We investigated how regular eyeglasses could be extended with multi-modal sensing and processing functions to support context-awareness applications. Our aim was to leverage eyeglasses as a platform for acquiring and processing context information according to the wearer's needs. The WISEglass architecture consists of inertial motion, environmental light, and pulse sensors, processing and wireless data transmission functionality, besides a rechargeable battery. We implemented prototypes of WISEglass and evaluated them in three application scenarios: daily activity recognition, screen-use detection, and heart rate estimation. We conducted a daily activity study with nine participants, each wearing WISEglass and recording for one day. When evaluating daily activity recognition, we obtained 77 % average accuracy for continuous recognition using Gaussian Mixture Models and classifier reject to ignore null class data. Using the light sensor for detecting screen-use, yielded 80 % accuracy. Against a chest-worn ECG reference, our heart rate estimation showed an difference below 10 beats for stationary activities across the full recording day. We concluded that smart eyeglasses provide information from a single measurement spot that is relevant in various context recognition applications.
Wahl and his co-workers designed REF an eyeglass (WISEglass) that consists of inertial motion, environmental light, and pulse sensors, processing and wireless data transmission functionality and also a rechargeable battery.
WISEglass: smart eyeglasses recognising context
BODYNETS
Medicine Computer Science