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

Summary

This study proposes a two-layer Long Short-Term Memory model integrated with Hodrick-Prescott filtering to accurately predict mechanical information in Chinese Traditional Mongolian Osteopathy, achieving high accuracy on experimental force-exertion data.

Abstract

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.

Introduction

Chinese Traditional Mongolian Osteopathy, also known as Chinese Traditional Mongolian Noharahu therapy, is a manual therapeutic approach in which the practitioner applies various manipulative techniques to specific body parts for the purpose of mechanical quantification and skill standardization, and it is clinically applied for fracture reduction. As a typical micro-gesture manipulation, Agora¹ indicated that the force application method of Mongolian Noharahu therapy differs significantly from that of Traditional Chinese Medicine (TCM). In China, Mongolian Osteopathy has been recognized as part of traditional medicine, and osteopathy education....

Protocol

LSTM Networks
A Recurrent Neural Network (RNN) was a type of neural network that could predict the value at the next time step based on the previous observations in a time series. RNNs processed one observation from a time series at a time and store historical information from the time series by continuously adjusting a "state vector" in the hidden layer. Although the goal of RNNs was to learn historical information in time series, RNNs were not suitable for processing longer time series due to the gradient vanishing problem encountered when using the backpropagation algorithm. The Long Short-Term Memory (LSTM) network is a variant of RNN that add....

Results

The primary model's performance was assessed using the test-set metrics. A high R² value (0.897) and low MAE/RMSE indicated that the two-layer LSTM with HP filtering successfully captured the force dynamics of the CTMO rolling method. Comparison with the baseline models showed that the HP-filtered models outperformed the non-filtered models, supporting the benefit of separating trend and cyclical components for LSTM learning. The model's performance metrics also improved as more sequential data (e.g., 1,200 s .......

Discussion

The results demonstrate that integrating a two-layer Long Short-Term Memory architecture with Hodrick–Prescott filtering substantially improves the prediction of force application in the Chinese Traditional Mongolian Osteopathy Rolling Method. Compared with single-layer and unfiltered LSTM models, the proposed combined model more effectively captured both long-term temporal dependencies and underlying trend components in force signals, leading to lower prediction errors and stronger generalization performance

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Artificial human forearm model	Not specified	Not available	Standard training model used as the recipient surface for rolling manipulation
Keras (R package)	RStudio/Posit	CRAN: keras	Deep learning API used for LSTM model construction; version 2.10
mFilter (R package)	RStudio/Posit	CRAN: mFilter	Used for Hodrick-Prescott filtering; function hpfilter with freq = 100
Multi-channel hand grip strength test system	Zhejiang Changxian Co. Ltd.	Tekscan	24-sensor glove system for mechanical force data acquisition at 100 Hz sampling rate
neuralnet (R package)	RStudio/Posit	CRAN: neuralnet	Used for training the Backpropagation (BP) neural network baseline model
NVIDIA GeForce RTX 3060 GPU	NVIDIA Corporation	RTX 3060 (12 GB VRAM)	GPU for accelerated neural network training
Personal computer (Intel Core i7-12700, 32 GB RAM)	Intel Corporation	i7-12700	Workstation running Windows 11 for all computational experiments
Python (scikit-learn)	Python Software Foundation	scikit-learn 1.2	RandomForestRegressor implementation for the random forest baseline model
R statistical software	R Core Team	Version 4.2	Primary statistical computing environment for data preprocessing, modeling, and visualization
randomForest (R package)	RStudio/Posit	CRAN: randomForest	Alternative random forest implementation in R
TensorFlow	Google LLC	Version 2.10	Backend deep learning framework for LSTM network training