Toward explainable AI-empowered cognitive health assessment

Abstract

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.

Keywords: Internet of Things; advanced sensors; assistive technology; explainable AI; healthcare; human activity recognition; key feature extraction.

Figure 1

Complete flow of the proposed framework.

Figure 2

Raw dataset illustration.

Figure 3

The comparison of the proposed XAI-HAR with feature selection methods such as PCA, mRMR, and IG in combination with RF for each activity of individuals with dementia and healthy individuals.

Figure 4

The comparison of the proposed XAI-HAR with feature selection methods such as PCA, mRMR, and IG in combination with RF for each activity of healthy individuals.

Figure 5

Comparison of proposed XAI-HAR with feature selection methods such as PCA, mRMR, and IG in combination with RF for each activity of individuals with dementia.

Figure 6

Feature explainability.

Figure 7

Local importance.

Figure 8

ELI5-based feature Inspection.

Figure 9

Feature explainability.

Figure 10

Local importance.

Figure 11

ELI5-based feature Inspection.

Figure 12

Feature explainability.

Figure 13

Local importance.

Figure 14

ELI5-based feature Inspection.