Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment

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