High-Precision Spatial Tracking of a Rotating Medical Device Around a Wound Bed Using Sensor Fusion and Visual SLAM

PhD Project

Project Overview

This research explores Visual SLAM (Simultaneous Localization and Mapping) combined with IMU-based sensor fusion to achieve millimeter-level positional accuracy and robust orientation tracking. The proposed systems integrate embedded hardware with depth-sensing technology and advanced algorithms, such as the Extended Kalman Filter (EKF).

This project has the potential to transform wound care by enabling reproducible measurements, supporting telemedicine, and laying the foundation for robotic-assisted clinical interventions.

Research Context & Motivation

The rationale for this research lies in its potential to significantly improve the accuracy and reliability of wound assessment procedures. By integrating depth-sensing cameras (e.g., Intel RealSense) with IMU data from embedded systems, the project aims to deliver millimeter-level positional accuracy and precise orientation tracking. This capability is essential for creating reproducible wound measurements, enabling better clinical decisions, and supporting the development of automated or robotic wound care systems in the future.

This project offers a pathway for a doctoral student to contribute to multidisciplinary innovation in medical technology, embedded systems, and computer vision. Building on expertise in virtual acoustics, embedded DSP, and interfacing techniques, future research could redefine what is possible in location tracking, ultimately improving quality of life through advanced assistive technologies.

The outcomes of this research offer direct applications across healthcare, telemedicine, and assistive robotics, making it both academically challenging and practically impactful. The student will have the opportunity to develop advanced skills in:

  • Hardware Integration: Embedded systems design and depth-sensor interfacing.
  • Software Development: System programming using C++ and Python.
  • Algorithm Design: Implementing Extended Kalman Filters (EKF), state estimation, and spatial mapping pipelines.

Application & Course Details

Interested candidates should review the formal course details below for their application:

Detail Specification
Course Title Engineering (PhD)
Mode of Attendance (MoA) Full-Time (FT)
Admissions Email gs.pgradmissions@hud.ac.uk

Supervisory Team