Hearable devices—earbuds and headsets—could soon track your heart rate, breathing, and movement during a run, but current models are too unreliable to trust. The problem is twofold: sensors on the wrist are easily thrown off by arm motion, and most hearables still only play audio or take calls. This programme will develop new sensor types and machine learning methods that run directly on the device, without sending personal data to commercial servers. If successful, the technology could turn cheap, everyday earbuds into continuous health monitors. That would allow researchers to track disease progression, recovery, and fitness trends across large populations at a fraction of the cost of clinical equipment. The work also tackles the fundamental challenge of making wearable data precise under real-world conditions—head movements, facial expressions, and varied body types all currently degrade accuracy. While the immediate focus is on fitness and clinical monitoring, the underlying advances in on-device computation and privacy-preserving analysis could apply to any wearable sensor system.
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Wearable devices have become pervasive and generating a lot of data which is indicative of our behaviour and physiology. This offers an unprecedented and detailed window onto human wellbeing, fitness as well as the potential for scalable public health and clinical monitoring tools. Recently, hearables have started to be used for a variety of activities ranging from the traditional music listening to more advanced fitness activities (such as running). During and following the pandemic, individuals have been commonly using these for all their virtual meetings too. Emerging companies are starting to market the devices also as comfortable sleeping aids. Hearable devices, placed on a person's head have also higher potential for detection of stable physiological signals with respect to watches, as arm movements are very pronounced and often affect sensors on watches, especially during full body movement, and hearables offer two channels (left and right). Yet, while hearable devices are indeed on the market in some form, their functions are generally still fairly restricted to means of transmission of audio and speech. Their ability to detect physiology, especially under motion and considering head and face macro and micro movements is also unproven. Additionally, they are not treated as standalone devices but they are usually dependent on smartphones for further computation and communication. Finally, the precious data generated usually, like for many other wearables, flows to commercial servers for analysis, potentially exposing users to privacy invasion. In general, there have been questions on the precision of data from wearable concerning our wellbeing and health: the sensors on these devices are often imprecise and various factors contribute to making the inference over this data hard (movement, variety of use, heterogeneity of human characteristics, etc). In this proposal I plan to advance the research on hearable sensing in fundamental ways to enable these devices to become truly reliable, trustworthy and privacy aware means of detection of our activity, fitness and health. The potential of such technology is immense: hearables are small and some versions are already very affordable, certainly more affordable than clinical diagnostics or fitness monitoring equipment. They are also more portable and people tend to wear them throughout their day (and sometimes nights, in the case of sleep hearables): this means that they have the potential of sensing the users continuously generating very precious longitudinal data which would impact the way in which we study personalized fitness as well as clinical disease progression, onset and recovery. The scalability enabled by such technology means that large populations can be reached and yet the temporal granularity of the data (i.e., the almost continuous monitoring of individuals) is not compromised, enabling public health and epidemiological studies to scale. Some of the findings of this work will impact the research in wearables and wearable data analysis in general, opening the door to a wide range of applications. More precisely the programme will innovate on the type of sensors which can be used to sense activity and health, the machine learning methods applied to this data and the systems aspects related to this which include the ability to run the models on device or explore the trade offs of local and remote computation. HearFit will also conduct extensive user studies in the context of fitness and health through collaborations with sport scientists and clinicians.
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