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.

Machine learning based canine posture estimation using inertial data

“…This allows the application of other machine learning techniques and a fair performance comparison between different methods. In particular, future research should investigate the use of other advanced feature selection techniques [ 54 , 55 ], improve the explainability of models using techniques such as local interpretable model agnostic (LIME) and ELI5 [ 56 , 57 ], and deep learning techniques (CNNs and LSTM networks) for the prediction of postures. Additional data should be added to the dataset to include different postures and other working dog breeds to create more complete automatic ethograms.…”

Designing Interpretable ML System to Enhance Trustworthy AI in Healthcare: A Systematic Review of the Last Decade to A Proposed Robust Framework

“…These will be summarized in Figure 12, but no additional details about the specific method will be provided here as it has been thoroughly described in the literature. In [57], they used local interpretable model agnostic (LIME) with a random forest (RF) classifier to develop an XAIenabled human activity recognition model for those who have chronic impairments. Another study by [70] introduced an interpretable attention-based model for asthma detection using Gradientweighted class activation mapping (Grad-CAM).…”

“…They employed techniques such as noise removal, image resizing, as well as CutMix and MixUp algorithms for data augmentation. Also, in the study of human activity recognition [57], a creative method was introduced called XAI-HAR to feature extraction from data gathered be sensors placed at various locations within a smart home.…”

“…IoT Wearable Medical Devices [114] Diabetes in pregnancy women Blood pressure and pulse rate sensors [20] Respiratory infections -COVID-19 HRV and BPM with wrist-worn biometric wearables [115] Respiratory symptoms for the pandemic MS Band 2 wristbandmonitor cough and fever [2] Hypoglycemia detection and diabetes Smartwatch sensors -detect hypoglycemia [57] Human Activity Recognition Motion sensors, temperature-sensitive devices [116] Chronic obstructive pulmonary disease (COPD) monitoring Smartphone, ECG patch providing day/night movement monitoring, pulse meter providing oximetry monitoring, weight scale, sphygmomanometer for blood pressure monitoring.…”

Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

“…In this context, an eHealth system is capable of real-time monitoring and tracking, recording health information, and storing eHealth records in cloud-based computing infrastructures. Furthermore, it can exchange medical records and health reports and provide a remote diagnosis by connecting different health service providers in a network [145], [146]. Nowadays, eHealth solutions enabled by 6G can be extended to various scenarios, such as hospitals, sports, homes, and pharmacies, in which the QoS for all eHealth applications and services should be ensured in indoor and outdoor environments.…”