 This study uses two deep learning methods, FNN and LSTM, to detect changes in human posture among three different movements, standing, walking, and sitting. The authors introduced transition stages as distinct features for the identification and found that the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. The network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. This article was authored by Chen Ting-kua, Jun Ji-lin, Kwok Wang-jin, and others.