Multimodal In-Ear Sensors Estimate Energy Expenditure During Outdoor Activity

To overcome these constraints and enable continuous monitoring, consumer wearables combine optical heart rate sensors with microelectromechanical triaxial accelerometers. While wristbands and chest straps are the common platforms, the ear canal offers several physiological advantages.

Its stable anatomy reduces motion artifacts during physical activity, and its proximity to major blood vessels provides strong photoplethysmography signals. Integrating heart rate and motion sensors into a single earbud therefore enables reliable, continuous physiological monitoring in a compact and unobtrusive form.

Methodology: Evaluating the Cosinuss Sensors

Researchers assessed two multimodal in-ear sensors, the cosinuss one and two, for heart-rate measurement and body motion within the ear canal. The cosinuss one device uses a 520 nm green light-emitting diode for photoplethysmography, whereas the cosinuss two uses dual red and infrared light sources at 665 and 940 nm.

Both devices employ a three-axis accelerometer operating at 100 Hz to measure body movement and calculate motion magnitude.

To evaluate sensor performance, 24 healthy adults completed a two-stage evaluation. During the laboratory phase, participants performed a modified Bruce treadmill protocol to establish individual calibration models across progressively increasing speeds and inclines.

Reference measurements were obtained using a portable calorimeter and an electrocardiogram-validated chest strap. Predictive models combined heart rate, acceleration magnitude, and biological sex.

Three days later, participants completed an outdoor trial on Munich's Olympiaberg hill. They performed uphill walking, downhill walking, and high-speed uphill walking along a one-kilometer route with an average gradient of 4.6%.

Accuracy in Real-World Conditions

Laboratory calibration models showed strong predictive performance, with the core variables explaining nearly 80% of the variation in energy expenditure for both sensors. Individual baseline metabolic differences improved model accuracy. During outdoor activity, neither sensor showed statistically significant systematic bias, supporting preliminary group-level agreement with the reference measurements.

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Specifically, during comfortable uphill walking, the mean absolute percentage error ranged from 22% to 26% for both sensors. This performance was comparable to commercial wrist-worn devices such as the Apple Watch 6 and exceeded that of the Fitbit Sense.

Prediction accuracy declined during downhill walking, where errors increased to 37–38%. The authors propose two potential reasons for this difference: calibration models were trained only on uphill data, which did not capture the changes in posture and muscle activity associated with downhill movement, and downhill walking requires lower energy expenditure, thus amplifying percentage errors.

Performance also varied between biological sex groups. Female participants showed stronger predictive correlations during fast uphill walking, whereas male participants exhibited weaker correlations under several conditions.

These findings suggest that factors not included in the current models, such as individual walking pace and other physiological variables, influence prediction accuracy and should be incorporated into future algorithms.

Applications in Health Monitoring

The integration of optical and motion sensing within the ear canal has significant potential for digital health and continuous physiological monitoring.

In clinical rehabilitation settings, in-ear sensors could be used to estimate energy expenditure in patients recovering from major surgery or managing chronic respiratory and cardiovascular diseases. Their discreet earbud design improves comfort, encouraging long-term adherence during daily use.

With further development, these devices could support athletic training and occupational safety by providing real-time estimates of physical workload in demanding environments. Because the ear canal is located close to major blood vessels, the same platform can simultaneously measure heart rate, heart rate variability, core body temperature, and body movement.

This capability could support the creation of a single wearable platform for monitoring fatigue, metabolic demand, and overall physiological status.

Conclusion and Future Directions

This study demonstrates that multimodal in-ear sensors have the potential to estimate physical activity energy expenditure with group-level accuracy comparable to that of conventional wrist-worn wearables, while avoiding the need for chest straps. It highlights the potential of ear-canal sensing as a compact platform for continuous physiological monitoring.

Future work should focus on further expanding model training to include downhill locomotion, varied terrain, and controlled walking speeds to improve prediction across diverse movement conditions. Validation should also be extended to participants with different ages, body compositions, and real-time clinical conditions to achieve robust individual-level performance and support the clinical adoption of in-ear health monitoring technologies.

Journal References

Camargo, D., et al. (2026). Feasibility of energy expenditure estimation during outdoor walking using multimodal in-ear sensors: a preliminary validation study against indirect calorimetry. Scientific Reports. 16. DOI: 10.1038/s41598-026-64756-6. https://www.nature.com/articles/s41598-026-64756-6.

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