Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders

Abstract

Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.

Figure 1

Etiologies pie chart.

Figure 2

MLP architecture for prediction.

Figure 3

LSTM architecture for prediction.

Figure 4

FCN architecture for prediction.

Figure 5

ResNet architecture for prediction.

Figure 6

Encoder architecture for prediction.

Figure 7

t-LeNet architecture for prediction.

Figure 8

Proposed $\mathrm{\Delta }GPS$ prediction workflow for the image-based approach.

Figure 9

2D FFT for a given gait cycle. (a) The gait cycle; (b) FFT spectrum of the gait cycle; (c) Centralized FFT spectrum of the gait cycle.

Figure 10

Tailored 2D CNN for prediction.

Figure 11

ROC curves for both approaches.