Non-invasive bone fracture measurements

Abstract

Bone fractures are common injuries that disproportionately affect vulnerable populations, including children and the elderly. Effective monitoring is crucial to prevent complications and ensure proper healing. However, traditional monitoring techniques, such as X-rays, are limited by their intermittent nature, ionizing radiation exposure, and dependency on clinical visits. To address these limitations, this thesis presents a state-of-the-art, IoT-based solution for real-time, noninvasive monitoring of bone fractures through a proof-of-concept model.

The proposed system utilizes two WT9011DCL-BT50 sensors to monitor fracture-related movements on a simulated bone model. By analyzing sensor data, the system accurately detects relative rotational movements, especially in typical wrist bone fractures where angles range from 15 to 30 degrees. However, the system is less effective at detecting linear displacements due to the gradual nature of such movements in bone fractures. Real-time alert mechanisms and visualization further enhance the system’s potential for improving fracture management.

Challenges addressed in this study include sensor noise and drift, as well as magnetic interference affecting rotational measurements. High-pass filtering and motion thresholding techniques were employed to minimize low-frequency drift and detect harmful movements, respectively. Despite some limitations, the model demonstrates the feasibility of using IoT-based sensor technology to continuously monitor bone fractures and detect potentially harmful movements in real-time.

This thesis establishes a foundational approach to next-generation fracture monitoring systems, demonstrating the potential to improve patient care, reduce complications, and enable more personalized rehabilitation strategies.