College of Electronic Information Engineering, Changchun University, Changchun 130012, China.
Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China.
Sensors (Basel). 2024 Feb 8;24(4):1105. doi: 10.3390/s24041105.
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.
中风是一种医疗紧急情况，可能导致运动障碍的发展，如异常肌肉张力、运动范围受限，或协调和平衡方面的异常。为了帮助中风患者尽快康复，康复训练方法采用各种运动模式，如普通运动和关节反应，以诱导四肢的主动反应，逐渐恢复正常功能。康复效果评估可以帮助医生了解不同患者的康复需求，确定有效的治疗方法和策略，提高治疗效率。为了实现动作检测的实时性和准确性，本文使用 Mediapipe 的动作检测算法，并提出了一种基于 MPL-CNN 的模型。Mediapipe 可用于识别患者上肢的关键点特征，并同时识别手部的关键点特征。为了检测上肢运动障碍康复训练的效果，将 LSTM 和 CNN 结合起来形成新的 LSTM-CNN 模型，用于识别 Mediapipe 提取的上肢康复训练的动作特征。MPL-CNN 模型可以有效地识别中风患者上肢康复训练过程中的康复运动的准确性。为了确保康复训练动作的科学有效性和统一标准，本文采用 Fugl-Meyer 上肢康复训练功能评估表（FMA）中的姿势，建立了 FMA 上肢康复数据集进行实验验证。实验结果表明，在 Fugl-Meyer 上肢康复训练评估效果检测的各个阶段，基于 MPL-CNN 的方法对上肢康复训练动作的识别准确率达到 95%。同时，各种上肢康复训练动作的平均准确率达到 97.54%。这表明该模型在不同动作类别下具有高度的稳健性，证明了 MPL-CNN 模型是一种有效且可行的解决方案。这种基于 MPL-CNN 的方法可以为中风后上肢运动障碍康复效果的评估提供高精度的检测方法，帮助临床医生评估患者的康复进展，并根据评估结果调整康复计划。这将有助于提高康复治疗的个性化和精确性，促进患者的康复。
