A Bit About Us

The FALLS Predict™ initiative was born out of a challenge presented to Andrew Cowen, Founder and CEO, The Future Care (UK) Ltd, while working with Cambridge, Digital Catapult and Cambridge Wireless ‘BOOST’ programme. The original objective was to extend and develop a more robust Cambridge’s LoRaWAN network.

At the time 2016, Future Care was a value added reseller for a vital sign remote monitoring device and ecosystem for seniors. The equipment included a gateway box that was placed in the senior’s home. To extend the Cambridge’s LoRaWAN network all we had to do was include a LoRaWAN gateway board into the box – job done!

However, Andrew Cowen was not content with this simple solution and wanted to explore the potential of health, smart homes and cities. Based on his previous background of working with seniors both as a family care giver and as founder of a home care agency he was more than aware  of the impact a fall had on seniors and the astronomical cost this had on the public purse.

FALL Predict™ was born out of wanting to solve this problem. To achieve this a consortium as formed consisting of members across the EU.

IoT Real-time Data Capture

To successfully predict falls we need access to the data generated by house and/or city IoT sensors. In some instances this data can be sent either in real-time or in regulated intervals according to its  relevance.

Big Data sets Integration

To identify the cause of a fall and predict it happening in the future we need access to a wide variety of historical and real-time data sets. For example meteorological real-time data integrated with medical and ambulance data recorded at a time of a fall in a particular are, time and circumstance.  The process is also further complicated by the fact that most data is in dissimilar formats and structures.

To achieve this, is a skill set and data science service in its own right

Data Analytics

Our services offers the best in analysing data for an end objective or to explore what we cannot see. Our approach can either be based on a hypothesis or more inquisitive seeking data hotspots to help us focus and drill down into these areas which may well offer insights we would not have considered.

Data Visualisation

More often than not data is represented in a liner form of juxtapositions of numbers. However this might not with be legible or comprehensible. Visualisation techniques will help us to analyse data relevant to predicting falls and present it in a fashion we understand and can respond to.

Meaning Interpretation of Data

Phasellus enim libero, blandit vel sapien vitae, condimentum ultricies magna et. Quisque euismod orci ut et lobortis aliquam. Aliquam in tortor enim.

Other Smart City Service

While our focus ion on predicting falls and then seeking to prevent them, it does not preclude us for applying these skill and vision to other areas of interest. The same technology and approach can apply to prediction of air quality, or other types of predictive objectives.

Consortium Foundation

The Consortium is made up of experts that when combined prepresent the full compliment of skills and experience necessary to develop this FALLS Predict product and service.

Besides the network of skills and experience helping to develop the FALLS Predict™ initiative, the consortium is made up of representative organisation located both the UK and EU to ensure that the solution meets the demands of a cross section of environmental impacts as well as and location specific challenges.

Our Skills

Product & Services Design

70%

Health & Wellness Monitoring

80%

System Design & Integration

100%

Data Analysis and Visualisation

90%

Collaboration & Project Management

75%

* The costs of falls are high, both to the individual, carers and society. The health care expenditure for treating fall-related injuries in the EU is estimated to be 25 billion Euros each year.
* A sample of over 200 hospitals across Europe, estimated within the EU, 3.8 million older people/annum attend emergency departments (ED) with a falls related injury; of which 1.4 million are admitted to hospital for further treatment.

EUPHASource: https://eupha.org/repository/sections/ipsp/Factsheet_falls_in_older_adults_in_EU.pdf

* 1:6 fall-related injuries occur on public roads (mainly side-walks)
* Falls often lead to post-fall anxiety, fear and subsequent dependency on family carers or even admittance into nursing care facilities.

Title of Research: Impact of falls on body and location where falls occurredSource: https://eupha.org/repository/sections/ipsp/Factsheet_falls_in_older_adults_in_EU.pdf

* The Public Health Outcomes Framework (PHOF) reported 2017-18
* Around 220,160 falls emergency hospital admissions among patients aged 65+, (66.6%) of these patients aged 80+
* Falls – 9th highest cause of disability-adjusted life years (DALYs) in England in 2013 and the leading cause of injury
* Unaddressed fall hazards in the home cost the NHS in England £435+ million
* Annual cost of fragility fractures to the UK, cost estimated £4.4 billion which includes £1.1 billion for social care; hip fractures account for around £2 billion of this sum
* Short and long-term outlooks for patients following a hip fracture, with an increased one-year mortality of between 18% and 33% and negative effects on daily living activities such as shopping and walking.
* Long-term disability review found approx. 20% of hip fracture patients entered long-term care in the first year after fracture
* Falls in hospitals are the most commonly reported patient safety incident with more than 240,000 reported in acute hospitals and mental health trusts in England and Wales
* Falls and fractures in older people are a costly and often preventable health issue

Title of Research: Source: https://www.gov.uk/government/publications/falls-applying-all-our-health/falls-applying-all-our-health

Team

The FALLS Predict™ consortium consists of the following members: