Towards Reliable Wearable-User Identification

Abstract

Wearables, such as Fitbit, Apple Watch, and Microsoft Band, with their rich collection of sensors, facilitate the tracking of healthcare- and wellness-related metrics. However, the assessment of the physiological metrics collected by these devices could also be useful in identifying the user of the wearable, e.g., to detect unauthorized use or to correctly associate the data to a user if wearables are shared among multiple users. Further, researchers and healthcare providers often rely on these smart wearables to monitor research subjects and patients in their natural environments over extended periods of time. Again, it is important to associate the sensed data with the corresponding user and to detect if a device is being used by an unauthorized individual. Existing one-time authentications using credentials (e.g., passwords, certificates) or trait-based biometrics (e.g., face, fingerprints, iris, voice) might fail since such credentials can easily be shared among users. Therefore, we need a reliable and continuous wearable-user identification mechanism.