As senior care shifts from reactive to proactive, more and more communities are utilizing the health insights identified by CarePredict’s AI and deep-learning models to find the earliest precursors to declines in health. The current standard in Senior care measures physiological indicators to manage chronic conditions and is a reactive model. But this is insufficient as it only addresses a small fraction of a whole person. With CarePredict Tempo, we combine those physiological indicators with our unique activity and behavior signals to find new digital biomarkers that are predictive of health declines such as risk of falls, UTIs, depression or nutrition deficits.

The Tempo is a wrist-worn wearable that houses a sophisticated combination of sensors and unique kinematic algorithms to detect a person’s activities and behaviors such as eating, bathing, sleeping, cooking and many more to help predict risks to wellbeing. The Tempo utilizes cutting-edge machine learning to provide deep insights without having to resort to creepy cameras or spooky radar sensors. The Tempo has no display or crown to fidget with, but with a light press of the button, the Tempo announces the time, day of the week, and battery level –   specifically designed for older adults as a voice-first device! It never needs to be taken off the wrist as the rechargeable, and swappable battery slides out and is replaced with a freshly charged one!  Each Tempo comes with two batteries and a personal 2-port battery charger. The Care Staff knows when the battery needs to be swapped and can assist as needed. The Tempo uses an industry-standard 22mm band and the included Velcro fabric band can be replaced with many readily available off-the-shelf options to personalize it. The Tempo is worn by residents on the wrist and by care staff and visitors on lapel clips or lanyards.

The battery-powered miniature Context Beacons provide the precise location of a person along with the type of room they are in. This context allows the CarePredict System to better understand the activity and behavior of the resident.  Using this contextual information, the Tempo can optimize which algorithms it runs to detect appropriate behaviors within that context. For example, it won’t try to detect eating when the person is in the bathroom but instead would increase the sensitivity of the fall detection algorithm as most falls occur in the bathroom. Knowing the exact location (e.g., dining room, bedroom, bathroom, art studio) of every person in the community means the system can automatically compute resident-staff interaction times, detect and document assisted activities and even direct third-party caregivers to the precise location of a resident.

Insights, to be useful, must be delivered in a timely manner to the right people, at the right time. CarePredict delivers actionable insights through real-time push notifications, web-based dashboards, scheduled emailed reports and self-serve on-demand reports. Insights requiring immediate attention are served up to care staff through configurable web, app, text and email alerts.

– Eating Duration
– Overall 1-10 Activity Score
– Walking Duration
– Sleep minutes
– Bathroom frequency
– Bathroom Duration
– Wake up at Night
– Socialization