Degree Discipline

Design of a Low-Cost Spirometer to Detect COPD and Asthma for Remote Health Monitoring (open access)

Design of a Low-Cost Spirometer to Detect COPD and Asthma for Remote Health Monitoring

This work develops a simple and low-cost microphone-based spirometer with a scalable infrastructure that can be used to monitor COPD and Asthma symptoms. The data acquired from the system is archived in the cloud for further procuring and reporting. To develop this system, we utilize an off-the-shelf ESP32 development board, MEMS microphone, oxygen mask, and 3D printable mounting tube to keep the costs low. The system utilizes the MEMS microphone to measure the audio signal of a user's exhalation, calculates diagnostic estimations and uploads the estimations to the cloud to be remotely monitored. Our results show a practical system that can identify COPD and Asthma symptoms and report the data to both the patient and the physician. The system developed can provide a means of gathering respiratory data to better assist doctors and assess patients to provide remote care.
Date: May 2022
Creator: Olvera, Alejandro
System: The UNT Digital Library
FruitPAL: An IoT-Enabled Framework for Automatic Monitoring of Fruit Consumption in Smart Healthcare (open access)

FruitPAL: An IoT-Enabled Framework for Automatic Monitoring of Fruit Consumption in Smart Healthcare

This research proposes FruitPAL and FruitPAL 2.0. They are full automatic devices that can detect fruit consumption to reduce the risk of disease. Allergies to fruits can seriously impair the immune system. A novel device (FruitPAL) detecting fruit that can cause allergies is proposed in this thesis. The device can detect fifteen types of fruit and alert the caregiver when an allergic reaction may have happened. The YOLOv8 model is employed to enhance accuracy and response time in detecting dangers. The notification will be transmitted to the mobile device through the cloud, as it is a commonly utilized medium. The proposed device can detect the fruit with an overall precision of 86%. FruitPAL 2.0 is envisioned as a device that encourages people to consume fruit. Fruits contain a variety of essential nutrients that contribute to the general health of the human body. FruitPAL 2.0 is capable of analyzing the consumed fruit and then determining its nutritional value. FruitPAL 2.0 has been trained on YOLOv5 V6.0. FruitPAL 2.0 has an overall precision of 90% in detecting the fruit. The purpose of this study is to encourage fruit consumption unless it causes illness. Even though fruit plays an important role in people's …
Date: December 2023
Creator: Alkinani, Abdulrahman Ibrahim M.
System: The UNT Digital Library