Video Analytics Helps Monitor Bedridden Patients

New technology uses a video camera to accurately recognize the status of hospital patients, detecting activities such as sitting up in bed, getting out of bed, or moving in bed.

Developed by Fujitsu Laboratories (Kawasaki, Japan), the technology is based on two modalities. The first recognizes the patient's head and tracks it to identify when the patient sits up or gets out of bed, which can be precursors to actions such as wandering, slipping, or falling. The second detects and visualizes conditions such as restiveness or sleeplessness, which demand further attention. The state of the patient in bed is then classified into five categories depending on posture, and defines a state-transition diagram that relates them to each other.

The recognition process also uses learned data, wherein next likely states are limited by the current state, based on the state-transition diagram. Selecting learned data in the recognition process aids in highly accurate patient head position state. But even with the selection of learned data based on patient status, the potential remains to incorrectly recognize pillows as heads. To correct for that, the technology observes points that will always move when the patient sits up or stands up, and will identify multiple regions in the image that might be the head to confirm if it really is the patient's head, or not.

With the cooperation of Tamagawa Hospital (Tokyo, Japan), Fujitsu Laboratories conducted a field trial to test sensing of sitting up and getting out of bed on several patients. The visualization of patient behaviors was found to perform with 91% accuracy. Behaviors demanding attention were displayed at the nursing station, allowing nurses to easily monitor patient movements without going on rounds, resulting in a high standard of patient protection for the hospital or care facility, while at the same time lightening the workload of the nurses.

Existing techniques to detect when a patient has sat up or gotten out of bed rely on pressure sensors that detect bodyweight; but this approach has some problems. Sometimes patients will intentionally avoid the sensor so that it does not react when they get out of bed. In addition, the sensor cannot distinguish when the patient is turning over while sleeping, or when movements by the nurse are triggering it, resulting in false alarms. And even with the sensors, nurses still need to make frequent checks, and if anything their workload may be higher.

Related Links:
Fujitsu Laboratories
Tamagawa Hospital