Publication Type
Conference Paper
Version
acceptedVersion
Publication Date
5-2023
Abstract
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.
Keywords
Time-Series Data, Explainable AI, Stroke Rehabilitation Exercises
Discipline
Artificial Intelligence and Robotics | Health Information Technology | Numerical Analysis and Scientific Computing
Publication
ICLR 2023 Workshop on Time Series Representation Learning for Health
First Page
1
Last Page
9
City or Country
Virtual
Citation
LEE, Min Hun and CHOY, Yi Jing.
Exploring a gradient-based explainable AI technique for time-series data: A case study of assessing stroke rehabilitation exercises. (2023). ICLR 2023 Workshop on Time Series Representation Learning for Health. 1-9.
Available at: https://ink.library.smu.edu.sg/sis_research/8580
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://arxiv.org/abs/2305.05525
Included in
Artificial Intelligence and Robotics Commons, Health Information Technology Commons, Numerical Analysis and Scientific Computing Commons