Fatigue Assessment using ECG and Actigraphy Sensors

Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, we developed an automated system using wearable sensing and machine learning techniques for objective fatigue assessment. ECG/Actigraphy data were collected from subjects in free-living environments. Preprocessing and feature engineering methods were applied, before interpretable solution and deep learning solution were introduced. Specifically, for interpretable solution, we proposed a feature selection approach which can select less correlated and high informative features for better understanding system's decision-making process. For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment. Extensive experiments were conducted, and very promising results were achieved.Comment: accepted by ISWC 202

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states. Existing automatic protective behavior detection (PBD) methods rely on pre-segmentation of activities predefined by users. However, in real life, people perform activities casually. Therefore, where those activities present difficulties for people with chronic pain, technology-enabled support should be delivered continuously and automatically adapted to activity type and occurrence of protective behavior. Hence, to facilitate ubiquitous CP management, it becomes critical to enable accurate PBD over continuous data. In this paper, we propose to integrate human activity recognition (HAR) with PBD via a novel hierarchical HAR-PBD architecture comprising graph-convolution and long short-term memory (GC-LSTM) networks, and alleviate class imbalances using a class-balanced focal categorical-cross-entropy (CFCC) loss. Through in-depth evaluation of the approach using a CP patients' dataset, we show that the leveraging of HAR, GC-LSTM networks, and CFCC loss leads to clear increase in PBD performance against the baseline (macro F1 score of 0.81 vs. 0.66 and precision-recall area-under-the-curve (PR-AUC) of 0.60 vs. 0.44). We conclude by discussing possible use cases of the hierarchical architecture in CP management and beyond. We also discuss current limitations and ways forward.Comment: Submitted to PACM IMWU

An analysis of human movement accelerometery data for stroke rehabilitation assessment

Ph. D. Thesis.Human Activity Recognition (HAR) is concerned with the automated inference of what a person is doing at any given time. Recently, small unobtrusive wrist-worn accelerometer sensors have become affordable. Since these sensors are worn by the user, data can be collected, and inference performed, no matter where the user may be. This makes for a more flexible activity recognition method compared to other modalities such as in-home video analysis, lab-based observation, etc. This thesis is concerned with both recognizing subjects activities as well as recovery levels from movement-related disorders such as stroke. In order to perform activity recognition or to assess the degree to which a subject is affected by a movement-related disease (such as stroke), we need to create predictive models. These models output either the inferred activity (e.g. running or walking) in a classification model, or else the inferred disease recovery level using either classification or regression (e.g. inferred Chedoke Arm and Hand Activity Inventory Score for stroke rehabilitation assessment). These models use preprocessed data as inputs, a review of preprocessing methods for accelerometer data is given. In this thesis, we provide a systematic exploration of deep learning models for HAR, testing the feasibility of recurrent neural network models for this task. We also discuss modelling recovery levels from stroke based on the number of occurrences of events (based on mixture model components) on each side of the body. We also apply a MultiInstance Learning model to model stroke rehabilitation using accelerometer data, which has both visualization advantages and the potential to also be applicable to other diseases