Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease

Balaji, E; Brindha, D; Ramasamy, Balakrishnan

doi:10.1016/j.asoc.2020.106494

Cited by 60 publications

(11 citation statements)

References 31 publications

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“…Finally, to identify the stages of Parkinson's, only supervised linear classifiers have been used. Nonlinear classifiers, on the other hand, can be utilized to generate nonlinear correlations, especially when tremor data and gait patterns are used [49]. Each stage of the disease is distinguished by different symptoms of shared motor characteristics.…”

Section: Tremormentioning

confidence: 99%

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Dixit

Bohre²,

Singh

et al. 2023

Electronics

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Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study.

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Section: Tremormentioning

confidence: 99%

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Dixit

Bohre²,

Singh

et al. 2023

Electronics

View full text Add to dashboard Cite

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“…On the other hand, Balaji E. et al [ 23 ] proposed a machine-learning model that can assist clinicians in detecting the stages of PD through gait information. Gait information provides all mobility information about healthy people and PD-affected people.…”

Section: Severity Identification Of Parkinson’s Diseasementioning

confidence: 99%

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

et al. 2023

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Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson’s Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer’s disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.

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“…F1 − Scor e = 2T P 2T P + F P + F N (11) where T P denotes the number of positive samples correctly detected. F N is in reverse, which implies the number of negative samples mistakenly detected.…”

Section: B Experiments On Local Datasetmentioning

confidence: 99%

“…ML strategies for human activity identification, including support vector machines (SVM), multi-layer perceptron (MLP), and random forest (RF), have been widely investigated in healthcare, owing to their promising ability to address multiple-dimensional and nonlinear data patterns. The most popular applications include fall detection [8], gait pattern classification in post-stroke patients [9], walking versus running [10], Parkinson's disease diagnosis [11], [12], among others [13]. For example, Ilias et al [14] combined the artificial neural network (ANN) and SVM methods to classify the gait patterns of autistic children from normal gait.…”

Section: Introductionmentioning

confidence: 99%

Dense & Attention Convolutional Neural Networks for Toe Walking Recognition

Chen

Soangra

Grant-Beuttler

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Idiopathic toe walking (ITW) is a gait disorder where children's initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new onedimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are incorporated into the network to highlight useful features while suppressing unwanted noises. Also, the Focal Loss function is enhanced to alleviate the imbalance sample issue. The proposed approach outperforms other methods and obtains a superior performance. It achieves a test recall of 88.91% for recognizing idiopathic toe walking on the local dataset collected from real-world experimental scenarios.To ensure the scalability and generalizability of the proposed approach, the algorithm is further validated through the publicly available datasets, and the proposed approach achieves an average precision, recall, and F1-Score of 89.34%, 91.50%, and 92.04%, respectively. Experimental results present a competitive performance and demonstrate the validity and feasibility of the proposed approach.

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Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease

Cited by 60 publications

References 31 publications

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

Dense & Attention Convolutional Neural Networks for Toe Walking Recognition

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