A method of capturing the initial, fused sensor data other key metrics such as the pressure map, body structure, mobility, activity and physiological data. Using machine learning approaches, an individualized profile is built to monitor the effect of tissue’s oxygen deprivation and waste buildup.
A highly accurate image-based algorithm for classification of a patient’s posture on bed which can be used with different pressure mapping systems.
Identifies and tracks at-risk limbs/regions of the body and their corresponding statistics for more accurate monitoring and risk assessment.
A mathematical model that utilizes the tissue stress-recovery and generates an optimal repositioning schedule to minimize nursing effort while ensuring that no region ever exceeds its stress threshold.
Finds an efficient repositioning schedule for bed-bound patients based on their risk of ulcer development. The patient-specific turning schedule minimizes the overall cost of nursing staff involvement in repositioning the patients while simultaneously decreasing the chance of pressure ulcer formation.
An efficient and user-friendly visualization and summary report of the data that is collected and analyzed. The processed data is communicated effectively to caregivers for monitoring, prevention and management of ulcer.
Advanced technology to aid in pressure ulcer risk assessment, prevention and management.
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