Exploring a Gradient-based Explainable AI Technique for Time-Series
  Data: A Case Study of Assessing Stroke Rehabilitation Exercises
Min Hun Lee Yi Jing Choy
Explainable artificial intelligence (AI) techniques are increasingly being
explored to provide insights into why AI and machine learning (ML) models
provide a certain outcome in various applications. However, there has been
limited exploration of explainable AI techniques on time-series data,
especially in the healthcare context. In this paper, we describe a
threshold-based method that utilizes a weakly supervised model and a
gradient-based explainable AI technique (i.e. saliency map) and explore its
feasibility to identify salient frames of time-series data. Using the dataset
from 15 post-stroke survivors performing three upper-limb exercises and labels
on whether a compensatory motion is observed or not, we implemented a
feed-forward neural network model and utilized gradients of each input on model
outcomes to identify salient frames that involve compensatory motions.
According to the evaluation using frame-level annotations, our approach
achieved a recall of 0.96 and an F2-score of 0.91. Our results demonstrated the
potential of a gradient-based explainable AI technique (e.g. saliency map) for
time-series data, such as highlighting the frames of a video that therapists
should focus on reviewing and reducing the efforts on frame-level labeling for
model training.