TY  - JOUR
PY  - 2015//
TI  - An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ANN) and support vector machine (SVM) algorithms
JO  - Open journal of applied biosensor
A1  - Nukala, Bhargava Teja
A1  - Shibuya, Naohiro
A1  - Rodriguez, Amanda
A1  - Tsay, Jerry
A1  - Lopez, Jerry
A1  - Nguyen, Tam
A1  - Zupancic, Steven
A1  - Lie, Donald Yu-Chun
SP  - 29
EP  - 29
VL  - 3
IS  - 4
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2168-5401
UR  - http://dx.doi.org/10.4236/ojab.2014.34004
ID  - ref1
ER  -