Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management | JoVE Visualize

Area of Science:

Biomedical Engineering
Clinical Nursing
Rehabilitation Technology

Background:

Regular repositioning is crucial for preventing and treating pressure injuries (PIs) in at-risk individuals.
Non-contact position detection systems using bed load cells offer a promising approach for monitoring patient position.
Previous research indicated a need for higher precision in position detection to ensure off-loading of high-risk bony prominences.

Purpose of the Study:

To evaluate the impact of increased position categorization precision on the performance of a non-contact bed sensor system.
To determine if higher precision in predicting patient position improves the system's ability to identify off-loading of pelvic bony prominences.
To assess the relationship between position category bin size and the prediction F1 score of the developed system.

Main Methods:

Utilized data from 18 participants, collected via load cells under bed legs and a pelvis-mounted inertial measurement unit.
Trained classifiers to predict transverse pelvic angle using three distinct position bin sizes: 45°, ~30°, and 15°.
Employed a leave-one-participant-out cross-validation approach to rigorously evaluate classifier performance for each bin size.

Main Results:

The prediction F1 score demonstrated a decline as the precision of position categorization was increased.
Classifiers trained with larger position bins (lower precision) achieved higher F1 scores compared to those trained with smaller bins (higher precision).
This suggests that the system's accuracy in predicting patient position is inversely related to the desired level of positional detail.

Conclusions:

Increasing the precision of position detection in the developed non-contact system led to a decrease in prediction accuracy (F1 score).
The findings suggest that simpler, less precise position classifications may be more reliable for this specific sensor technology.
Further research is needed to optimize sensor algorithms or explore alternative methods for achieving accurate, high-precision position detection for pressure injury prevention.