Fall classification using neural network

Automated fall detection provides important information to doctors in treating their patients. This information is particularly important in children with cerebral palsy as they are more vulnerable to falling. Shriners Hospitals for Children–Northern California is searching for an automatic fall detection system that can accurately recognize not only fall versus non–fall events but also different types of falls. The purpose of this work is to design effective fall detection methods using neural networks utilizing the MATLAB neural network tool. Impact, orientation, and rotation features are extracted from signal waveforms generated from accelerometers worn on the lower backs of children with and without CP. The designed neural networks use the gradient descent, scaled gradient descent, and Levenberg–Marquardt learning algorithms in search for a high accuracy neural network for detecting falls. The results show that the Levenberg–Marquadt algorithm is the best, at 84.6% accuracy, in categorizing torso falls, falls to the knees, falls to the bottom, and non–falls events. When creating a new falls class, made up of torso falls, falls to the knees, and falls to the bottom, accuracy improves to 94.3%. Alternatively, when the falls to the knees and falls to the bottom are included in the non–fall class, the best performing neural network is 93.2% accurate.