Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform.

Terraces in phylogenetic tree space are, among other things, important for the design of tree space search strategies. While the phenomenon of phylogenetic terraces is already known for unlinked partition models on partitioned phylogenomic data sets, it has not yet been studied if an analogous structure is present under linked and scaled partition models. To this end, we analyze aspects such as the log-likelihood distributions, likelihood-based significance tests, and nearest neighborhood interchanges on the trees residing on a terrace and compare their distributions among unlinked, linked, and scaled partition models. Our study shows that there exists a terrace-like structure under linked and scaled partition models as well. We denote this phenomenon as quasi-terrace. Therefore quasi-terraces should be taken into account in the design of tree search algorithms as well as when reporting results on 'the' final tree topology in empirical phylogenetic studies.

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Chest compressions (CC) are the most important means of treating cardiac arrest but are challenging to perform. Real-time feedback of depth and rate can improve CC quality. For the first time, we compared three wearable positions and six depth, and rate estimation algorithms under the same conditions. We share their optimal tuning parameters. Our evaluation of earables results in a new prime candidate for high-quality cardiopulmonary resuscitation (CPR) feedback. For depth estimation on chest, wrist, and ear the median absolute deviation (MAD) was 3.4 mm, 4.5 mm, and 5.9 mm, respectively (target depth range: 50-60 mm). Though not necessary for effective CC, fusing sensor locations reduces the depth MAD further to 3.2 mm. CC rate was estimated at less than 1.6 compressions per minute (cpm) MAD in all configurations. Hence, all wearables and algorithms give precise input for live-saving CPR. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing; Ubiquitous and mobile devices.