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The real-time health monitoring system is a promising body area network application to enhance the safety of firefighters when they are working in harsh and dangerous environments. Other than monitoring the physiological status of the firefighters, on-body monitoring networks can be also regarded as a candidate solution of motion detection and classification. In this paper, we consider motion classification with features obtained from the on-body radio frequency (RF) channel. Various relevant RF features have been identified and a Support Vector Machine (SVM) has been implemented to facilitate human motion classification. In particular, we distinguish the most frequently appearing human motions of firefighters including standing, walking, running, lying, crawling, climbing and running upstairs with an average true classification rate of 88.69%. Classification performance has been analyzed from three different perspectives including typical classification results, effects of candidate human motions and effects of on-body sensor locations. We prove that even a subset of available RF features provides an acceptable classification rate, which may result in less computational cost and easier implementation by using our proposed scheme.1536-1233 (c)

Wireless capsule endoscopy (WCE) has become a good therapeutic method for a period of time. It helps detect, exam and heal gastro-intestinal (GI) related diseases. In the Capsule endoscopy application, knowledge of capsule position inside human body is rather important because it enables doctors locate the tumor of bleeding inside GI track and prepare for further therapeutic operations. However, due to the harsh environment for in-body wireless channel, in-body localization remains difficult and erroneous. In this paper, an improved three dimensional maximum likelihood algorithm has been introduced based on received signal strength (RSS) localization technology. Human body mesh and GI track mesh are built as the environment of algorithm simulation. Algorithm performance has been evaluated by comparison with the Cramer-Row Lower Bound (CRLB) and ranging error of the algorithm varies from 25mm to 140mm. By analyzing the results, we conclude that the three dimensional maximum likelihood is heavily impacted by the distance between implant and base station and its performance can be further improved.

Performance evaluation of wireless access and localization is important for body sensor networks, as any defects in the design not only cause wastage of resources, but also threaten an individual's health and safety. The typical cyber methods, however, such as software simulation, often fail to accurately simulate the influence of hardware implementation. The traditional physical methods, however, such as field testing, are not capable of creating repeatable and controllable channel conditions. To combine cyber and physical factors as well as to address the issue, we present a cyber physical test-bed for environment virtualization to facilitate the performance evaluation of wireless access and localization in body sensor networks. This test-bed creates a virtualized environment by emulating the wireless channel in a cybernetic way using a real time channel emulator. The original devices or systems under testing can be physically connected to a channel emulator to evaluate the performance in the virtualization environment. Furthermore, the cyber physical test-bed supports various scenarios from in-body data transmission to time of arrival based indoor localization. To validate the cyber physical approach, emulated outputs are compared with the empirical data obtained from actual measurements. To overcome the bandwidth limitation of traditional digital channel emulators, we have designed an analog channel emulator for UWB technologies. The preliminary verification of this analog emulator is introduced at the end of this paper.

To investigate coupled oscillator model and community detection algorithm, an improvement method about the phase synchronization oscillator model and an optimized method (community detection via model modification) was proposed. By using the Kuramoto oscillator model as a basis, after joining the negative coupling strength, the nodes can be divided into several different synchronized clusters. In the synchronization process, the internal nodes in the same matrix are connected closely. In this method Kuramoto coupled oscillator model are expanded. The network can realize synchronization or be partitioned into several clusters depending on its structure. If all the nodes of the network are densely connected as a whole entity, synchronization will appear. If the network consists of several groups within which the connections are dense and between which the connections are sparser, the network will be partitioned into several clusters by their phases. The networks are divided into several communities because of this clustering phenomenon. The experiments show that the method is very promising. The simulation results in a variety of community structure that the proposed algorithm is an accurate, efficient and practical method for detecting community structure in networks.