Heads and hearts: establishing the principles behind health monitoring from the ear canal

Abstract

Human bodies are governed by multiple interacting functions – cardiac, neural, and respiratory to name a few. To assess the health of a high-risk person or as part of an ordinary medical check-up, a wearable device would allow users to continue with their daily routine. Therefore, the ultimate aim in mobile physiological monitoring is to design a single unobtrusive device that observes and analyses as many body parameters as accurately as possible. This would enhance the user’s quality of life, provide a more natural setting for health assessment, and equip physicians with continuous real-world data they could not study previously. Existing wearable solutions are either obtrusive or only cover a subset of the body functions (or both). In this work, the ear canal is investigated as a base for a mobile multi-function health monitor that is as unnoticable as hearing devices worn by millions of people every day. So far, head-based locations for recording physiological signals other than brain rhythms have been underexplored, as the comparatively narrow neck impedes the propagation of vital signals from the torso to the head surface. The suitability of the ear canal as a location for meaningful observations of physiological signals is first shown through a novel three-dimensional multi-shell biophysics model with realistic body geometries and dielectric properties, which is used to simulate the propagation of biosignals from their sources to the sensor positions on the head and in the ear. After establishing the theoretical principles behind cardiac and neural recordings from in-ear locations, the feasibility of such measurements is demonstrated through experiments. Starting with sensors embedded in a motorcycle helmet – also concealed in scenarios where a helmet is routinely worn – standard brain responses are reliably determined and heart beats correctly detected using a novel multi-channel algorithm in real-world scenarios during motion. Furthermore, full cardiac cycles are extracted from the noisy head-ECG, which facilitates an early warning system for the diagnosis of cardiovascular diseases. Subsequently, this methodology is proven to work with sensors embedded onto earplugs. The straightforward helmet and in-ear set-ups permit their usage in everyday life and enable the analysis of changes in vital body parameters. A new perspective on the evaluation of heart rate changes for stress analysis is next developed, by a simultaneous consideration of multiple heart rate derived stress metrics to categorise the psychological and physiological state of a person. The proposed theoretical models, conducted experiments, and developed algorithms pave the way to 24/7 continuous and inconspicuous health monitoring and promise to empower practitioners with a unique long-term data source for prescribing treatment.