Muscle estimation is required to estimate the muscle activity of older adults, and a real-time process can influence the effectiveness of monitoring applications. However, using simulation tools to assess muscle activity consumes much computation energy, which is ineffective for long term monitoring. In this paper, we proposed a novel method named Stacked-LSTM (S-LSTM) multivariate regression model for muscle estimation. S-LSTM contributes to resolving the local information lost in the sequence to-sequence architecture. To validate the proposed model, we recorded human walking on a treadmill using multiple Kinect Azure to form the 3D skeleton joints as the input to our model to predict muscle activity. These skeleton data are passed to Human Musculoskeletal Simulation based on Mujoco Engine to generate the desired outputs for training and testing. Through the experiment, S-LSTM outperforms other multivariate regression models. We measured computation time, and our proposed model runs 0.25 milliseconds faster than the MuJoCo simulation tool.