In order to achieve real-time and accurate fall prediction, and transplant the deep learning model to run on wearable devices, a lightweight convolutional neural network model is proposed. Drawing on the lightweight model idea of DSC network, the network structure is designed, and the number of channels and the size of convolution kernel are optimized, which greatly reduces the computational complexity of the model while keeping the accuracy rate basically unchanged. In order to deploy the algorithm in the wearable fall protection device, a real-time running framework of the model on the embedded side is proposed, and the algorithm is written as a C program and transplanted to the STM32 microcontroller. This model achieves 97.5% accuracy with 204.3ms lead time on the Sisfall dataset. The transplanted model is only 11.65KB in size, and the algorithm delay in the STM32 microcontroller is only 8.24ms. The experimental results show that the model has high prediction accuracy and good real-time performance, which provides a further reference for the development of fall prediction algorithms and fall protection devices.