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Abstract
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.
Highlights
It is estimated that one third of the senior citizens in the United States fall each year, which often results in serious injuries [1]
This paper reports our new Wireless Gait Analysis Sensor (WGAS) used for real-time automatic fall detection with an efficient Back Propagation Artificial Neural Network (BP ANN) algorithm with different training schemes and Support Vector Machine (SVM) with different kernel functions
Our custom Wireless Gait Analysis Sensor (WGAS) was applied for real-time automatic fall detection with a simple but very fast BP ANN using 6 input features trained by the Back Propagation Artificial Neural Network (BP ANN), and with a Support Vector Machine (SVM) classifier
Summary
It is estimated that one third of the senior citizens in the United States (i.e., around 12 million people) fall each year, which often results in serious injuries [1]. We have performed investigation using our custom sensor without the wireless feature and achieved 100% accuracy in identifying all 60 falls vs the Activities of Daily Living (ADL) with no false positives on young volunteers using threshold based algorithms [2]. For this current work, the sensor is wireless and allows for real-time untethered testing for fall detections, and for gait analysis [2] [3].
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