Explainable and Robust Data-Driven Machine Learning Methods for Digital Healthcare Monitoring

Digital healthcare monitoring uses multidisciplinary sensing techniques to track diverse human data and behaviors. Machine learning can promote an individual's well-being through more efficient and accurate health status monitoring. However, challenges hinder precise monitoring, such as privacy concerns, varied subjects, diverse sensors, and different objectives. To help address these challenges, this thesis explores projects spanning various healthcare domains. Explainable and robust machine-learning solutions are proposed and tested, which include novel signal processing guidelines, innovative feature engineering methods, and pioneering deep-learning networks. These solutions contribute to the state-of-the-art in their respective healthcare domains.

The first project addressed the challenge of assessing fall risk among individuals with varying levels of mobility using inertial sensors. Machine-learning models were developed and evaluated using datasets from stroke survivors and community-dwelling elders with participants of varying levels of mobility. Risk indicators were obtained through kinematics simplification that are both explainable and modifiable. These indicators considerably enhance fall risk classification performance compared to existing approaches and the conclusions align with available biomechanical evidence.

In the second project, a new machine-learning architecture was created for fall detection and classification using multistatic radar sensing. This new approach (called eMSFRNet) solved the common problem of weak and varied Doppler signatures caused by line-of-sight restrictions. It is the first method that can classify among fall types using radar sensing, and yielded state-of-the-art accuracy for both fall detection (99.3%) and seven fall types classification (76.8%) tasks.

In the third project, a novel combination of signal processing and a machine learning framework (named MIND) was designed to detect and forecast motor restricted and repetitive behaviors (RRBs) among children with autism spectrum disorder (ASD), using data from multiple wearable sensors. Contrary to prior beliefs that such detection or forecasting was unattainable, the novel MIND AI framework offers a comprehensive and generalizable approach. Transition behaviors were first defined and then identified, suggesting the potential to detect behavioral shifts preceding motor RRBs. The new signal monitoring quantification (MQ) guidelines minimize the impacts of inconsistent data caused by individualized sensor placements. MIND achieved 100% accuracy in detecting motor RRBs on new subjects with unfamiliar behavior types and 92.2% accuracy in forecasting motor RRBs.

In conclusion, the work in this thesis showcases the pivotal contributions of robust and explainable machine learning solutions tailored for specific healthcare challenges. These contributions either solve longstanding problems in different healthcare fields or guide new research directions. The new methodologies introduced – including the MQ guidelines, modifiable fall risk indicators, and innovative deep learning models – all help to advance healthcare machine learning applications by merging accuracy with explainability.