The use of a single method of acceleration threshold discrimination cannot fully characterize the change in human fall behavior, which can easily result in misjudgment. In this paper, we propose a human fall detection algorithm that combines human posture, support vector machine (SVM), and quadratic threshold decision. Firstly, a large number of human posture data are collected through a six-axis inertial measurement module (MPU6050). A fall detection model is established through filtering preprocessing, eigenvalue extraction, classification, and SVM training. Secondly, a first-level threshold determination is performed through a wearable wristband device. When a suspected fall occurs, six eigenvalues will be captured and uploaded to a cloud platform to trigger second-level SVM fall judgments. By matching the eigenvalues with the fall detection model, it can be determined accurately whether a fall has taken place. The experimental results show that the fall detection has a recognition rate of 92.2%, a false rate of 3.593%, and missing rate of 2.187%, which can better distinguish other nonfall actions.