Undergraduate Senior Project - Self-Sanitizing Mask
In recent years, using inexpensive wearable sensors and mobile technologies (mHealth technologies), in home-based rehabilitation, have demonstrated effectiveness and improved quality of life The overarching goal of this project is to develop and validate a new closed-loop mHealth technology, namely media-instrumented gait training, to facilitate the self-management capacity of long-term home rehabilitation for improvement of gait anomalies and balance, and decreased fall risks among people with neurological disorder. This novel wearable media-instrumented gait sensing training system is capable of guiding gait rehabilitation via rhythmic auditory stimulation and quantifying gait performance, by utilizing a user-centric biofeedback and just-in-time auditory cueing system to improve patient’s functional mobility.
Cultural-Specific Smoking Cessation Program for Economically Disadvantaged African Americans
The tobacco smoking prevalence in Cleveland is more than double the national average (35% Cleveland, 15% national) so that the Cleveland area becomes an appropriate location to design and implement a living lab of technology-inspired, smoker-centered, and economically viable solutions to address these intractably complex socio-economic smoking cessation challenges. In this study, we design a novel smoking cessation system that combines motion detection and an Android app to monitor smoking in real-time. A personalized smoking cessation plan could be created based on the goal of complete cessation or smoking reduction. The mobile system will monitor user’s smoking activity and provide just-in-time message intervention. Our findings have implications for new tobacco cessation treatment delivery and novel assessment of smoking status, such as tracking progress, relapse, and transition of each stage of the smoking cessation.
Precision HIV Health App Powered by Data Harnessing, AI, and Learning
Mobile phone applications provide a new and easy-access health management platform that can be used to provide disease prevention and care. Recent studies have shown that mobile interventions have positive effects in adhesive to care program and antiretroviral therapy, self-management of disease, and is also critical in decreasing the HIV pandemic, and stigmatization. Partnering with the MetroHealth clinical researchers, we develop an intelligent social platform to help the targeted youth for the prevention and care of human immunodeficiency virus (HIV) and sexually transmitted diseases. Artificial Intelligence (AI) and Natural language processing (NLP) methods are applied for improving user engagement and providing a compact platform for users to better find the answers to their questions and concerns. The designed App features include smart medication reminders, personal lab test tracking, and daily inspirational message support, private consultation, and HIV health self-management resources.
The past decades have witnessed great progress in the prosthetics field through new materials and technologies such as targeted muscle reinnervation, powered knee, and ankle prosthesis. However, the biomechanical load due to the unnatural mechanical interaction between the soft tissues of the residual limb and the prosthetic socket is still not fully understood and needs to be further investigated. A poorly-fitting socket can lead to chronic skin problems, including pressure ulcers, dermatitis, infections, and pain, which seriously affect a patient’s health and quality of life. This project explores an accurate force-sensing design using a unique three-dimensional nanowall network structure, which allows the fabrication of a robust sensor array on a flexible platform for quantitative pressure and shear force measurement. This interdisciplinary research project includes investigators from Cleveland Veterans Affairs (VA) Medical Center and Ohio State University and conducts research on sensor device fabrication, stump-socket interface monitoring, and machine learning approaches to analyze the sense of comfortable fit.
Intelligent Assistant for Infrastructure Inspection Worker Health and Safety
Intelligent assistant design powered by AI will be viable through close integration of advanced technology with human to support the data-driven infrastructure-related decision-making process. Integration of advanced assistant technology will help improve the inspection worker’s efficiency, health welfare, and safety. In this study, we developed a smartphone and wearable solution to identify and visualize work-related injuries, such as slip, trip, and fall (STF), and musculoskeletal disorders (WMSD) risk factors such as awkward postures, force exertions, and excessive repetitions through data-driven approaches, which enable objective worker risk factors identification in their daily routine.
Deep learning has been becoming a promising focus in data mining research. With deep learning techniques, researchers can discover deep properties and features of events from quantitative mobile sensor data. However, many data sources are geographically separated and have strict privacy, security, and regulatory constraints. Upon releasing the privacy-sensitive data, these data sources generally no longer physically possess their data and cannot interfere with the way their personal data being used. Therefore, it is necessary to explore distributed data mining architecture which is able to conduct consensus learning based on needs. We developed a distributed deep-learning optimized system (DDOS) that contains a cloud server and multiple smartphone devices with computation capabilities, and each device is served as a personal mobile data hub for enabling mobile computing while preserving data privacy. DDOS system keeps the private data locally in smartphones, deploys computation model, shares trained risk factor parameters, and builds a consensus risk factor classification model incrementally among all participated smartphones and wearable devices.
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