Being able to detect in real-time the activity performed by a user in a home setting provides highly valuable context. It can allow more effective use of novel technologies in a large variety of applications, from comfort and safety to energy efficiency, remote health monitoring and assisted living. In a home setting, activity recognition has been traditionally studied based on either a large sensor network infrastructure already set up in a home, or a network of wearable sensors attached to various parts of the user's body. We argue that both approaches suffer considerably in terms of practicality and propose instead the use of commercial-shelf smart watches, already owned by the users. We test the feasibility of this approach with two different smart watches of very different capabilities, on a variety of activities performed daily in a domestic environment, from brushing teeth to preparing food. Our experimental results are encouraging, as using standard Support Vector Machine based classification, the accuracy rates range between 88% and 100%, depending on the type of smart watch and the window size chosen for data segmentation.
['Avgoustinos Filippoupolitis', 'Babak Takand', 'George Loukas']
Activity Recognition in a Home Setting Using Off the Shelf Smart Watch Technology