Electrical and Electronic Engineering - Theses
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ItemAutomated Assessment of Motor Functions in Stroke using Wearable Sensors( 2022)Driven by the aging population and an increase in chronic diseases worldwide, continuous monitoring of human activities and vital signs have become a major focus of research. This has been facilitated by the advent of wearable devices equipped with miniaturized sensors. Compared to bench-top devices in hospitals and laboratories, wearable devices are popular in improving health outcomes, because of their compact form factors and unobtrusive nature. Stroke, a neurological disorder, is a major concern among all chronic diseases because it causes high rates of death and disability globally every year. Motor deterioration is the most common effect of stroke, leading to one-sided weakness (i.e., hemiparesis), and limiting movements and coordination. Stroke survivors require regular assessments of motor functionality during the acute, sub-acute and chronic phases of recovery, leading to dependence on human intervention and massive expenditures on patient monitoring. Therefore, an automated system for detecting and scoring hemiparesis, independent of continuous specialized medical attention, is necessary. This thesis develops various methods to objectively quantify motor deterioration related to stroke using wearable motion sensors, for automated assessment of hemiparesis. In the first part of the thesis, we use accelerometer data acquired from wrist-worn devices to analyze upper limb movements and identify the presence and severity of hemiparesis in acute stroke, during a set of spontaneous and instructed tasks. We propose measures of time (and frequency) domain coherence between accelerometry-based activity measures from two arms, that correlate with the clinical gold standard National Institutes of Health Stroke Scale (NIHSS). This approach can accurately distinguish between healthy controls, mild-to-moderate and severe hemiparesis through supervised pattern recognition, using a hierarchical classification architecture. We propose additional descriptors of bimanual activity asymmetry, that characterize the distribution of acceleration-derived activity surrogates based on gross and temporal variability, through a novel bivariate Poincare analysis method. This leads to achieving further granularity and sensitivity in hemiparesis classification into four classes, i.e., control, mild, moderate and severe hemiparesis. The second part of the thesis analyzes the quality of spontaneous upper limb motion captured using wearable accelerometry. Here, velocity time series estimated from the acquired data is decomposed into movement elements, which are smoother and sparser in the normal hand than the paretic hand, and the amount of smoothness correlates with hemiparetic severity. Using statistical features characterizing their bimanual disparity, this method can classify mild-to-moderate and severe hemiparesis with high accuracy. Compared to the activity-based features, this method is more interpretable in terms of joint biomechanics and movement planning, and is robust to the presence of noise in the acquired data. In the third part of the thesis, we propose unsupervised methods for bimanual asymmetry visualization in hemiparesis assessment, using motion templates representative of well-defined instructed tasks. These methods are aimed at creating models for assessing the qualitative progression of motor deterioration over time instead of single-point measurements, or when class labels representing clinical severity are not available. We propose variants of the Visual Assessment of (cluster) Tendency (VAT) algorithm, to study cluster evolution through heat maps, by representing instructed task patterns through local timeseries characteristics, known as shapelets. These shapelets transform high dimensional sensor data into low-dimensional feature vectors for VAT evaluation. We show the significance of these methods for efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring, applicable not only to hemiparesis assessment, but also in identifying motor functionality in other neurological disorders or activity recognition problems. Finally, in the fourth part of the thesis, we show applications of the above methods to objectively measure gait asymmetry in stroke survivors, using lower limb position data from wearable infrared markers and camera-based motion capture devices. These methods can efficiently quantify the severity of lower limb hemiparesis, thereby being suitable for automated gait monitoring during extended training and rehabilitation in the chronic phase of recovery.