Detection and Quantification of Resting Tremor in Parkinson’s Disease Using Long-Term Acceleration Data

Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.

Download Full-text

Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall

Journal of Biomechanical Engineering ◽

10.1115/1.4043449 ◽

2019 ◽

Vol 141 (8) ◽

Cited By ~ 4

Author(s):

Tae Hyong Kim ◽

Ahnryul Choi ◽

Hyun Mu Heo ◽

Kyungran Kim ◽

Kyungsuk Lee ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Continuous Time ◽

Classification Accuracy ◽

Fall Detection ◽

Series Data ◽

Machine Learning Classifiers ◽

Detection Model ◽

Learning Classifiers ◽

Proposed Model

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

Download Full-text