Abstract
Effective clinical management and intervention development depend on accurate evaluation of Parkinsons disease (PD) severity. Many researchers have worked on developing gesture-based PD recognition systems; however, their performance accuracy is not satisfactory. In this study, we propose a multi-class Parkinson Disease detection system based on finger tapping using an attention-enhanced CNN BiLSTM. We collected finger tapping videos and derived temporal, frequency, and amplitude based features from wrist and hand movements. Then, we proposed a hybrid deep learning framework integrating CNN, BiLSTM, and attention mechanisms for multi-class PD severity classification from video-derived motion features. First, the input sequence is reshaped and passed through a Conv1D MaxPooling block to capture local spatial dependencies. The resulting feature maps are fed into a BiLSTM layer to model temporal dynamics. An attention mechanism focuses on the most informative temporal features, producing a context vector that is further processed by a second BiLSTM layer. CNN-derived features and attention-enhanced BiLSTM outputs are concatenated, followed by dense and dropout layers, before the final softmax classifier outputs the predicted PD severity level. The model demonstrated strong performance in distinguishing between the five severity classes, suggesting that integrating spatial temporal representations with attention mechanisms can improve automated PD severity detection, making it a promising non-invasive tool to support clinicians in PD monitoring and progression tracking.
Abstract (translated)
有效的临床管理和干预措施开发依赖于对帕金森病（PD）严重程度的准确评估。许多研究人员致力于开发基于手势的PD识别系统，但它们的表现准确性不尽如人意。本研究提出了一种基于手指敲击动作，并结合注意力增强的CNN BiLSTM的多类帕金森病检测系统。 我们收集了手指敲击视频，并从手腕和手部的动作中提取出时域、频域和幅度相关特征。然后，我们提出了一个集成CNN（卷积神经网络）、BiLSTM（双向长短时记忆网络）和注意力机制的混合深度学习框架，用于基于视频动作特征进行多类PD严重程度分类。 首先，输入序列被重新塑形并通过Conv1D MaxPooling块传递以捕捉局部空间依赖性。生成的特征图通过一个BiLSTM层来建模时间动态特性。注意力机制聚焦于最具有信息量的时间特征，产生上下文向量，并进一步由第二层BiLSTM处理。随后将CNN衍生出的特征与增强后的BiLSTM输出进行拼接，之后经过密集和dropout层，最后通过softmax分类器输出预测的PD严重程度等级。 该模型在区分五个严重程度类别方面表现出了强大的性能，表明结合空间时间表示和注意力机制可以提高自动化PD严重度检测能力。这使其成为支持临床医生用于PD监控及病情跟踪的一项有前景的非侵入性工具。
URL
https://arxiv.org/abs/2510.10121
