Event Detection On Observations Of Daily Living

With the development of data management technology and the popularity of wearable intelligent devices,personal life data has attracted more and more attention.The detection of personal life events has always been an important research content in the field of complex event detection.By effectively combining personal daily living data with event detection,it can better guide people to improve their living standards and quality of life.It has important research value and economic value.Simple behavioral events are mainly descriptions of individual physical state,such as sitting,running and walking.These behavioral events can highlight the individual's daily exercise situation,and proper exercise is extremely important for personal health.Faced with the rapid development of integrated sensors in intelligent devices,this thesis proposes a simple behavior event detection method based on multi-sensor and multi-weight,MW_KNN,which can mine the relationship between multi-sensor data and user-specific behavior,and fully consider that different sensors have different detection effects for different simple behavior events,and set corresponding feature weights,so as to achieve the goal.Improve the recognition effect.At the same time,aiming at the high time complexity of distance-based computation in KNN,an optimization scheme is proposed to reduce time complexity by filtering query sets.Relevant experimental results show that the recognition rate of human simple behavior events can be effectively improved by using a simple behavior event detection model based on multisensor collaboration,and the optimization scheme can effectively reduce the need time of behavior detection.After obtaining the real-time movement of individuals and combining with other life data information,this thesis puts forward the concept of Observations of daily living network(ODL Network),and gives the concrete construction algorithm.In order to construct a typical life data event database,three cutting methods for life data network are proposed,which are based on time feature,space feature and clustering.These three methods can effectively partition life data network.Secondly,since graph editing distance is NP-Hard problem as a common method to measure the similarity between two graphs,this thesis proposes three similarity measurement methods for life data network to detect complex behavior events,namely Star Mapping Based Network Similarity(StarMapping),Metapath Based Similarity Measurement(PathMapping)and Dominant Structure Sequence Mapping Based Network Similarity(DSSeqMapping)take full account of the semantic and structural information of life data networks.The experimental results show that in the face of different partitioning modes of life data networks,the detection effect of complex behavior events of each algorithm is also different.PathMapping is more suitable for time feature partition,DSSeqMapping is more suitable for space feature partition,StarMapping is more suitable for clustering partition.In addition,PathMapping has higher time,StarMapping and DSSeqMapping is comparable.Then,in order to meet the requirements of real-time detection of complex behavioral events for life data networks,three strategies of using upper and lower bounds to filter are proposed based on the above three detection algorithms.Experiments show that the three methods can effectively improve the efficiency of real-time detection.Finally,this thesis designs and implements an event detection prototype system for life data,which is used to process and store life data and apply event detection technology.