Baxter International has patented an exit prediction system for support apparatus. The system uses force sensors to detect movement trends without combining data to determine occupant weight, generating notifications based on predictions. GlobalData’s report on Baxter International gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Baxter International, Remote monitored dialysis machines was a key innovation area identified from patents. Baxter International's grant share as of January 2024 was 59%. Grant share is based on the ratio of number of grants to total number of patents.
Exit prediction system for support apparatus using force sensors
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A recently granted patent (Publication Number: US11883158B2) discloses an innovative exit prediction system designed for a bed. The system includes one or more force sensors positioned on the bed frame and a computing device integrated into the bed. The computing device processes data inputs from the force sensors, detects changes in these inputs, and stores predefined changes associated with movement trends. By comparing detected changes to predefined ones, the system determines movement trends indicative of an occupant's intention to exit the bed. Based on these trends, an exit prediction is generated, and a notification is issued on the bed itself. The system operates without combining force sensor data to determine the occupant's weight, enhancing its efficiency and accuracy.
Moreover, the patent claims detail additional features such as the use of analog-to-digital counts derived from analog signals received from the force sensors. Control rules, including Shewhart control logic, are applied to the movement trends to determine exit predictions. The system also incorporates siderail sensors to detect siderail positions and footboard attachments, further refining the exit predictions. Additionally, a method is outlined for predicting occupant exits from a support apparatus, utilizing machine learning to recognize trends and applying control rules for accurate predictions. The method also considers movements like transitioning from supine to sitting positions and rolling from side to side, enhancing the system's predictive capabilities. By integrating advanced technology and sensor data, this patented system offers a sophisticated solution for predicting and notifying potential exits from support apparatuses like beds.
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