Embedded bioimpedance sensing for user recognition
Takram was collaborating with MIT's Camera Culture Group to design and develop their embedded, multi-electrode bioimpedance sensing technology.
New interactions and connectivity protocols are increasingly expanding into shared objects and environments, such as furniture, vehicles, lighting, and entertainment systems. We introduce Zensei, an implicit sensing system that leverages biosensing, signal processing and machine learning to classify uninstrumented users by their body’s electrical properties. Zensei could allow many objects to recognise users; e.g. phones that unlock when held, cars that automatically adjust mirrors and seats, or power tools that restore user setting.
Zensei enables physical objects to identify users by sensing the body’s electrical properties and touch/grasping behaviour through electrical frequency response sensing. It allows almost any object to be capable of user recognition. For example, a phone that unlocks as it is picked up. The sensor hardware is small in size and works by sensing the electrical frequency response properties of a user touching a surface. An array of electrodes measures this response from multiple perspectives, building up a virtual representation of the user's electrical profile depending on their skin characteristics and touch/grasping style. Thousands of frequency properties are then collected and fed into a machine learning algorithm for subsequent user recognition. This electrode array data, when combined with the use of an AC signal, allows Zensei to be implemented in a wide variety of surfaces and configurations both with and without direct skin contact.
To demonstrate the concept of casual identification on everyday devices, we built a round smartphone case to house our sensor board. The case's round shape invites unique grasping styles while simultaneously adding surface area for the painted-on electrodes. The Zensei sensor board is mounted within the case and communicates with the attached smartphone via bluetooth for continuous classification.
Munehiko Sato, Rohan Puri (MIT Media Lab),
Alex Owal (Google)
Lukas Franciszkiewicz (ex-Takram)
Ramesh Raskar (MIT Media Lab, Camera Culture Group)