Journal: Brain

Article Title: Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson’s disease

doi: 10.1093/brain/awz417

Figure Lengend Snippet: Experimental setup and analysis pipeline. ( A ) Deep brain electrode schematic. ( B ) The directional DBS lead (Boston Scientific). Contacts are distributed along four levels. On levels two and three, there are three segmented contacts (level two: contacts 2/3/4; level three: contacts 5/6/7). ( C ) The fixed-limb voluntary movement task; upper and lower limb movements performed in separate blocks, with each block preceded with an instruction describing the limb to be moved after hearing the imperative auditory cue. ( D ) Random-limb voluntary movement task; upper and lower limb movements randomly instructed within the same experimental block. Here the auditory cue prior to each trial also describes the limb to be moved. ( E ) Flow chart summarizing analysis pipeline. LFP signals are used to predict the limb moved using the naïve Bayes technique. Acc. = acceleration; Ant = anterior; au = arbitrary units; Lat = lateral; Med = medial; Post = posterior.

Article Snippet: The Gaussian Naïve Bayes classifier MATLAB implementation was used to differentiate between the upper and lower limb movements ( Friedman et al. , 1997 ; ; ).

Techniques: Blocking Assay

Journal: International Journal of Environmental Research and Public Health

Article Title: Ability of Wearable Accelerometers-Based Measures to Assess the Stability of Working Postures

doi: 10.3390/ijerph19084695

Figure Lengend Snippet: Classification accuracies for different sensor configurations and different feature sets using different classifiers (data labeling based on COPV_AP).

Article Snippet: The following classifiers were used for models training, k-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Kernel Naive Bayes (KNB), Logistic Regression (LR), Discriminant Analysis (DA), Support Vector Machine (SVM), Decision Tree (DT), Bagged Trees (BT) and Optimizable Ensemble (OE) classifiers, which were provided in MATLAB Classification Learner toolbox [ ].

Techniques: Labeling, Blocking Assay

Journal: International Journal of Environmental Research and Public Health

Article Title: Ability of Wearable Accelerometers-Based Measures to Assess the Stability of Working Postures

doi: 10.3390/ijerph19084695

Figure Lengend Snippet: Classification accuracies for different sensor configurations and different feature sets using different classifiers (data labeling based on PPS).

Article Snippet: The following classifiers were used for models training, k-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Kernel Naive Bayes (KNB), Logistic Regression (LR), Discriminant Analysis (DA), Support Vector Machine (SVM), Decision Tree (DT), Bagged Trees (BT) and Optimizable Ensemble (OE) classifiers, which were provided in MATLAB Classification Learner toolbox [ ].

Techniques: Labeling, Blocking Assay

Journal: International Journal of Environmental Research and Public Health

Article Title: Ability of Wearable Accelerometers-Based Measures to Assess the Stability of Working Postures

doi: 10.3390/ijerph19084695

Figure Lengend Snippet: Classification accuracies for different feature sets using different classifiers based on Pelvis acceleration data.

Article Snippet: The following classifiers were used for models training, k-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Kernel Naive Bayes (KNB), Logistic Regression (LR), Discriminant Analysis (DA), Support Vector Machine (SVM), Decision Tree (DT), Bagged Trees (BT) and Optimizable Ensemble (OE) classifiers, which were provided in MATLAB Classification Learner toolbox [ ].

Techniques: Blocking Assay