Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 arable sensors attached to various parts of the user's body.  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.  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 beten 88% and 100%, depending on the type of smart watch and the window size chosen for data segmentation.
['Avgoustinos Filippoupolitis', 'Babak Takand', 'George Loukas']
I am writing a research paper on a idea Activity Recognition in a Home Setting Using Off the Shelf Smart Watch Technology
[{"from": "human", "value": "I am writing a research paper on a idea Activity Recognition in a Home Setting Using Off the Shelf Smart Watch Technology"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 arable sensors attached to various parts of the user's body.  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.  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 beten 88% and 100%, depending on the type of smart watch and the window size chosen for data segmentation."}]