Rehabilitation Process to Control and Predict User Hand Gestures Through Emg Signal-Based Reinforced Transradial Amputation Model

Abstract

Assistive devices support disabled people through object traction intention and prediction. To develop a smart assistive device to support paralyzed patients through the flexible nature of gloves which allow the user finger movements which has no strength or nerve-based controls. The kinematic signal variations at the upper limb position are collected through the electromyography (EMG) signals. The interaction between the prosthetic hand and the human-disabled part is achieved through inferred parameters used in the communication. The rehabilitation and assistive technologies enhance the process of user intention to perform the task under various situations. The intended object prediction model involves human interaction with prosthetic signals. The object tracking and hand movement positions are combined in the proposed model to predict the exact activities of the user. The low and high muscle variations derive the pattern and its associated task. The proposed model introduces the deep transradial amputation model (DTAM) to predict the user intention movement based on EMG sensor-based hand gesture recognition. The proposed method analyzes the EMG signals collected from the upper and lower hand muscles. The model also constructs and trains the data to predict the 3D hand gestures and their positions from the features collected through EMG signals. The reinforcement-based recurrent fuzzy neural network (RFNN) is used to derive the pattern by combining various positions of the hand gesture. The maximum reward value used to obtain the accurate prediction is a performance metric. The correlation mapping and its coefficient values provide sufficient evidence to analyze the muscle variation data to predict user-intended activities. The 3D prosthetic hand values and finger positions of the complex object task acceleration can be predicted with the mean performance of [Formula: see text] and NRMSE value = 0.101. The maximum reward count to 50 under the various iteration processes to analyze the movements. The proposed transradial amputation manages to predict the user’s intention within the time period of 124[Formula: see text]ms. Through the results, the model enhances the task intention prediction and movement position quickly compared to the other models.