Open AccessTemporal pyramid attention‐based spatiotemporal fusion model for Parkinson's disease diagnosis from gait data
Author(s) -
Pei Xiaomin,
Fan Huijie,
Tang Yandong
Publication year - 2021
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12018
Subject(s) - pyramid (geometry) , computer science , gait , artificial intelligence , convolutional neural network , data set , sensor fusion , pattern recognition (psychology) , set (abstract data type) , fusion , feature (linguistics) , machine learning , physical medicine and rehabilitation , medicine , mathematics , geometry , programming language , linguistics , philosophy
Parkinson's disease (PD) is currently an ongoing challenge in daily clinical medicine. To reduce diagnosis time and arduousness and even assess PD levels, a temporal pyramid attention‐based spatiotemporal (PAST) fusion model for diagnosis of PD is produced by using gait data from ground reaction forces. This model is innovative in two aspects. First, by using the temporal pyramid attention module, multiscale temporal attention is obtained from raw sequences. Second, 1D convolutional neural network and bidirectional long short‐term memory layers are used together to learn spatial fusion features from multiple channels in the spatial domain to obtain multichannel, multiscale fusion features. Experiments are performed on the PhysioBank data set, and the results show that the proposed PAST model outperforms other state‐of‐the‐art methods on classification results. This model can assist in the diagnosis and treatment of PD by using gait data.