Abstract - New technique for real-time interface pressure analysis: Getting more out of large image data sets

1Cleveland Advanced Platform Technology Center, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH; 2Department of Orthopaedics, Case Western Reserve University, Cleveland, OH; 3Department of Quantitative Health Sciences, The Cleveland Clinic, Cleveland, OH; 4Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH; 5Departments of Radiology and Biomedical Engineering, Case Western Reserve University and University Hospitals Case Medical Center, Cleveland, OH; 6Department of Statistics, Case Western Reserve University, Cleveland, OH

Abstract — Recent technological improvements have led to increasing clinical use of interface pressure mapping for seating pressure evaluation, which often requires repeated assessments. However, clinical conditions cannot be controlled as closely as research settings, thereby creating challenges to statistical analysis of data. A multistage longitudinal analysis and self-registration (LASR) technique is introduced that emphasizes real-time interface pressure image analysis in three dimensions. Suitable for use in clinical settings, LASR is composed of several modern statistical components, including a segmentation method. The robustness of our segmentation method is also shown. Application of LASR to analysis of data from neuromuscular electrical stimulation (NMES) experiments confirms that NMES improves static seating pressure distributions in the sacral-ischial region over time. Dynamic NMES also improves weight-shifting over time. These changes may reduce the risk of pressure ulcer development.

Key words: biomedical imaging, gluteal stimulation, LASR, longitudinal analysis and self-registration, interface pressure mapping, neuromuscular electrical stimulation, pressure ulcers, real-time statistical analysis, rehabilitation, seating pressure, self-registration, translational research.

Abstract - New technique for real-time interface pressure analysis: Getting more out of large image data sets

Kath Bogie, DPhil;1-2* Xiaofeng Wang, PhD;3-4 Baowei Fei, PhD;5 Jiayang Sun, PhD6