Research On The Wireless Sensor Network Based Activity Recognition And Object Localization

Recent advances in sensor technology, wireless communications, embedded computing, distributed information processing and micro electrical mechanical systems (MEMS) have enabled the development of wireless sensor network (WSN). WSN bridges the physical world and the logical world, provides a new interaction method between human and environment, and enables us to express the physical world by logical symbols in an efficient and convenient way. Nowadays, the WSN is widely used in ambient intelligence (AmI), environmental surveillance, manufacture, transportation and military engineering.As one important application area of WSN, AmI refers to the electronic environments that are sensitive and responsive to the presence of people. The realization of AmI systems relies on the collection of human information and environmental conditions, and, of course, on the analysis, judgement and inference of these information. With the context information such as positions, activities and the interaction between human and environment, the AmI system can provide intelligent services to the users.This thesis is focused on the activity recognition and object localization, which are two important research topics in AmI. As to activity recognition, three methods are introduced, which are environment variable based method, human-object interaction based method and wearable activity recognition method. For the wearable accelerometer based activity recognition, the supervised learning method is often used to recognize the human's activities. However, this method cannot detect and recognize the unknown activities, and cannot extend the system's recognition capability. In this thesis, the one-class classification algorithm is introduced and the combined Gauss one-class classification models are used to judge whether one activity is known. For the known activities, the weighted support vector machine (WSVM) is used to recognize their types. For the continuous unknown activities, based on the segmentation algorithm, training samples of new activities are selected and added into the existed recognition system to extend its recognition capability.In order to implement the recognition algorithms in the WSN in a distributed way, a mobile agent based distributed classification method is proposed and applied to two typical classification algorithms. The classification model is firstly decomposed and the model parameters are stored at related sensor nodes. During distributed classification, each sensor node calculates its own feature and classification result. A mobile agent is dispatched to visit all the sensor nodes serially and aggregate the results on them. Compared with the centralized classification algorithm, the proposed algorithm can reduce the bandwidth requirement, and balance the computation, storage and power consumption among sensor nodes.As to the object localization, the human localization and the acoustic source localization are introduced in this thesis. For human localization, the usual methods often needs wearing the sensors on or needs some additional devices. This thesis proposes a radio based human localization method without on-body sensor. The attenuation of received signal strength indicator (RSSI) is used to detect whether there is a person is standing between a pair of transceivers and determine his position accordingly.For the acoustic source localization, a distributed localization method with unknown source energy is proposed. With the combination of the incremental gradient algorithm and the energy ratios based acoustic source localization method, the cost function of the energy ratios based localization method is decomposed and reformulated to adapt to the distributed computation. Based on appropriate number of iteration and initial search locations, the proposed method can obtain approximate accuracy as that of the exhaustive search (ES) method with much less computation cost.