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Exploring the Potential of Earables for Personal-Scale Sensing



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Earables (in-ear wearables) present a new frontier in wearables. Acting both as leisure devices, providing personal audio, and as computing and sensing platforms, earables can collect sensory data from the head. However, earables, more than the majority of other wearables (like smartwatches), have inherent size, shape, and usability constraints arising from their small form factor. These restrictions inevitably limit the number, type, and placement of the sensors that could be added to such platforms. Because of that, earables' full potential has yet to be truly unlocked. In this dissertation, we characterize and explore three different sensors that realistically will be integrated into future earables. With usability and feasibility constraints in mind, we investigate (i) in-ear, 9 degrees of freedom (DoF), inertial measurement units (IMU); (ii) microphones (facing in-the-ear-canal); (iii) photoplethysmography sensors (PPG). Data collected by earables augmented with these sensors in and around the ear may enable several personal-scale sensing applications such as augmented/virtual reality, improved navigation, medical rehabilitation, fitness tracking, identification, and health condition screening. This is facilitated by the human head being subject to fewer vibrations and random movement variations than the lower parts of the body, thanks to the inherent damping in the musculoskeletal system.

First, we study inertial sensing for head movement tracking. However, an absolute reference is key in order to re-calibrate IMUs and track absolute rotations, thus enabling more advanced applications. In mobile devices, this is done by coupling accelerometers and gyroscopes with a magnetometer. Though, as of today, no earables are equipped with a magnetometer. Hence, we investigate how to add one to an existing pair of earables (eSense). After characterizing the source of interference that would affect a magnetometer in an earable, and understanding how traditional calibrations fall short, we devise a user-transparent magnetometer calibration for earables. This sheds light on the potential of earables for both relative motion tracking as well for more advanced use cases such as navigation.

The second part of this dissertation features another commodity sensor: the microphone. We focus on in-ear facing microphones, exploiting the unique positioning of earables. Unlike IMUs, not yet readily available in all consumer earables, in-ear facing microphones are already present in both high-end leisure earbuds (e.g., Apple AirPods Pro) and in hearing aids for noise cancellation purposes. Leaning on that, we research in-ear acoustic sensing for both motion sensing (step-counting, hand-to-face gesture interactions, and human activity recognition) and user-identification (based on acoustic-gait tracking). This exploration paves the way to new interactions between users and earables, whilst increasing the security of sensory earables (through identification).

Finally, we investigate ear-worn photoplethysmography (PPG) sensing. PPGs, like IMUs and microphones, are easily integrated into an earable form factor. First, we identify the optimal sensor placement for ear-worn PPG sensing, looking both at a resting baseline as well as the impact of motion artifacts. Then, we focus on head movements and facial expressions that people perform naturally when wearing earables, causing skin and tissues displacements around the ear and inside the ear canal. Understanding such artifacts becomes key to the success of earables as the next wearable platform for accurate and reliable cardiovascular health monitoring.

The prototypes developed, the data collected, the analyses performed, and the insights drawn in this dissertation provide evidence of the potential of earables as a disruptive platform for mobile personal-scale sensing.





Mascolo, Cecilia


mobile sensing, earables, wearables, ubiquitous computing, mobile systems


Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
The work done during my PhD was sponsored by a donation of Nokia Bell Labs to the Department of Computer Science of the University of Cambridge.