The proposed method can be enhanced by integrating temporal information through a sequential processing approach. By leveraging previously estimated poses, the system can utilize a form of temporal coherence, which assumes that human poses do not change drastically between consecutive frames. This can be achieved by implementing a Kalman filter or a similar state estimation technique that predicts the current pose based on the previous pose and updates it with new measurements from the active measurement and sensor fusion framework.
Additionally, a recurrent neural network (RNN) or long short-term memory (LSTM) network could be employed to learn the temporal dynamics of human motion. By training the model on sequences of poses, it can better predict the current pose based on historical data, thus reducing the computational load associated with real-time pose estimation. This approach would allow the system to skip certain optimization processes when the pose is relatively stable, thereby improving the overall efficiency of the pose estimation process while maintaining high accuracy.