To accurately identify the elderly′s fall posture and timely carry out the medical intervention, a human fall posture identification method based on multi-modal time series images was proposed. First, the resultant acceleration is decomposed into three sub-sequences by wavelet packet, and then three time series image algorithms are used to transform the resultant acceleration into three three-channel time series images. Then, its high-dimensional features are extracted through ResNet-18, and multimodal feature fusion is used. Finally, the fusion results are combined with the improved random forest algorithm to complete the identification of human fall posture. The accuracy of 98.7% and 99.3% were verified in UMAFall and SisFall. The results show that the method has high accuracy in the identification of human falls, and can provide timely help for elderly people who fall.