Abstract
Traditional pattern recognition algorithms for human activity recognition observe data from all available sensors and obtain feature vectors of a fixed dimension. Pattern classification is performed in the vector space of the same dimension. As a result, the computational cost increases as the number of sensors increases for higher accuracy, and the algorithm fails if some of the sensors are offline. As such, data–driven activity recognition suffers from the disadvantage of scalability and reusability. So there is a great need for a kind of recognition framework with high flexibility and low obtrusiveness especially for older people, not only can monitor human acceleration data but also generate human’s environment even object usage information. Prior information such as people’s routine which is relatively stable in his/her life can be used as knowledge to design a powerful human-computer collaboration system for human activity recognition. In this framework, we make a guess from the users’ routine knowledge, and then use the relevant sensor data for every individual to validate the result, the result could be positive or changed according to the decision criteria. The experimental results showed that that the accuracy of human activity recognition can be up to 90%.Especially, when some sensors are offline, the model can still achieve good results.
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