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Ocuvera Automated Video Monitoring

Automated Video Monitoring system for fall risk reduction in hospital settings

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Who is your idea designed for and how does it enable older adults to live their best possible life by preventing falls?

Ocuvera is designed for use in hospital settings for the benefit of patients and staff alike. Older adults, particularly those aged 65 and over, are at a higher risk of suffering injurious falls. Our project seeks to reduce this risk through the implementation of an Automated Video Monitoring system, which can automatically predict unassisted bed exits before they occur and alert nursing staff that a patient might need assistance.

The system uses existing hospital networks to interconnect camera units in patient rooms to mobile devices carried by nurses to allow hospital personnel to view real-time video of patients. When the system detects a potential bed exit, video-based notifications are sent to nursing staff, allowing them sufficient time to assess the patient and assist with unmet needs that initiated the attempt to exit the bed. 

Patient falls are a common, costly, and serious problem in hospitals and care facilities. Conventional fall prevention technologies only alert hospital personnel to a small range of activities that could indicate a broad range of possible situations and patient intentions, and without visual feedback, this decreases effectiveness in both preventing falls and delivering useful and actionable information.  

Currently, there is little to no research or data on what patient activity and behaviors most commonly precede falls. In developing our product, we have collected a large amount of data in the form of 3D footage of real patients in rooms during their hospital stays. We continue to analyze this data to gain a better understanding of what precedes and indicates dangerous fall situations.  

With improved knowledge of specific patient behaviors and postural changes that are antecedents of unattended bed exits, Ocuvera’s goal is to create an innovative product that gives nursing staff the opportunity to intervene before a potential fall occurs. We have developed preliminary algorithms to detect postural changes (i.e. supine – sidelying – sitting – standing) and predict patient attempts to exit the bed, which are based on many standard and nonstandard computer vision techniques, including: coordinate system conversion, 3D object modeling, motion tracking, background identification, and object segmentation. They also include machine-learned artificial intelligence algorithms such as decision tree risk assessment, decision forest pixel classification, and convolutional neural network classification, all of which are designed by Ocuvera’s computer vision team.  

The Automated Video Monitoring approach to fall risk reduction has many advantages. Our predictive algorithms allow our technology to give nursing staff early warning of a potential bed exit, with adequate time to respond. Most existing competitive fall prevention technologies activate only after a fall or bed exit has occurred. Our system also has a low false positive rate and a high rate of accurate bed exit detection. Existing technologies typically have high false alarm rates that lead to nurse fatigue and are thus underutilized for fall prevention. Unlike patient restraints, which are associated with an increased rate of injury without decreasing falls rates, the nature of Ocuvera still allows patients independence and freedom of movement.  

Ocuvera also has the benefit of being a cost-effective solution. The mobile nature of our system gives nursing staff the ability to move and set up the intervention quickly and smoothly wherever it’s needed. This flexibility allows hospitals to afford Ocuvera at the level of their specific size and occupancy needs.

ocuvera.com

What early, lightweight experiment might you try out in your own community to find out if the idea will meet your expectations?

Currently, we need rigorous scientific validation of the efficacy of the Ocuvera system. Based on simulations, our system would have alerted nursing staff to over 95% of unattended exits with a false alarm rate around 30%. We are currently testing a prototype of the full system and are planning for formal feasibility studies. At hospitals where Ocuvera is deployed for field-testing, results indicate fewer unattended bed exits, more exits where nursing staff responds, and faster response times.

What skills, input, or guidance from the OpenIDEO community would be most helpful in building out or refining your idea?

We are a team of software developers. We know how to run a business, how to solve mathematical problems, and how to engineer quality software: we have built and sold numerous companies and products. However, this is our first medical product. We could use assistance in bringing technology to market in the health care space, in executing rigorous testing and efficacy validation, and in seeking partners in the medical field.

How long has your idea existed?

  • Over 1 year

This idea emerged from

  • A group brainstorm

Tell us about your work experience:

Our team includes: a CEO who has developed over a dozen companies, computer vision experts including Ph.D. mathematicians, software architects, veterans, an MBA, and a hardware technician. We are partnering with fall risk reduction researchers at the University of Nebraska-Medical Center.

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Photo of Jawad
Team

Hey, I think this is a great idea. I believe that this technology can be used in all kinds of areas. It doesn't have to be limited to only one area. For example, automated video recording can be used in specific homes. You would have to have a caretaker in each home however.

With the right kinds of people, I think this idea can be greatly beneficial to elderly people in preventing falls.

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