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[期刊论文]

A novel approach for handwriting recognition in Parkinson's disease by combining flexible sensing with deep learning technologies

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author:

Zhang, Yue (Zhang, Yue.) [1] | Lin, Haifeng (Lin, Haifeng.) [2] | Xie, Xiangjie (Xie, Xiangjie.) [3] | Unfold

Indexed by:

EI

Abstract:

The presence of dysgraphia in patients with Parkinson's disease (PD) is frequently observed as an early motor symptom, serving as a vital biomarker for early diagnosis and monitoring of disease progression. However, accurately identifying and quantifying dysgraphia remains a significant obstacle for individuals affected by PD. This study introduces a novel quantification approach utilizing a 16 × 16 pressure-sensitive array that exhibits exceptional sensitivity (0.72 N−1 at 1.02–4.14 N), rapid response time (10 ms), sustained signal stability (resistance increased by 2 % after 1000 cycles), and reliable electromechanical synchronization. Through the implementation of an isolation layer, the proposed sensing array effectively addresses the issue of signal crosstalk between sensing pixels in conventional arrays, thereby enabling precise detection of subtle irregularities during the writing process. The utilization of Resnet-18 CNN in conjunction with pressure-sensitive array yields a high degree of precision in classification for Parkinson's dysgraphia. This approach offers a valuable tool for detecting dysgraphia in PD patients. © 2025

Keyword:

Deep neural networks Neurodegenerative diseases

Community:

  • [ 1 ] [Zhang, Yue]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Lin, Haifeng]Fuzhou University Affiliated Provincial Hospital, Fuzhou University, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou; 350001, China
  • [ 3 ] [Xie, Xiangjie]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Peng, Peng]Fuzhou University Affiliated Provincial Hospital, Fuzhou University, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou; 350001, China
  • [ 5 ] [Chen, Ting]Fuzhou University Affiliated Provincial Hospital, Fuzhou University, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou; 350001, China
  • [ 6 ] [Zhao, Zhenhua]Fuzhou University Affiliated Provincial Hospital, Fuzhou University, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou; 350001, China
  • [ 7 ] [Wen, Yuxing]Fuzhou University Affiliated Provincial Hospital, Fuzhou University, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou; 350001, China
  • [ 8 ] [Hong, Wenyao]Fuzhou University Affiliated Provincial Hospital, Fuzhou University, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou; 350001, China

Reprint 's Address:

  • [zhao, zhenhua]fuzhou university affiliated provincial hospital, fuzhou university, shengli clinical medical college of fujian medical university, fujian provincial hospital, fuzhou; 350001, china;;

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Related Article:

Source :

Sensors and Actuators A: Physical

ISSN: 0924-4247

Year: 2025

Volume: 385

4 . 1 0 0

JCR@2023

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

30 Days PV: 0

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