Date: 22nd Feb 2019

ARMED named as finalist at Digital Health & Care Awards 2019

ARMED (Advanced Risk Modelling for Early detection) was named finalist for the Independent Living Award at this year’s Digital Health & Care Awards. The announcement was made last night, at the ceremony that took place at the Waldorf Astoria, Edinburgh.

ARMED, is an innovative prevention and self-management solution from H.A.S. Technology and combines pioneering predictive analytics modelling (developed with Edinburgh Napier University and Loreburn Housing Association) with innovative wearable technology, and health and social care data.

This technology-enabled care solution is a powerful tool that can identify risks for the elderly (including risk of falling), earlier in the care cycle. ARMED provides technology-enabled care solutions to empower people to remain independent in their home for longer.  In addition to monitoring the individual, the wearable device also allows issues to be picked up in the wider home setting, helping improve quality of life.

Brian Brown, Director of ARMED (Early Intervention & Prevention Solutions) said:

“We are delighted to be recognised as part of the Independent Living Award.  Our wearable devices detect early indicators of frailty and escalating risk such as activity/inactivity levels, sleep patterns, low grip strength, muscle mass and hydration levels. Predictive analytics modelling - developed in partnership with Edinburgh Napier University - then uses data to predict the risk of a potential fall and allow intervention.  Examples of this are now demonstrating escalations of risk being identified up to 32 days in advance of previously identified falls.  This is transforming future health and social care provision identifying a 25 : 1 save to spend ratio for where ARMED has been used”

“We are delighted to be recognised as part of the Independent Living Award.  Our wearable device detects early indicators of frailty, such as low grip strength, muscle mass, hydration levels, low heart rate and heart rate variability. Predictive analytics modelling - developed in partnership with Edinburgh Napier University - then uses data to predict the risk of a potential fall and allow intervention.  Examples of this are now demonstrating escalations of risk being identified up to 32 days in advance of previously identified falls.”

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