ORCID ID

https://orcid.org/0009-0006-1231-0796

Date Awarded

2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Computer Science

Advisor

Gang GZ Zhou

Committee Member

Bin BR Ren

Committee Member

Huajie HS Shao

Committee Member

Andreas AS Stathopoulos

Committee Member

Honggang HW Wang

Abstract

In the field of ubiquitous computing, health-related problem analysis has gained increasing attention. Collaborations between domain doctors and computing researchers have been established to recognize and address health-related issues. However, accurate detection and recognition of health-related problems remain a major challenge that attracts extensive research efforts. Among all the research works, wearable sensors-based health-related problem recognition is promising as it is low cost, low power, and easy to carry. This dissertation focuses on utilizing wearable sensors to study health-related problems. The first project introduced in this dissertation is TremorSense, a PD tremor detection system designed to classify Parkinson's Disease hand tremors. PD hand tremors are common symptoms in all stages of PD and have a severe influence on patients' daily quality of life. TremorSense utilizes accelerometers and gyroscopes as wearable sensors on patients' wrists to collect data from 30 PD patients. An 8-layer Convolutional Neural Network (CNN) was designed to classify PD rest, postural, and action tremors. The CNN model was evaluated with accuracies greater than 94% for all three evaluations. The second project introduces a PD action tremor detection method to recognize PD tremors from regular activities. The method uses a dataset from 30 PD patients wearing accelerometers and gyroscope sensors on their wrists. Hand-crafted time-domain and frequency-domain features were selected and compared with existing CNN data-driven features. Multiple supervised machine learning models were trained for detecting PD action tremors. The performance of all models using the hand-crafted features achieved more than 90% F1 scores. The third project reviews the previous research on Freezing of Gait (FoG) computing, which refers to a sudden and short event in which a patient loses the ability to step forward, commonly experienced by advanced Parkinson's Disease patients. Wearable devices have been explored for detecting and predicting FoG and falls in PD, but a systematic survey is still lacking in this area. This project discusses a series of FoG challenges and future research trends, which will contribute to further research advancement. Finally, AudioPalate is a novel automatic dietary monitoring tool that uses Apple AirPods Pro to classify food types based on audio data. Developed through data from four users and various food types, the system employs preprocessing, Short-Time Fourier Transform for feature extraction, and data augmentation to improve accuracy. Utilizing a Long Short-Term Memory model, it classifies 20 food types with over 85% accuracy in several evaluations, demonstrating its effectiveness in food recognition and its potential to aid in dietary monitoring and healthier eating habits.

DOI

https://dx.doi.org/10.21220/s2-bnej-7543

Rights

© The Author

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