Knowledge-driven Human Daily Activity Recognition for Older People from the Perspective of Evidence Theory Using Wearable Sensors-eButton

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. Introduction Human activity recognition, especially for older people, is gaining more and more attention due to its endless potential applications ranging from elderly assistance to tactical operations. For instance, a wearable smart band application could generate a daily report summarizing the total amount of time the user used in walking, eating, running and sleeping[1]. With the development of microelectronics and computers, recent advances in wearable devices with accelerometers, physiological signal sensor and camera sensors, have made profound changes for human activity recognition to monitor people's daily lifestyle[2], [3]. It is an important application as IOT (Internet of Things). Wearable device data is multidimensional data set containing huge numbers of features. There are three different recognition approaches for activity recognition using external and wearable sensors. The first one is object usage based activity recognition, which was usually used in smart home. The system has numbers of sensors placed in target objects people are supposed to interact with every day (e.g., coffee machine, TV set, washing machine, etc.)[4]. The more sources of data available, the richer the information that can be extracted from different attributes. Kenneth P et al. used Hands-On RFID to obtain and use knowledge of person-object interactions. They presented a novel pair of wearable devices, a glove and a bracelet, that detect when users interact with unobtrusively tagged objects[5]. Donald J. Patterson et al. related to achieving fine-grained activity recognition for context-aware computing applications. They examine the advantages and challenges of reasoning with globally