Abstract
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.
Highlights
Fall detection systems can be classified into three types based on video images, physical environment, and wearable devices.(1) The video-image-based system can achieve accurate fall detection and recognition, but it requires a camera to be fixed in a room, limiting user’s activities
Considering the limited monitoring range and high installation cost, it is only used in specific scenarios and is not accepted by the public.(2) the fall detection system based on wearable devices has the advantage, that is, it can be used both indoor and outdoor,(3) with no limit on the user’s behaviors and no violation of privacy.(4) Its alarm mode is relatively flexible, and its low price is suitable for widespread
We propose a human fall detection algorithm based on multiple-threshold comprehensive judgment and design a fall detection wearable device with STM32 as a hardware platform, MPU6050 as a sampler, and SIM808 as a communication positioning module, equipped with a fall detection bracelet
Summary
Fall detection systems can be classified into three types based on video images, physical environment, and wearable devices.(1) The video-image-based system can achieve accurate fall detection and recognition, but it requires a camera to be fixed in a room, limiting user’s activities This system is mostly not accepted by users because of personal privacy. We propose a human fall detection algorithm based on multiple-threshold comprehensive judgment and design a fall detection wearable device with STM32 as a hardware platform, MPU6050 as a sampler, and SIM808 as a communication positioning module, equipped with a fall detection bracelet It is suitable for both indoor and outdoor, which determines the fall state through two levels, and can trigger an alarm automatically. It has advantages of small size, simplicity, and stable operation.(5)
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