Chapters authored
A Robotics-Based Machine Learning Approach for Fall Detection of People
For a person when carrying out household chores or even when walking on the streets, there is a risk of falling. This risk increases throughout the years due to the natural aging process. In this work, a bibliographic review was performed to find related papers who discussed different techniques for fall classification. The aim of this study was to develop two ML models: an SVM and a k-NN model, to classify the fall. An accelerometer, gyroscope, and magnetometer located on the waists of 15 volunteers are the application sensors. The extracted features were the mean, standard deviation, and range for each sensor. The best accuracy obtained was 93.89%, a sensitivity of 85.10%, and a specificity of 96.99%. All results were obtained by simulations, by using the test set separated in the first stage of the implementation. So, a shortcoming is the fact that the ML models were not tested with a hardware implementation. In future works, the models can be embedded into a microcontroller and classify data in real time.
Part of the book: Cognitive Robotics and Adaptive Behaviors