Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person
lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very
attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system
can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the
person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and
Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM
algorithm obtain a highest accuracy of 99.23% than RF algorithm.
