Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography

Wei Lu,Lifu Gao,Daqing Wang,Zebin Li,Huibin Cao

doi:10.3390/electronics10161946

Abstract

Accurate and long-term prediction of elbow flexion force can be used to recognize the intended movement and help wearable power-assisted robots to improve control performance. Our study aimed to find a proper relationship between electromyography and flexion force. However, the existing methods must incorporate biomechanical models to produce accurate and timely predictions of flexion force. Elbow flexion force is largely determined by the contractile properties of muscles, and the relationship between flexion force and the motor function of muscles has to be thoroughly analyzed. Therefore, based on the investigation on the contributions of different muscles to the flexion force, original electromyography signals were decomposed into non-linear and non-stationary parts. We selected the mean absolute value (MAV) of the non-linear part and the variance of the non-stationary part as inputs for an Informer prediction model that does not require detailed a priori knowledge of biomechanical models and is optimized for processing time sequences. Finally, a long-term flexion force probability interval is proposed. The proposed framework performs well in predicting long-term flexion force and outperforms other state-of-the-art models when compared to experimental results.

Highlights

With greater attention being given to physical health, the elderly have an increasing need for upper limbs to perform highly flexible tasks [1]
Intending to accurately predict flexion force, we propose a confidence interval prediction method of elbow joint flexion force based on the Informer model using EMG signals
R2 are taken into account to evaluate the performance of our algorithm [30]

Summary

Introduction

With greater attention being given to physical health, the elderly have an increasing need for upper limbs to perform highly flexible tasks [1]. Wearable assist robots are currently used in the field of limb rehabilitation [2,3]. The devices currently used for rehabilitation have only a single control scheme that cannot be customized for individual patients. Many researchers have used a combination of human bio-sensor technology and wearable assist robot technology to develop rehabilitation control algorithms [4]. The accurate prediction of upper limb flexion force will be key to improving the control of wearable assist devices [5,6]. Flexion force prediction (FFP) can be regarded as a crucial input for robot control systems. The flexion force can be obtained in the following ways [7]: (a)

Results

Discussion

Conclusion