用简单动作自动估测患者自理能力，准确率达70%以上。
Movement-Specific Analysis for FIM Score Classification Using Spatio-Temporal Deep Learning
- 结合图卷积与注意力机制，从运动轨迹中提取关键关节信息。
- 在277名患者上实现70.09%-78.79%的分类准确率。
- 识别出特定动作模式，可指导临床评估与康复训练。
功能独立性量表（FIM）广泛用于评估患者日常生活活动的自理能力。然而传统FIM评估对患者和医护人员负担较重。为此，本文提出一种基于非标准动作的自动化FIM评分估计方法。该方法采用融合时空图卷积网络（ST-GCN）、双向长短期记忆网络（BiLSTM）和注意力机制的深度神经网络架构，有效捕捉长期时序依赖，并通过学习到的注意力权重识别关键身体关节贡献。在包含277名康复患者的实验中，针对FIM转移与行走项目进行评估，模型能成功区分完全独立与需协助的患者，各项任务平衡准确率达70.09%-78.79%。此外，分析揭示了特定运动模式可作为部分FIM评估项的可靠预测因子。
原文摘要 · Abstract (English)
The functional independence measure (FIM) is widely used to evaluate patients' physical independence in activities of daily living. However, traditional FIM assessment imposes a significant burden on both patients and healthcare professionals. To address this challenge, we propose an automated FIM score estimation method that utilizes simple exercises different from the designated FIM assessment actions. Our approach employs a deep neural network architecture integrating a spatial-temporal graph convolutional network (ST-GCN), bidirectional long short-term memory (BiLSTM), and an attention mechanism to estimate FIM motor item scores. The model effectively captures long-term temporal dependencies and identifies key body-joint contributions through learned attention weights. We evaluated our method in a study of 277 rehabilitation patients, focusing on FIM transfer and locomotion items. Our approach successfully distinguishes between completely independent patients and those requiring assistance, achieving balanced accuracies of 70.09-78.79 % across different FIM items. Additionally, our analysis reveals specific movement patterns that serve as reliable predictors for particular FIM evaluation items.
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