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This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.

This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.

In this paper, we demonstrate an empirical analysis of the reliability of low-rate wireless u-healthcare monitoring applications. We have considered the performance analysis of the IEEE 802.15.4 low-rate wireless technologies for u-healthcare applications. For empirical measurement, we considered three scenarios in which the reliability features of the low-rate wireless u-healthcare monitoring applications have been measured: (i) distance between sensor nodes and base station; (ii) deployment of the number of sensor nodes in a network; and (iii) data transmission by different time intervals. The experimental results show that received data are used to calculate BER and analyze the performance according to the scenarios. The BER is affected when varying the distance between sensor node and base station, the number of nodes, and time interval. 513Low-rate wireless u-healthcare monitoring system works well in single-hop (ad hoc network) data communications but revealed some scheduling problems in a multi-hop network. One of the more difficult constraints was that several wireless biological data sensors, including ECG, and accelerometer reached the limits of using a multi-hop environment. Our experimental results demonstrated that the star topology and the one-hop connection to the base station are able to improve the performance of the reliability of the low-rate wireless u-healthcare monitoring applications. In applying low-rate wireless technology for wireless u-healthcare monitoring application, sampling rate, number of nodes and data packet sizes as well as reliable medical devices are closely related to the reliability that will improve the system performance.Young-Dong Lee received his BS, MS, and PhD degrees from and 1998, respectively. He is currently an associate professor in the School of Computer and Information Engineering at Dongseo University. From 1987 to 1998, he was a research associate at the Agency for Defense Development. His current research interests include developing secure communication system, side-channel attack, and ubiquitous sensor network/radio frequency identification security.

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