Deep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study.

Rehabilitation of gait function in post-stroke hemiplegic patients is critical for improving mobility and quality of life, requiring a comprehensive understanding of individual gait patterns. Previous studies on gait analysis using unsupervised clustering often involve manual feature extraction, which introduces limitations such as low accuracy, low consistency, and potential bias due to human intervention. This cross-sectional study aimed to identify and cluster gait patterns using an end-to-end deep learning approach that autonomously extracts features from joint angle trajectories for a gait cycle, minimizing human intervention.

Pathological gait clustering in post-stroke patients using motion capture data.

Background: Analyzing the complex gait patterns of post-stroke patients with lower limb paralysis is essential for rehabilitation.

Research Question: Is it feasible to use the full joint-level kinematic features extracted from the motion capture data of patients directly to identify the optimal gait types that ensure high classification performance?

Methods: In this study, kinematic features were extracted from 111 gait cycle data on joint angles, and angular velocities of 36 post-stroke patients were collected eight times over six months using a motion capture system. Simultaneous clustering and classification were applied to determine the optimal gait types for reliable classification performance.

Demand risk transfer and government's cost efficiency: Focusing on Korean waste treatment PPP cases.

This study aimed to examine the government's cost efficiency considering the high-risk/high-return mechanism of PPP. Faced with increasing demand but with limited budget, the Korean government has relied on the Public-Private Partnership (PPP) to provide waste treatment services for the last couple of decades to expand fiscal space. However, most of waste treatment facilities projects have been promoted using the BTO (Build-Transfer-Operate) method with high rate of return due to the demand risk that is transferred to the private.