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

2022 ◽  
Author(s):  
Aishwarya Balakrishnan ◽  
◽  
Jeevan Medikonda ◽  
Pramod Kesavan Namboothiri ◽  
Manikandan Natarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Wearable Sensors ◽  
Disease Assessment ◽  
Learning Approaches

scholarly journals Accelerating diagnosis of Parkinson’s disease through risk prediction

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
William Yuan ◽  
Brett Beaulieu-Jones ◽  
Richard Krolewski ◽  
Nathan Palmer ◽  
Christine Veyrat-Follet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Subsequent Development ◽  
Time Compression ◽  
Disease Modifying Therapies ◽  
Machine Learning Approach ◽  
Diagnostic Window

Abstract Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

scholarly journals Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches

2018 ◽  
Vol 46 (12) ◽  
pp. 2057-2068 ◽  
Author(s):  
Erika Rovini ◽  
Carlo Maremmani ◽  
Alessandra Moschetti ◽  
Dario Esposito ◽  
Filippo Cavallo
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Supervised Machine Learning ◽  
Learning Approaches

Machine Learning Classification of Parkinson's Disease and Essential Tremor Using Wearable Sensors

2020 ◽  
Vol 101 (11) ◽  
pp. e44
Author(s):  
Sanghee Moon ◽  
Hyun-Je Song ◽  
Kelly Lyons ◽  
Rajesh Pahwa ◽  
Vibhash Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Wearable Sensors ◽  
Machine Learning Classification
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