Long Short-Term Memory Modeling of the Chinese Traditional Mongolian Osteopathy Rolling Method Using Adam and the...

The acquisition and prediction of mechanical information were important for the digitization of Chinese Traditional Mongolian Osteopathy. The extraction of periodic and trend-oriented mechanical information from the force exertion process presented a substantial challenge in achieving precise mechanical information prediction. To tackle this challenge, this article introduced a double-layer Long Short-Term Memory (LSTM) network integrated with Hodrick-Prescott (HP) filtering, which combined HP filtering techniques with LSTM to enhance the accuracy of mechanical information prediction. This research was grounded in experimentally collected data for mechanical information analysis, wherein dimensionality reduction was performed on the acquired data based on the primary joints involved in force application. Specifically, three sensor data points were selected from a total of 24 as pivotal input features. LSTM was employed to capture long-term dependencies inherent in sequential data. In this investigation, the Adam optimization algorithm was utilized to fine-tune the model's hyperparameters, ensuring optimal performance. The experimental outcomes underscored the efficacy of the proposed methodology, evidenced by a coefficient of determination (R²) of 0.897, a mean absolute error of 0.025, and a root mean square error of 0.031. A comparative analysis with alternative estimation methods further attested to the stability, accuracy, and generalization capabilities of the two-layer LSTM network augmented with HP filtering.