Automatic Temporal Segmentation for Post-Stroke Rehabilitation: A Keypoint Detection and Temporal Segmentation Approach for Small Datasets

TL;DR

This paper introduces an automated method for analyzing post-stroke rehabilitation videos by detecting keypoints and segmenting activities over time, aiming to improve assessment consistency and efficiency with small datasets.

Contribution

It presents a novel framework combining 2D keypoint detection and 1D temporal segmentation tailored for small datasets in stroke rehabilitation video analysis.

Findings

Effective in segmenting rehabilitation activities with limited data

Automates assessment process, reducing subjectivity and time

Potential for real-world clinical deployment

Abstract

Rehabilitation is essential and critical for post-stroke patients, addressing both physical and cognitive aspects. Stroke predominantly affects older adults, with 75% of cases occurring in individuals aged 65 and older, underscoring the urgent need for tailored rehabilitation strategies in aging populations. Despite the critical role therapists play in evaluating rehabilitation progress and ensuring the effectiveness of treatment, current assessment methods can often be subjective, inconsistent, and time-consuming, leading to delays in adjusting therapy protocols. This study aims to address these challenges by providing a solution for consistent and timely analysis. Specifically, we perform temporal segmentation of video recordings to capture detailed activities during stroke patients' rehabilitation. The main application scenario motivating this study is the clinical assessment of daily tabletop object interactions, which are crucial for post-stroke physical rehabilitation. To achieve this, we present a framework that leverages the biomechanics of movement during therapy sessions. Our solution divides the process into two main tasks: 2D keypoint detection to track patients' physical movements, and 1D time-series temporal segmentation to analyze these movements over time. This dual approach enables automated labeling with only a limited set of real-world data, addressing the challenges of variability in patient movements and limited dataset availability. By tackling these issues, our method shows strong potential for practical deployment in physical therapy settings, enhancing the speed and accuracy of rehabilitation assessments.