An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network ANN and Support Vector Machine SVM AlgorithmsReportar como inadecuado




An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network ANN and Support Vector Machine SVM Algorithms - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living ADL and 7 Dynamic Gait Index DGI tests using a custom-designed Wireless Gait Analysis Sensor WGAS. In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network BP-ANN and Support Vector Machine SVM based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 at back or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

KEYWORDS

Artificial Neural Network ANN, Back Propagation, Fall Detection, Fall Prevention, Gait Analysis Sensor, Support Vector Machine SVM, Wireless Sensor

Cite this paper

Nukala, B. , Shibuya, N. , Rodriguez, A. , Tsay, J. , Lopez, J. , Nguyen, T. , Zupancic, S. and Lie, D. 2014 An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network ANN and Support Vector Machine SVM Algorithms. Open Journal of Applied Biosensor, 3, 29-39. doi: 10.4236-ojab.2014.34004.





Autor: Bhargava Teja Nukala1, Naohiro Shibuya1,2, Amanda Rodriguez3, Jerry Tsay1, Jerry Lopez1, Tam Nguyen1,3, Steven Zupancic3, Donald

Fuente: http://www.scirp.org/



DESCARGAR PDF




Documentos relacionados