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Query: UMLS:C0030567 (Parkinson's disease)
 63,064 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Parkinson's disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems utilize machine learning to classify the movement properties based on the features derived from the signals. Smartphones can provide an inexpensive measurement platform with their built-in sensors for movement assessment. This study compared three feature selection and nine classification methods for identifying PD patients from control subjects based on accelerometer and gyroscope signals measured with a smartphone during a 20-step walking test. Minimum Redundancy Maximum Relevance (mRMR) and sequential feature selection with both forward (SFS) and backward (SBS) propagation directions were used in this study. The number of selected features was narrowed down from 201 to 4-15 features by applying SFS and mRMR methods. From the methods compared in this study, the highest accuracy for individual steps was achieved with SFS (7 features) and Naive Bayes classifier (accuracy 75.3%), and the second highest accuracy with SFS (4 features) and k Nearest neighbours (accuracy 75.1%). Leave-one-subject-out cross-validation was used in the analysis. For the overall classification of each subject, which was based on the majority vote of the classified steps, k Nearest Neighbors provided the most accurate result with an accuracy of 84.5% and an error rate of 15.5%. This study shows the differences in feature selection methods and classifiers and provides generalizations for optimizing methodologies for smartphone-based monitoring of PD patients. The results are promising for further developing the analysis system for longer measurements carried out in free-living conditions.
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PMID:Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. 3270 55

This paper describes the effects of a smartphone-based wearable telerehabilitation system (called Smarter Balance System, SBS) intended for in-home dynamic weight-shifting balance exercises (WSBEs) by individuals with Parkinson's disease (PD). Two individuals with idiopathic PD performed in-home dynamic WSBEs in anterior-posterior (A/P) and medial-lateral (M/L) directions, using the SBS 3 days per week for 6 weeks. Exercise performance was quantified by cross-correlation (XCORR) and position error (PE) analyses. Balance and gait performance and level of fear of falling were assessed by limit of stability (LOS), Sensory Organization Test (SOT), Falls Efficacy Scale (FES), Activities-specific Balance Confidence (ABC), and Dynamic Gait Index (DGI) at the pre-(beginning of week 1), post-(end of week 6), and retention-(1 month after week 6) assessments. Regression analyses found that exponential trends of the XCORR and PE described exercise performance more effectively than linear trends. Range of LOS in both A/P and M/L directions improved at the post-assessment compared to the pre-assessment, and was retained at the retention assessment. The preliminary findings emphasize the advantages of wearable balance telerehabilitation technologies when performing in-home balance rehabilitation exercises.
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PMID:Effects of a Smartphone-Based Wearable Telerehabilitation System for In-Home Dynamic Weight-Shifting Balance Exercises by Individuals with Parkinson's Disease. 3301 65