Deep learning-based screening for locomotive syndrome using single-camera walking video: Development and validation study

Abstract

Locomotive Syndrome (LS) is defined by decreased walking and standing abilities due to musculoskeletal issues. Early diagnosis is vital as LS can be reversed with appropriate intervention. Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. This model objectively assesses gait patterns through single-camera video captures, offering a novel and efficient method for LS prediction and analysis. Our model was trained and validated using a dataset of 186 walking videos, plus 65 additional videos for external validation. The model achieved an average sensitivity of 0.86, demonstrating high effectiveness in identifying individuals with LS. The model's positive predictive value was 0.85, affirming its reliable LS detection, and it reached an overall accuracy rate of 0.77. External validation using an independent dataset confirmed strong generalizability with an Area Under the Curve of 0.75. Although the model accurately diagnosed LS cases, it was less precise in identifying non-LS cases. This study pioneers in diagnosing LS using computer vision technology for pose estimation. Our accessible, non-invasive model serves as a tool that can accurately diagnose the labor-intensive LS tests using only visual assessments, streamlining LS detection and expediting treatment initiation. This significantly improves patient outcomes and marks a crucial advancement in digital health, addressing key challenges in management and care of LS.

Fig 1. Data sets for model creation and external validation.

Fig 2. Area under the curve (AUC) for external validation performance.

Fig 3. Proportion of diagnosis accuracy for external validation of DL-based model by LS Stage.

Fig 4. Deep learning-based locomotive syndrome prediction method.

a. Steps involved in deep learning-based method for locomotive syndrome prediction. Step 1: Video recording of the subject. Step 2: Pose estimation conducted using OpenPose. Step 3: Development of the LS prediction model utilizing MS-G3D. Step 4: Final prediction of Locomotive Syndrome. LS stands for Locomotive Syndrome. b. Skeleton model generated by OpenPose. Depicts the 2D coordinates for 25 key body points as identified by the OpenPose framework. c. Spatial-Temporal GCN-Based LS Prediction Model Utilizing MS-G3D. Diagram of the Spatial/Temporal Graph Convolutional Network (GCN) component. d. Spatial-Temporal GCN-Based LS Prediction Model Utilizing MS-G3D. Enhanced Spatial-Temporal GCN architecture incorporating skip connections.