Source-linked AI summary
Hearables: Multimodal physiological in-ear sensing
Valentin Goverdovsky, Wilhelm von Rosenberg, Takashi Nakamura, David Looney, David J Sharp, Christos Papavassiliou, Mary J Morrell, Danilo P Mandic
TL;DR
Existing physiological monitors provide limited multimodal, unobtrusive monitoring for long-term community use. This paper introduces a comfortable in-ear earpiece integrating miniature sensors for neural, cardiac, respiratory, and mechanical signals, and validates its modalities through experiments and case studies. The device measures ear-EEG and cardiac activity while using cross-modal sensing to reduce real-life artifacts.
Problem
Existing health-monitoring solutions rarely combine multiple physiological modalities, while respiration and neural monitoring remain inconvenient or stigmatizing outside the clinic.
Method
The paper develops an in-ear device integrating memory foam, microphones, and conductive cloth electrodes for multimodal physiological sensing.
Results
0.99 ECG–MPG Pearson correlation and validated EEG paradigms show that the earpiece can measure cardiac activity and neural responses, while multimodal sensing reduces mechanical artifacts.
Takeaways & Limitations
The earpiece supports inconspicuous monitoring of neural, cardiac, respiratory, and mechanical activity, including artifact handling in real-life scenarios.
Takeaways & Limitations
The simulated ear-EEG amplitude depends on EEG-source orientation and strength, so the model supports relative rather than absolute signal comparisons.
Abstract
from arXiv · showhide
Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time and in the community. Human body responses are manifested through multiple modalities, such as the mechanical, electrical and chemical; yet current physiological monitors (actigraphy, heart rate) largely lack in both the desired cross-modal and non-stigmatizing aspects. We address these challenges through an inconspicuous and comfortable earpiece, equipped with miniature multimodal sensors, which benefits from the relatively stable position of the ear canal with respect to vital organs to robustly measure the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in health monitoring is illustrated through case studies. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-life scenarios.
Results and Discussion
The earpiece integrates electro-mechanical and acoustic sensing to measure neural, cardiac, respiratory, and speech-related signals in the ear canal. Experiments show ear-EEG feasibility, multimodal artifact reduction, and cardiac measurement comparable with reference sensors.
- Sensor construction: The device combines memory foam, microphones, and conductive cloth electrodes to provide a flexible, comfortable, multimodal in-ear sensor.The foam absorbs mechanical deformations and redistributes outward pressure for a snug fit.
- Physics behind ear-EEG: Simulations predict weaker ear-EEG potentials than scalp recordings, while supporting feasibility of EEG measurement inside the ear canal.The predicted in-ear electrode difference was about a sixth of the T8–Cz amplitude.
- EEG acquisition: Ear-EEG showed a 40 Hz ASSR with SNR similar to conventional on-scalp electrodes, while SSVEP responses were weaker at the ear electrode.The ear electrode also reproduced the waveform shape and timing of transient visual evoked potentials measured by scalp electrodes.
- Dealing with EEG artifacts through multimodality: The memory-foam earpiece reduces mechanical artifacts, and its microphone enables additional denoising of jaw-clench contamination.Mechanical noise was visibly attenuated even when the artifact proxy was compromised.
- Cardiac activity: 0.99 ECG–MPG Pearson correlation verified that the earpiece measured pulse rate and provided a proxy for the PPG waveform in the absence of jaw movements.The ECG–PPG correlation was 0.98 in the same comparison.
- Speech and breathing: The microphones captured speech and respiration signals, although the MMS microphone’s speech signal was compromised at high frequencies and low-rate breathing was unreliable.Speech from the inward-facing microphone was similar in quality to the external microphone, while respiration became more measurable as breathing rate increased.
B C D
The in-ear system was evaluated for sleep-related EEG monitoring alongside its broader neural, cardiac, and respiratory sensing potential. In a proof-of-concept sleep-scoring study, ear-canal and scalp recordings showed substantial agreement when distinguishing wake from sleep.
- Sleep scoring: The device’s potential for sleep monitoring was tested by simultaneously recording scalp and ear EEG during 45-minute afternoon naps in four subjects.Subjects had restricted sleep the previous night, and clinicians scored blinded, filtered, and rescaled recordings from both locations.
- Sleep scoring: Sleep clinicians compared blinded scalp and ear recordings to distinguish Wake from combined N1, N2, and N3 sleep stages.The comparison used sleep scores generated from simultaneous recordings.
- Implications: The authors present the result as supporting out-of-hospital assessment and diagnosis of sleep-related conditions, including insomnia, sleep apnea, and excessive daytime sleepiness.The passage frames these conditions as potential application areas for the proposed device.
- System scope: The broader system demonstrated multimodal in-ear sensing of neural, cardiac, and respiratory activity in an inconspicuous and comfortable form factor.The authors also report that the substrate mitigates mechanical disturbances and that cross-modal information helps address real-life artifacts such as jaw clenches.