Modelling and identification of characteristic kinematic features preceding freezing of gait with convolutional neural networks and layer-wise relevance propagation

Abstract

Background: Although deep neural networks (DNNs) are showing state of the art performance in clinical gait analysis, they are considered to be black-box algorithms. In other words, there is a lack of direct understanding of a DNN's ability to identify relevant features, hindering clinical acceptance. Interpretability methods have been developed to ameliorate this concern by providing a way to explain DNN predictions.

Methods: This paper proposes the use of an interpretability method to explain DNN decisions for classifying the movement that precedes freezing of gait (FOG), one of the most debilitating symptoms of Parkinson's disease (PD). The proposed two-stage pipeline consists of (1) a convolutional neural network (CNN) to model the reduction of movement present before a FOG episode, and (2) layer-wise relevance propagation (LRP) to visualize the underlying features that the CNN perceives as important to model the pathology. The CNN was trained with the sagittal plane kinematics from a motion capture dataset of fourteen PD patients with FOG. The robustness of the model predictions and learned features was further assessed on fourteen PD patients without FOG and fourteen age-matched healthy controls.

Results: The CNN proved highly accurate in modelling the movement that precedes FOG, with 86.8% of the strides being correctly identified. However, the CNN model was unable to model the movement for one of the seven patients that froze during the protocol. The LRP interpretability case study shows that (1) the kinematic features perceived as most relevant by the CNN are the reduced peak knee flexion and the fixed ankle dorsiflexion during the swing phase, (2) very little relevance for FOG is observed in the PD patients without FOG and the healthy control subjects, and (3) the poor predictive performance of one subject is attributed to the patient's unique and severely flexed gait signature.

Conclusions: The proposed pipeline can aid clinicians in explaining DNN decisions in clinical gait analysis and aid machine learning practitioners in assessing the generalization of their models by ensuring that the predictions are based on meaningful kinematic features.

Keywords: Convolutional neural networks; Explainable artificial intelligence; Freezing of gait; Gait analysis; Parkinson’s disease.

Fig. 1

Visualization of the proposed methodology. The proposed methodology consists of two-stages (1) a convolutional neural network (CNN) to model the dramatic reduction of movement present before a freezing of gait (FOG) episode (Phase 2), and (2) layer-wise relevance propagation (LRP) to interpret the underlying features that the CNN perceives as important to model the pathology (Phase 3). The CNN was trained with the sagittal plane kinematics as recorded by a motion capture system (Phase 1). The figure illustrates the benefit of interpretation in a deep learning framework

Fig. 2

Mean and standard deviation of the hip, knee, and ankle joint trajectories in the sagittal plane for six of the seven freezers who experienced FOG during the protocol (a), with the excluded subject discussed separately (b), and the fourteen non-freezers and fourteen healthy control subjects (c). The joint trajectories are colorized with the relevance map (heatmap) ${\sum }_x{R}_x^{(1)}$ using $ϵ$-LRP. To ensure an equal contribution, six strides (three pre-FOG and three FGC) are used of each freezer, with exception of subject seven who only froze once. For the non-freezers (NF) and healthy control (HC) subjects, all 2421 and 2258 strides were used. For the attribution plots of the freezers (a and b), the error clouds depict the standard deviations of the pre-FOG trajectories (gray) and FGC trajectories (green). For the attribution plots of the NF and HC (c), the error clouds depict the standard deviations of NF trajectories (green) and HC trajectories (gray). Positive relevance (red) indicates contribution to FOG, while negative relevance (blue) indicates contribution to FGC