Dynamic Handwriting Analysis for Parkinson's Disease Identification using C-BiGRU Model

Moetesum, Momina; Siddiqi, Imran; Javed, Farah; Masroor, Uzma

doi:10.1109/icfhr2020.2020.00031

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…The comprehensive outcomes of our proposed system, evaluated on the PaHaW database using a similar experimental setup, demonstrate notable enhancements in classification performance for the tasks 1, 2, 3, 4, 5 and 8 compared to the findings of Drotár et al, [30], Diaz et al, [28], Moetesum et al, [27], Diaz et al, [26]. our approach exhibits superior results specifically in classifying spiral patterns.…”

Section: B Experiments and Results Using Parkinson's Disease Handwrit...mentioning

confidence: 60%

Parkinson’s Disease Detection From Online Handwriting Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Allebawi,

Dhieb,

Neji

et al. 2024

IEEE Access

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The increasing age of our society is connected to a rising number of people suffering from disorders. One such disorder is Parkinson's disease (PD). Predictions indicate that the number of individuals affected by PD will more, than double in the future. Neurologists and data scientists consider handwriting as one of the motor symptoms of PD and recognize it as a valuable resource for detecting this disorder. Within this framework, we introduce an innovative system for Parkinson's disease detection, which encompasses several key stages. The process commences with data augmentation and preprocessing, subsequently leading to the segmentation of online handwriting into Beta strokes. Following that, feature extraction is carried out utilizing the Beta-elliptical approach and the fuzzy perceptual detector. Finally, we employ bidirectional long short-term memory (BLSTM) for the classification task. To assess the performance of our system, we created a new online Arabic handwriting dataset designed for detecting Parkinson's disease. The results we obtained affirm the efficacy of our proposed system. Through comprehensive evaluations conducted on the PaHaW dataset, we achieved good accuracy, thereby highlighting that our system surpasses the performance of existing systems.

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Section: B Experiments and Results Using Parkinson's Disease Handwrit...mentioning

confidence: 60%

Parkinson’s Disease Detection From Online Handwriting Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Allebawi,

Dhieb,

Neji

et al. 2024

IEEE Access

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“…To comprehensively depict the robustness and precision of our proposed PDD-ET model compared to other models, we uniformly train all compared models with the same configuration. The models subjected to comparison include (i) SVR [29], (ii) CNN [30], (iii) Stacked-LSTM [31], (iv) LSTM [26], (v) GRU [33], (vi) Alex Net [34], (vii) DT+RF+SVR [32], (viii) Deep Neural Network [35], (ix) HOG [36], (x) Quantum ReLU Activator [37], (xi) Improved KNN [43], (xii) Adaptive Boosting [40], (xiii) RF [41], and (xiv) Deep Learning [44] Models.…”

Section: Implementation Detailsmentioning

confidence: 99%

PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset

Chatterjee,

Kumar,

Bandyopadhyay

et al. 2023

Information

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Parkinson’s disease (PD) is a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson’s disease in its initial phases is crucial to preserving the well-being of those afflicted. However, accurately identifying PD in its early phases is intricate due to the aging population. Therefore, in this paper, we harnessed machine learning-based ensemble methodologies and focused on the premotor stage of PD to create a precise and reliable early-stage PD detection model named PDD-ET. We compiled a tailored, extensive dataset encompassing patient mobility, medication habits, prior medical history, rigidity, gender, and age group. The PDD-ET model amalgamates the outcomes of various ML techniques, resulting in an impressive 97.52% accuracy in early-stage PD detection. Furthermore, the PDD-ET model effectively distinguishes between multiple stages of PD and accurately categorizes the severity levels of patients affected by PD. The evaluation findings demonstrate that the PDD-ET model outperforms the SVR, CNN, Stacked LSTM, LSTM, GRU, Alex Net, [Decision Tree, RF, and SVR], Deep Neural Network, HOG, Quantum ReLU Activator, Improved KNN, Adaptive Boosting, RF, and Deep Learning Model techniques by the approximate margins of 37%, 30%, 20%, 27%, 25%, 18%, 19%, 27%, 25%, 23%, 45%, 40%, 42%, and 16%, respectively.

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“…Similarly, end-to-end feature learning (and classification) using deep learning models is also gaining popularity in recent research, e.g. [74,87,90,96,97].…”

Section: Estimationmentioning

confidence: 99%

A survey of visual and procedural handwriting analysis for neuropsychological assessment

Moetesum

Diaz

Masroor

et al. 2022

Neural Comput & Applic

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To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

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Dynamic Handwriting Analysis for Parkinson's Disease Identification using C-BiGRU Model

Cited by 7 publications

References 26 publications

Parkinson’s Disease Detection From Online Handwriting Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Parkinson’s Disease Detection From Online Handwriting Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset

A survey of visual and procedural handwriting analysis for neuropsychological assessment

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