Global rise in life expectancy has resulted in an increased demand for affordable healthcare and monitoring services. The advent of miniature and low–power sensor technologies coupled with the emergence of the Internet–of–Things has paved the way towards affordable health monitoring tools in wearable platforms. However, ensuring power–efficient operation, data accuracy and user comfort are critical for such wearable systems. This thesis focuses on the development of accurate and computationally efficient algorithms and low–cost, unobtrusive devices with potential predictive capability for monitoring mobility and cardiac health in a wearable platform.
A three–stage complementary filter–based approach is developed to realize a computationally efficient method to estimate sensor orientation in real–time. A gradient descent–based approach is used to estimate the gyroscope integration drift, which is subsequently subtracted from the integrated gyroscope data to get the sensor orientation. This predominantly gyroscope–based orientation estimation approach is least affected by external acceleration and magnetic disturbances.
A two–stage complementary filter–based efficient sensor fusion algorithm is developed for real–time monitoring of lower–limb joints that estimates the IMU inclinations in the first stage and uses a gradient descent–based approach in the second stage to estimate the joint angles. The proposed method estimates joint angles primarily from the gyroscope measurements without incorporating the magnetic field measurement, rendering the estimated angles least affected by any external acceleration and insensitive to magnetic disturbances.
An IMU–based simple, low–cost and computationally efficient gait–analyzer is developed to track the course of an individual's gait health in a continuous fashion. Continuous monitoring of gait patterns can potentially enable detecting musculoskeletal or neurodegenerative diseases at the early onset. The proposed gait analyzer identifies an anomalous gait with moderate to high accuracy by evaluating the gait features with respect to the baseline clusters corresponding to an individual’s healthy peer group. The adoption of a computationally efficient signal analysis technique renders the analyzer suitable for systems with limited processing capabilities.
A flexible dry capacitive electrode and a wireless ECG monitoring system with automatic anomaly detection capability are developed. The flexible capacitive electrode reduces motion artifacts and enables sensing bio–potential over a dielectric material such as cotton cloth. The virtual ground of the electrode allows for obtaining single–lead ECG using two electrodes only. ECG measurements obtained over different types of textile materials and in presence of body movements show comparable performance to other reported ECG monitoring systems. An algorithm is developed separately as a potential extension of the software to realize automatic identification of Atrial Fibrillation from short single–lead ECGs.
The association between human gait and cardiac activities is studied. The gait is measured using wearable IMUs and the cardiac activity is measured with a single–lead handheld ECG monitor. Some key cardiac parameters, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation. These associations between gait and heart can be useful in realizing low–cost in–home personal monitoring tool for early detecting CVD–related changes in gait features before the CVD symptoms are manifested. / Thesis / Doctor of Philosophy (PhD) / Wearable health monitoring systems can be a viable solution to meet the increased demand for affordable healthcare and monitoring services. However, such systems need to be energy–efficient, accurate and ergonomic to enable long–term monitoring of health reliably while preserving user comfort.
In this thesis, we develop efficient algorithms to obtain real–time estimates of on–body sensors' orientation, gait parameters such as stride length, and gait velocity and lower–limb joint angles. Furthermore, we develop a simple, low–cost and computationally efficient gait–analyzer using miniature and low–power inertial motion units to track the health of human gait in a continuous fashion.
In addition, we design flexible, dry capacitive electrodes and use them to develop a portable single–lead electrocardiogram (ECG) device. The flexible design ensures better conformity of the electrode to the skin, resulting in better signal quality. The capacitive nature allows for obtaining ECG signals over insulating materials such as cloth, thereby potentially enabling a comfortable means of long–term cardiac health monitoring at home. Besides, we implement an automatic anomaly detection algorithm that detects Atrial Fibrillation with good accuracy from short single–lead ECGs.
Finally, we investigate the association between gait and cardiac activities. We observe that some important cardiac signs, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26105 |
| Date | January 2020 |
| Creators | Majumder, Sumit |
| Contributors | Deen, M. Jamal, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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