Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning

Abstract

The analysis of body motion is a valuable tool in the assessment and diagnosis of gait impairments, particularly those related to neurological disorders. In this study, we propose a novel automated system leveraging artificial intelligence for efficiently analyzing gait impairment from video-recorded images. The proposed methodology encompasses three key aspects. First, we generate a novel one-dimensional representation of each silhouette image, termed a silhouette sinogram, by computing the distance and angle between the centroid and each detected boundary points. This process enables us to effectively utilize relative variations in motion at different angles to detect gait patterns. Second, a one-dimensional convolutional neural network (1D CNN) model is developed and trained by incorporating the consecutive silhouette sinogram signals of silhouette frames to capture spatiotemporal information via assisted knowledge learning. This process allows the network to capture a broader context and temporal dependencies within the gait cycle, enabling a more accurate diagnosis of gait abnormalities. This study conducts training and an evaluation utilizing the publicly accessible INIT GAIT database. Finally, two evaluation schemes are employed: one leveraging individual silhouette frames and the other operating at the subject level, utilizing a majority voting technique. The outcomes of the proposed method showed superior enhancements in gait impairment recognition, with overall F1-scores of 100%, 90.62%, and 77.32% when evaluated based on sinogram signals, and 100%, 100%, and 83.33% when evaluated based on the subject level, for cases involving two, four, and six gait abnormalities, respectively. In conclusion, by comparing the observed locomotor function to a conventional gait pattern often seen in healthy individuals, the recommended approach allows for a quantitative and non-invasive evaluation of locomotion.

Keywords: assisted knowledge learning; deep learning; gait analysis; gait disorders; silhouette images.

Figure 1

An example for a representation of a series of standardized binary silhouettes.

Figure 2

Overview diagram of the proposed framework, including silhouette sinogram generation from silhouette images, development of 1D CNN, and two evaluation schemes.

Figure 3

Schematic diagram of the generation process of the silhouette sinogram.

Figure 4

Silhouette sinogram signals for different gait patterns. Examples of normal (NM), full-body severe gait impairment (FB), motionlessness in the left arm, and left leg are shown from left-to-right, respectively.

Figure 5

Schematic diagram of the proposed 1D CNN that involves multiple inputs serving as collaborative assisted knowledge learning.

Figure 6

Confusion matrices of classifying n classes using k frames, where n = 4 (top) and n = 6 (bottom), and k = 1, 10, 20, and 30.

Figure 7

Correct vs. false classification for n = 4.

Figure 8

Correct vs. false classification for n = 6.

Figure 9

Four-class prediction labels for the test sets.

Figure 10

Six-class prediction labels for the test set.