A new technology can enable smartwatches to automatically recognize what objects users are touching like laptop, riding a motorcycle or creating new opportunities for context-aware apps. Developed by Carnegie Mellon University and Disney Research, the technique called ‘EM-Sense’ takes advantage of the body’s natural electrical conductivity to detect whether a person is touching an electrical or electromechanical device. Also Read - You can now own a Bugatti at around Rs 80,000Also Read - Beginners guide to purchasing a budget smartwatch
This technology is based on the distinctive electromagnetic noise emitted by such devices and automatically identifies the object. This could be a great feature for smartwatches, making them much smarter than before,” said Gierad Laput, PhD student in Carnegie Mellon’s Human-Computer Interaction Institute (HCII). Also Read - Garmin Venu SQ series of smartwatches launched in India: Price, availability, features
A smartwatch equipped with EM-Sense would have a much more detailed understanding of what the user is doing than is possible with common mobile sensors, such as accelerometers or pulse monitors. The smartwatch, therefore, could automatically start a timer when the wearer begins using an electric toothbrush, unlock a keyboard without a password when users touch their laptops, or play the latest news when breakfast is being prepared. Linking a smartwatch with a smartphone or other mobile device would expand the possibilities even further.
We are now able to gain a greater contextual understanding of user activities by recognizing what objects they are interacting with, added Alanson Sample, research scientist at Disney Research.
EM-Sense is able to differentiate between scores of objects based on the ambient electromagnetic noise they emit. The human body serves as an antenna for EM-Sense. From any body part an object touches, its distinctive electromagnetic emissions propagate through the body to an electrode worn at the wrist.
Kitchen appliances, power tools, electronic scales and door handles with electrically triggered locks are among the items that can be detected and identified. It’s even possible to differentiate between different models of cell phones.
The researchers discussed the technology at ‘UIST 2015’, the ACM Symposium on User Interface Software and Technology in Charlotte, North Carolina.