PDF(Pubmed)
Abstract：
This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system\'s promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.
摘要：
本研究旨在提出一种便携式智能康复评估系统，用于数字脑卒中患者康复评估。具体来说,这项研究设计并开发了一种能够发射红光的融合装置，绿色,和红外光同时进行光电体积描记术（PPG）采集。利用这些光波长的不同穿透深度和组织反射特性，该设备可以提供更丰富、更全面的生理信息。此外，建立了多通道卷积神经网络-长短期记忆-注意力(MCNN-LSTM-attention)评价模型。这个模型,基于多个卷积通道构建，便于对采集到的多模态数据进行特征提取和融合。此外,它包含了一个能够动态调整输入信息重要性权重的关注机制模块，从而提高康复评估的准确性。为了验证所提出的系统的有效性，招募了16名志愿者进行临床数据收集和验证，包括八名中风患者和八名健康受试者。实验结果证明了系统的良好性能指标（准确度：0.9125，精度：0.8980，召回率：0.8970，F1得分：0.8949，损失函数：0.1261）。这种康复评估系统具有中风诊断和识别的潜力，为可穿戴式卒中风险评估和卒中康复辅助奠定坚实的基础。
