Name
Title | ||
|---|---|---|
Sequence-based dynamic handwriting analysis for Parkinson’s disease detection with one-dimensional convolutions and BiGRUs |
Abstract | ||
|---|---|---|
Parkinson’s disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients’ fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on one-dimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2020.114405 | Expert Systems with Applications |
Keywords | DocType | Volume |
Parkinson’s disease,Dynamic handwriting analysis,Recurrent neural networks,Computer-aided diagnosis | Journal | 168 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moises Diaz-Cabrera | 1 | 89 | 9.80 |
| Momina Moetesum | 2 | 1 | 1.70 |
| Imran Siddiqi | 3 | 421 | 36.56 |
| Gennaro Vessio | 4 | 22 | 7.26 |