Support Vector Machine based activity detection

Human activity monitoring enables detecting instances when people need help during daily routines. They may have forgotten taking medication or they can experience more severe situations such as falling. Detecting their activities yield their context information revealing occurrences of such cases. We designed and implemented a solution to activity detection proposing a Support Vector Machine (SVM) based method. We gathered data through accelerometer to come up with a noninvasive solution. Our method is the combination of a feature extractor and classifier. Presented activity recognition suit eliminates the need for experimenting with multiple features to determine the best classifying features contrary to some approaches utilizing SVM. With our SVM based activity recognizer, we classified sit, stand, lie and walk actions with 100 % accuracy.