Parkinson’s Disease(PD)is a neurodegenerative disease which shows a deficiency in dopaminehormone in the brain.It is a common irreversible impairment among elderly people.Identifying this disease in its preliminary s...Parkinson’s Disease(PD)is a neurodegenerative disease which shows a deficiency in dopaminehormone in the brain.It is a common irreversible impairment among elderly people.Identifying this disease in its preliminary stage is important to improve the efficacy of the treatment process.Disordered gait is one of the key indications of early symptoms of PD.Therefore,the present paper introduces a novel approach to identify pa rkinsonian gait using raw vertical spatiotemporal ground reaction force.A convolution neural network(CNN)is implemented to identify the features in the parkinsonian gaits and their progressive stages.Moreover,the var iations of the gait pressures were visually recreated using ANSYS finite element software package.The CNN model has shown a 97%accuracy of recognizing parkinsonian gait and their different stages,and ANSYS model is implemented to visualize the pressure variation of the foot during a bottom-up approach.展开更多
Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and...Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications.This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization.We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements.Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing.Subsequently,we extracted only four key time-frequency and fuzzy features from each segment,effectively capturing the signal's inherent uncertainty.Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters:the initial learning rate and the number of hidden units in the network.The detection phase yields an exceptional accuracy of 99.19%,surpassing state-of-the-art studies with the same dataset.In the grading phase,classification based on the unified PD rating scale values achieves an accuracy of 92.28%.The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD,aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors.This method demonstrates significant effi-ciency in terms of complexity,cost,and energy consumption by utilizing a single-dimensional signal,eliminating the need for pre-processing steps,and limiting the features used for training.展开更多
文摘Parkinson’s Disease(PD)is a neurodegenerative disease which shows a deficiency in dopaminehormone in the brain.It is a common irreversible impairment among elderly people.Identifying this disease in its preliminary stage is important to improve the efficacy of the treatment process.Disordered gait is one of the key indications of early symptoms of PD.Therefore,the present paper introduces a novel approach to identify pa rkinsonian gait using raw vertical spatiotemporal ground reaction force.A convolution neural network(CNN)is implemented to identify the features in the parkinsonian gaits and their progressive stages.Moreover,the var iations of the gait pressures were visually recreated using ANSYS finite element software package.The CNN model has shown a 97%accuracy of recognizing parkinsonian gait and their different stages,and ANSYS model is implemented to visualize the pressure variation of the foot during a bottom-up approach.
文摘Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications.This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization.We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements.Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing.Subsequently,we extracted only four key time-frequency and fuzzy features from each segment,effectively capturing the signal's inherent uncertainty.Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters:the initial learning rate and the number of hidden units in the network.The detection phase yields an exceptional accuracy of 99.19%,surpassing state-of-the-art studies with the same dataset.In the grading phase,classification based on the unified PD rating scale values achieves an accuracy of 92.28%.The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD,aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors.This method demonstrates significant effi-ciency in terms of complexity,cost,and energy consumption by utilizing a single-dimensional signal,eliminating the need for pre-processing steps,and limiting the features used for training.
