPG element combining an LED and photodiode, with green light emitted into the ear canal and an accelerometer used alongside it. In that study, applying the device’s signal-quality indicator improved agreement with ECG because unreliable readings were excluded. The result demonstrates the value of quality filtering for that device and setting; it does not establish that every earbud uses green light, the same hardware or the same algorithm. Read the clinical study.
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Why fit and movement can change the number
Mechanical fit
PPG needs a reasonably stable optical path. If an earbud shifts, the angle between the LED, tissue and photodiode changes. A loose seal can introduce outside light; excessive pressure can also alter the local tissue conditions. Correct tip size and placement therefore matter as much as the nominal sensor specification.
Motion artefacts
Running, cycling out of the saddle, arm movement and vibration can create changes in the optical signal that resemble a pulse or obscure real beats. Motion sensors help software identify this contamination, but they cannot remove every error. A 2019 graded-cycling study of two in-ear devices found mean absolute percentage error of 5% or less under controlled conditions, while also reporting slight underestimation and sensitivity to motion artefacts. Its conclusion was that accuracy and precision depended on measurement location, stress situation and exercise. See the 2019 study record.
Exercise intensity
At higher heart rates, pulses arrive closer together and movement is often more vigorous. Both conditions can make individual beats harder to separate. The available validation studies show that a device can have a small average error while its readings become more dispersed during harder efforts.
Signal-quality filtering
A displayed value may represent a filtered estimate rather than every raw sample. Filtering can improve agreement by dropping low-quality windows, but it may also leave gaps or delay an update when the signal is poor. A device that reports fewer values during vigorous motion is not necessarily failing; it may be refusing to present a low-confidence estimate.
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What published accuracy studies found
Results are model- and protocol-specific. The following studies used chest-strap or ECG comparators and should not be treated as a universal ranking of all earbuds.
| Device and study | Participants and protocol | Reported result | How to interpret it |
|---|---|---|---|
| AirPods Pro 3; Doherty et al., PLOS Digital Health, 2026 | 40 healthy adults; rest and progressive treadmill exercise; 16,735 paired five-second epochs compared with a Polar H10 chest strap | Mean bias −0.03 bpm; mean absolute error 2.08 bpm; mean absolute percentage error 2.02%; 95% limits of agreement −10.27 to +10.22 bpm | Average error was low, but individual five-second readings could differ by roughly 10 bpm in either direction, and dispersion increased at higher heart rates. Read the study |
| Hera Leto; Yang et al., Scientific Reports, 2025 | 28 athletes; steady cycling, severe-intensity exercise and high-intensity interval training; compared with a Polar H10 | Overall mean absolute error 2 bpm; systematic bias −0.67 bpm | Precision declined as exercise intensity increased. The findings apply to this model and stationary-cycling protocol. Read the study |
| Two earlier in-ear devices; International Journal of Sports Medicine, 2019 | Graded cycling under controlled conditions | Mean absolute percentage error of 5% or less | The authors still observed slight underestimation and motion sensitivity, with performance varying by location, stress and exercise. View the PubMed record |
The AirPods Pro 3 result illustrates why one average number is insufficient. A mean absolute error of 2.08 bpm describes the average size of the differences across thousands of short epochs. The limits of agreement, −10.27 to +10.22 bpm, describe a much wider range in which individual differences may fall. A device can therefore be excellent for tracking a trend or workout zone while occasionally being several beats away from a reference at a particular instant.
The cosinuss One clinical work adds a different kind of evidence. It collected long-term data in an epilepsy-monitoring setting and compared quality-filtered in-ear readings with ECG, supporting the technical feasibility of in-ear PPG. The clinical population, device and filtering process limit how far that result can be generalized to consumer earbuds. Read the clinical report.
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Mean absolute error
Mean absolute error (MAE) is the average absolute distance from the reference value. It treats a reading 3 bpm too high and one 3 bpm too low as errors of the same size, without allowing them to cancel each other out.
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Bias
Bias is the average signed difference. A negative bias indicates systematic under-reading; a positive bias indicates over-reading. A bias near zero does not mean every reading is close, because positive and negative errors can balance.
Mean absolute percentage error
Mean absolute percentage error expresses the error relative to the reference heart rate. It can make comparisons across different heart-rate ranges easier, but it is still an average and does not show the worst individual intervals.
Limits of agreement
Limits of agreement show the expected spread of individual differences in a particular study. They are especially important for short, rapidly changing readings. Correlation alone is not enough: two devices can rise and fall together yet disagree materially on the actual value.
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- Changing fit: The earbud moves, the tip is the wrong size or the sensor is not positioned against the intended tissue.
- High-impact motion: Running, jumping, rapid head movement and vibration add optical noise.
- Rapid intensity changes: Sprints and interval transitions give the algorithm less stable time to identify the new pulse rate.
- Individual anatomy: Ear-canal shape, tissue thickness, skin properties and hair or wax can affect optical coupling.
- Low perfusion or environmental effects: Anything that reduces or changes the local optical signal can make pulse detection harder.
- Short windows: A five-second estimate is more vulnerable to a missed or falsely detected beat than a longer averaged trend.
These factors explain why controlled treadmill or stationary-cycling results should not be presented as guarantees for every person, sport or workout.
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How earbuds compare with chest straps and ECG
Chest straps are commonly used as comparison devices in validation studies, while ECG remains the clinical electrical reference in the cited work. An earbud’s PPG estimate can be convenient and sufficiently consistent for many fitness decisions, but it does not directly measure cardiac electrical activity. The AirPods Pro 3 investigators characterized the device as useful for fitness tracking in healthy adults and cautioned that it should not replace clinical-grade equipment when high precision is required. Read the authors’ conclusion.
Do not infer that in-ear PPG is automatically more accurate than wrist sensing, or that a chest strap is perfect in every circumstance. The defensible comparison is narrower: ask what the specific earbud was tested against, under which activity and intensity, with which participants, and whether poor-quality samples were removed.
A practical way to use an earbud heart-rate feature
- Check the exact model: Confirm that your earbuds actually include a heart-rate sensor; the category as a whole does not.
- Fit both earbuds as directed: Try the supplied tip sizes and place the sensor where the manufacturer specifies. A stable fit is more important than maximum tightness.
- Start with a steady effort: Compare the reading with a trusted reference during rest and moderate, sustained exercise before relying on it during intervals.
- Watch for signal-quality warnings: Treat gaps, rapidly jumping values or an explicit low-quality indicator as uncertainty, not as a precise measurement.
- Use trends for training: Sustained heart-rate zones, recovery patterns and changes across workouts are generally more useful than reacting to one short interval.
- Recheck unusual values: Pause, reseat the earbud and compare again. If the number remains implausible or symptoms are present, do not use the earbud as a diagnostic test.
How to evaluate a model before buying or relying on it
Use a checklist rather than a single marketing percentage:
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- Was the comparator an ECG or a chest strap, and was the comparison simultaneous?
- Were participants healthy adults, trained athletes or a clinical population?
- Did the protocol include your activity, speed and intensity?
- Does the paper report MAE, bias and an individual-agreement measure such as limits of agreement?
- Were low-quality samples filtered, and how might that affect the number of readings shown?
- Does the product explain fit requirements and indicate when signal quality is poor?
Do not rank two published error figures without accounting for different participants, exercise protocols, sampling windows and filtering rules. A 2 bpm result from stationary cycling and a result from outdoor running are not interchangeable benchmarks.
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Do not confuse heart-rate accuracy with other earbud metrics
Some earbud studies classify stress or other physiological responses using several sensors. Those outcomes are not heart-rate accuracy measures. For example, a 2022 multimodal-earbud experiment with 18 participants reported 91.30% recall, 80.52% precision and an 85.12% F1 score for stress-response classification. Those statistics describe a classification task, not how closely the earbuds matched a reference heart rate. See the study record.
When an earbud reading should not guide a medical decision
Published evidence supports describing selected earbuds as fitness-tracking tools under studied conditions. It does not establish them as universal medical monitors. Do not use an earbud value to diagnose an arrhythmia, adjust medication or dismiss symptoms. Chest discomfort, fainting, severe shortness of breath, sustained palpitations or another urgent symptom requires appropriate medical care regardless of what an earbud displays.
For clinical monitoring or decisions that require high precision, use the equipment and interpretation specified by a qualified clinician. An earbud can supplement awareness of exercise response, but its optical estimate should not replace ECG or other clinically indicated measurements.
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The bottom line
Earbud heart-rate sensors generally work by shining light into ear tissue, detecting pulse-related changes in reflected light and filtering the signal with software and motion data. Recent studies found low average errors for particular models in controlled treadmill or cycling protocols, yet individual short-interval differences were wider and performance became less precise as movement and intensity increased. Treat the exact model, fit, activity and quality indicator as part of the measurement. For fitness trends, an evaluated earbud may be useful; for diagnosis or high-stakes precision, it is not a substitute for ECG or clinical-grade equipment.
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