Real-time fall detection has great advantages of reducing physical and psychological damage in senior citizens group after falls and improving solitude ability and health level of senior citizens. A support vector machine (SVM) algorithm, which is based on RBF(Radial Basis Function) and applied to achieve fall detection, has been proposed in order to improve accuracy rate and lower false positive and false negative rate of fall detection system on the basis of inertial sensor. First, the system completes data collection by portable inertial sensing system at waist; then, it utilizes RBF-based SVM classifier to identify suspected fall behaviors and Particle Swarm Optimization to complete optimization of penalty factor ‘C’ and RBF argument ‘g’ in sorting algorithm. The falls and similar falls daily activities distinguishing experimetal results indicate that accuracy rate, false positive and false negative rate based on SVM algorithm are 9767%, 4.0% and 0.67% respectively. Compared with traditional threshold methods, the performance of proposed method on fall detection is promoted remarkably, so it can conclude that the appliance of the system in senior citizens fall detection is enhanced as well.