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The world’s population is aging faster than ever. Incidences of chronic conditions are increasing. Plus, there is a persistent and severe shortage of healthcare workers. These trends have taken hold and are not expected to change anytime soon.
What if care providers could recognize and act on persons’ behaviors before there’s any visible sign of a health problem? Early intervention can support longer, healthier, and more independent lives. Traditional approaches to home behavior monitoring typically detect problems after they occur and call attention to them—falling short of predictive potential.
Our passive, non-invasive, whole-home monitoring solution enables predictive capabilities and proactive notifications to create a better context for care.
We’re analyzing home energy usage patterns to find 'signals in the noise' to support a better context for care. We’re studying home energy patterns, creating a household energy-use fingerprint, to identify behavior anomalies so caregivers can quickly take action.
This personalized energy fingerprint starts with our smart breakers. Providing vital functionality in virtually any home or congregate care setting, our smart breakers support safe, reliable power and enable granular energy data with cloud connectivity. Our smart breaker is also a gamechanger for remote monitoring—passively and non-invasively enabling whole-home energy data collection and analysis.
Using measurable inputs like electricity (and water usage), we apply artificial intelligence and machine learning to inferentially sense* relationships and patterns related to the hard-to-measure activities of daily living. Our data models and algorithms, developed by our Center for Intelligent Power can identify changes in these types of activities and help remote caregivers respond to changes in occupants’ behaviors.
All of this data can be delivered to care providers via API to be ingested by existing digital healthcare platforms to provide a context of patient or resident behaviors, allowing caregivers to better understand and respond to their needs.
Whether smart ambient household monitoring is used independently or integrated with other systems, it provides a cutting-edge and proven approach to support a more informed context for care.
Key takeaways:
Intelligent power management technologies are being put to use to help care providers create a more complete understanding of their patients' daily lives so they can repond better to their needs.
Inferential sensing refers to the process of making observations and predictions about an occurrence that cannot be directly observed by using available data. We use inferential sensing to organize the energy usage patterns that come from activities of daily living to create an abstract of what 'normal' looks like. When there is a deviation from normal, we 'infer' it may be an indication of a situation that a care giver may want to address.
