Parkinson's disease monitoring from gait analysis via foot-worn sensors

Asuroglu, Tunc; Açıcı, Koray; Erdaş, Çağatay Berke; Toprak, M. Kılınç; Erdem, Hamıt; Ogul, Hasan

doi:10.1016/j.bbe.2018.06.002

Cited by 53 publications

(25 citation statements)

References 53 publications

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“…However, in 2018 ( Aşuroğlu et al, 2018 ) proposed a Hybrid Machine Learning (ML) model (Locally Weighted Random Forest) and achieved a classification accuracy of 99%. Though our classifier does not outperform this, it is worth noting that the work presented in this paper achieves a relatively close result using a comparatively less complicated network architecture.…”

Section: Discussionmentioning

confidence: 99%

Parkinson’s Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Parkinson’s Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks

et al. 2020

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“…The coefficient of correlation, also known as Pearson’s correlation coefficient, denoted as R , is the degree of relationship between two values actual and predicted. 32 If both actual and predicted values moving in unison, there is a correlation between them. If one tends to increase while the other tends to decrease, there are opposites between them.…”

Section: Resultsmentioning

confidence: 99%

“…Aşuroğlu et al 32 introduced a machine learning model fed by GRF data collected from these gait sensors. They offered a hybrid model, named Locally Weighted Random Forest (LWRF), that provided for regression analysis on the numerical characteristics derived from an input signal.…”

Section: Related Studiesmentioning

confidence: 99%

CNN-based severity prediction of neurodegenerative diseases using gait data

2022

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Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.

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“…[102] A hybrid approach named Locally Weighted Random Forest (LWRF) is utilized for regression analysis using a similar dataset to predict UPDRS and H&Y scores. [118] LWRF approach reduces the interpatient variability by assigning weights to each sample; hence, a better correlation of prediction with clinical measures. A correlation coefficient (CC) of 0.960, mean absolute error (MAE) of 0.168, and root mean square error (RMSE) of 0.306 resulted in severity prediction in terms of H&Y scaling.…”

Section: Pd Severity Detectionmentioning

confidence: 99%

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Balakrishnan¹,

Medikonda²,

Namboothiri³

et al. 2022

Eng. Sci.

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Gait analysis, a way of assessing the manner of walking, is considered a significant criterion in diagnosing movement disorder. Various factors contribute to the alterations in gait patterns, of which neurodegenerative related disorders play a major role. Subjects affected by Parkinson's disease (PD) suffer from numerous gait-related disturbances, eventually worsening the Quality of Life. Artificial intelligence-based tools have shown great interest in computer-assisted diagnosis with the recent advancements in technology. This review article aims at portraying a novel collective approach of accenting every facet of PD gait by emphasizing the role of quantitative gait analysis and state-of-art technologies in the betterment of clinical diagnosis. The paper includes all the relevant research works (2014-2021) regarding PD assessments categorized as 1) Classification of PD and Healthy subjects, 2) PD severity prediction, and 3) Freezing of Gait detection by only considering gait modality as the mode of assessment.

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Parkinson's disease monitoring from gait analysis via foot-worn sensors

Cited by 53 publications

References 53 publications

Parkinson’s Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks

Parkinson’s Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks

CNN-based severity prediction of neurodegenerative diseases using gait data

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

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