Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN

Abstract

Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe's action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient's upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method's recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient's rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.

Keywords: MPL-CNN; Mediapipe; action recognition; deep learning; rehabilitation assessment.

Figure 1

Vitruvian man bounding box.

Figure 2

Vitruvian man bounding box.

Figure 3

Vitruvian man bounding box.

Figure 4

Detection of Blaze-Pose, Blaze-Hands and overall detection. The three pictures in the first row represent the hand joint points detected by Blaze-Hands; the three pictures in the second row represent the upper limb joint points detected by Blaze-Pose; the third picture represents the joint points of the upper limb and hand overall detection.

Figure 5

LSTM workflow diagram.

Figure 6

FMA standard posture detection. The three pictures in the first row represent the coordinated movement of flexor muscles, the joint movement of extensor muscles and the accompanying joint movement, respectively; the three pictures in the second row represent isolated movement, wrist stability and affected movement, respectively.

Figure 7

LSTM-CNN model architecture.

Figure 8

Model loss and accuracy iteration process.

Figure 9

Confusion matrix of standard movements of Fugl-Meyer upper limb movement disorder rehabilitation training.