Static-Dynamic Temporal Networks for Parkinson's Disease Detection and Severity Prediction.

Most patients with Parkinson's disease (PD) have different degrees of movement disorders, and effective gait analysis has a huge potential for uncovering hidden gait patterns to achieve the diagnosis of patients with PD. In this paper, the Static-Dynamic temporal networks are proposed for gait analysis. Our model involves a Static temporal pathway and a Dynamic temporal pathway.

A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors.

In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones.

Human Behavior Recognition Model Based on Feature and Classifier Selection.

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data.