用可穿戴传感器数据，用深度学习实时识别帕金森病步态冻结。
Parkinson's Disease Freezing of Gait (FoG) Symptom Detection Using Machine Learning from Wearable Sensor Data
- 融合Transformer编码器与双向LSTM的模型处理运动数据。
- 在公开数据集上准确率达92.6%，F1分数80.9%。
- 适合临床辅助诊断与智能康复系统开发人员参考。
步态冻结（Freezing of Gait, FoG）是帕金森病（Parkinson's Disease, PD）患者中一种特殊症状，表现为突然无法正常行走。患者佩戴的加速度计可记录发作期间的运动数据，结合机器学习算法可实现对这些数据的分类。本研究提出一种Transformer Encoder-Bi-LSTM融合模型，用于从加速度计数据中识别FoG事件。在Kaggle帕金森病步态冻结数据集上的评估显示，该模型达到92.6%的准确率、80.9%的F1分数和52.06%的平均精度均值（mAP）。结果表明，基于深度学习的方法可显著提升FoG检测性能，有助于改善帕金森病患者的诊疗与管理方案。
原文摘要 · Abstract (English)
Freezing of gait (FoG) is a special symptom found in patients with Parkinson's disease (PD). Patients who have FoG abruptly lose the capacity to walk as they normally would. Accelerometers worn by patients can record movement data during these episodes, and machine learning algorithms can be useful to categorize this information. Thus, the combination may be able to identify FoG in real time. In order to identify FoG events in accelerometer data, we introduce the Transformer Encoder-Bi-LSTM fusion model in this paper. The model's capability to differentiate between FoG episodes and normal movement was used to evaluate its performance, and on the Kaggle Parkinson's Freezing of Gait dataset, the proposed Transformer Encoder-Bi-LSTM fusion model produced 92.6% accuracy, 80.9% F1 score, and 52.06% in terms of mean average precision. The findings highlight how Deep Learning-based approaches may progress the field of FoG identification and help PD patients receive better treatments and management plans.
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