Annapurna Sharma

Publications

• High Accuracy Human Activity Monitoring using Neural network

  Annapurna Sharma, Young-Dong Lee, Wan-Young Chung

  07/2011;

  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... [more] 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.

• Frequency based Classification of Activities using Accelerometer Data

  Annapurna Sharma, Amit Purwar, Young-Dong Lee Young-Sook Lee Wan-Young Chung

  07/2011;

  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 ... [more] 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.

• Development and Modelling of High-Efficiency Computing Structure for Digital Signal Processing

  Annapurna Sharma, Hakimjon Zaynidinov, Hoon Jae Lee

  07/2011;

  The paper is devoted to problem of spline approximation. A new method of nodes location for curves and surfaces computer construction by means of B-splines and results of simulink-modeling is presented. The advantages of this paper is that we comprise the basic spline with classical polynomials both... [more] The paper is devoted to problem of spline approximation. A new method of nodes location for curves and surfaces computer construction by means of B-splines and results of simulink-modeling is presented. The advantages of this paper is that we comprise the basic spline with classical polynomials both on accuracy, as well as degree of paralleling calculations are also shown.

• A PCA-based Neural Network Classifier Approach for Wireless Physical Activity Monitoring

  Annapurna Sharma

  01/2009

  Degree: MS

  Supervisor: Prof. Hoon Jae Lee, Prof Hyotaek Lim, Prof Sug Hyon Tae, Prof. Wan Young Chung

• Frequency domain approach for activity classification using accelerometer.

  Wan-Young Chung, Amit Purwar, Annapurna Sharma

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 02/2008; 1:1120-1123.

  Activity classification was performed using MEMS accelerometer and wireless sensor node for wireless sensor network environment. Three axes MEMS accelerometer measures body's acceleration and transmits measured data with the help of sensor node to base station attached to PC. On the PC, real tim... [more] Activity classification was performed using MEMS accelerometer and wireless sensor node for wireless sensor network environment. Three axes MEMS accelerometer measures body's acceleration and transmits measured data with the help of sensor node to base station attached to PC. On the PC, real time accelerometer data is processed for movement classifications. In this paper, Rest, walking and running are the classified activities of the person. Both time and frequency analysis was performed to classify running and walking. The classification of rest and movement is done using Signal magnitude area (SMA). The classification accuracy for rest and movement is 100%. For the classification of walk and Run two parameters i.e. SMA and Median frequency were used. The classification accuracy for walk and running was detected as 81.25% in the experiments performed by the test persons.

• “Neural Network design for Ambulatory monitoring of elderly”

  Annapurna Sharma, Hoon Jae Lee, Wan-Young Chung

  The Korean Institute of Maritime Information & Communication Science(KIMICS). 01/2008; 12:265-269.

• Principal Component Analysis based Ambulatory monitoring of Elderly

  Annapurna Sharma, Hoon Jae lee, Wan- Young Chung

  The Journal of Korea institute of maritime information & communication sciences. 01/2008; 12:2105-2110.