precisionlife IoT2017-11-27T17:48:50+00:00

powering fully personalized edge analytics for smart home solutions
  • complex edge analytics delivering personalized decision support for smart IoT, digital health & dietary advice apps

  • predictably small RAM & CPU footprint on PCs, tablets, smartphones/watches & IoT/sensors

  • delivering complex decision support & personalized advice to users solely on their own devices

  • enabling cheaper, smaller & more secure IoT edge/wearable solutions with lower power & data needs

  • uses compiled knowledge models describing complex system behavior via an on-device runtime API

precisionlife IoT is a new IOT edge analytics platform that enables the delivery of fully contextualized responses and advice from the edge devices without having to transmit data back to a data center for processing.

precisionlife IoT enables building of smart home, digital health and wearable applications that can respond not just to generic signals and events, but in the personal context of what is best for the individual user.

Example Project:

In the ASSIST project, built using edge analytics from precisionlife IoT, we are detecting and providing fully contextualized responses to events based on combinations of signals from multiple sensors. These signals are analyzed using the RACE API (described below) running on a gateway device build around a Raspberry Pi Zero to provide appropriate responses in a variety of situations:

  • Automated fall detection and emergency intervention
  • Activity & social interaction
  • Fall prediction based on mobility features
  • Dementia progression via speech and activity features
  • Sleep, light, noise, pollution contextualization for wellness apps

An outline of the ASSIST project is shown below:

In the experimental ASSIST project, we are working with our partner Redgear Solutions to design a new type of smart home / assisted living solutions to support independent living amongst our elderly community.

precisionlife IoT brings together multi-dimensional data from a series of light, temperature, heat, movement and other sensors embedded in a standard light switch.

The RACE Engine and API

precisionlife IoT is build using our patented* RACE Array Logic based Constraint Engine technology. RACE powers our complex data analytics, decision support, smart IoT and embedded sensor/controller solutions.

RACE models complex systems’ behaviors in real-time, where the state of each component in a system may affect the behaviour of others. In these cases, such as personalization of advice in digital health, the problem cannot be simply split across multiple machines (e.g. with Hadoop) as a good solution to one section of the problem may be incompatible with other parts of the problem space.

RACE uses a new branch of maths called Array Logic to unravel the combinatorial complexity of relationships in the system to discover new associations and build high-value predictive models. We build comprehensive models of the problem space, and use the RACE Engine to apply constraints from the real-world to find only those potential solutions that meet all of our criteria. Rather than using machine learning or directed search methods to evaluate all the potential solutions to a problem, we can directly reduce a problem space which might have 101000 potential solutions down to just 1,000, using simple geometrical operators on the logical models. These 1,000 logically valid solutions can then be evaluated and priced for lowest cost/risk and highest benefit.

Critically we can do this in real-time, with a small & predictable memory and CPU footprint and in a provably complete manner (i.e. without taking short-cuts or eliminating any valid solutions) using the RACE API. The RACE API can be deployed across cloud, desktop, mobile, wearable and IoT platforms to power a huge range of applications.

* The application of Array Logic to constraint problems was developed by Dr Gert L. Møller, and our patented RACE technology (US 6,633,863 / EU 1,062,603) applies the mathematical foundation of Array-Based Logic.

powering fully personalized edge analytics for smart home solutions
  • complex edge analytics delivering personalized decision support for smart IoT, digital health & dietary advice apps

  • predictably small RAM & CPU footprint on PCs, tablets, smartphones/watches & IoT/sensors

  • delivering complex decision support & personalized advice to users solely on their own devices

  • enabling cheaper, smaller & more secure IoT edge/wearable solutions with lower power & data needs

  • uses compiled knowledge models describing complex system behavior via an on-device runtime API

precisionlife IoT is a new IOT edge analytics platform that enables the delivery of fully contextualized responses and advice from the edge devices without having to transmit data back to a data center for processing.

precisionlife IoT enables building of smart home, digital health and wearable applications that can respond not just to generic signals and events, but in the personal context of what is best for the individual user.

Example Project:

In the ASSIST project, built using edge analytics from precisionlife IoT, we are detecting and providing fully contextualized responses to events based on combinations of signals from multiple sensors. These signals are analyzed using the RACE API (described below) running on a gateway device build around a Raspberry Pi Zero to provide appropriate responses in a variety of situations:

  • Automated fall detection and emergency intervention
  • Activity & social interaction
  • Fall prediction based on mobility features
  • Dementia progression via speech and activity features
  • Sleep, light, noise, pollution contextualization for wellness apps

An outline of the ASSIST project is shown below:

In the experimental ASSIST project, we are working with our partner Redgear Solutions to design a new type of smart home / assisted living solutions to support independent living amongst our elderly community.

precisionlife IoT brings together multi-dimensional data from a series of light, temperature, heat, movement and other sensors embedded in a standard light switch.

The RACE Engine and API

precisionlife IoT is build using our patented* RACE Array Logic based Constraint Engine technology. RACE powers our complex data analytics, decision support, smart IoT and embedded sensor/controller solutions.

RACE models complex systems’ behaviors in real-time, where the state of each component in a system may affect the behaviour of others. In these cases, such as personalization of advice in digital health, the problem cannot be simply split across multiple machines (e.g. with Hadoop) as a good solution to one section of the problem may be incompatible with other parts of the problem space.

RACE uses a new branch of maths called Array Logic to unravel the combinatorial complexity of relationships in the system to discover new associations and build high-value predictive models. We build comprehensive models of the problem space, and use the RACE Engine to apply constraints from the real-world to find only those potential solutions that meet all of our criteria. Rather than using machine learning or directed search methods to evaluate all the potential solutions to a problem, we can directly reduce a problem space which might have 101000 potential solutions down to just 1,000, using simple geometrical operators on the logical models. These 1,000 logically valid solutions can then be evaluated and priced for lowest cost/risk and highest benefit.

Critically we can do this in real-time, with a small & predictable memory and CPU footprint and in a provably complete manner (i.e. without taking short-cuts or eliminating any valid solutions) using the RACE API. The RACE API can be deployed across cloud, desktop, mobile, wearable and IoT platforms to power a huge range of applications.

* The application of Array Logic to constraint problems was developed by Dr Gert L. Møller, and our patented RACE technology (US 6,633,863 / EU 1,062,603) applies the mathematical foundation of Array-Based Logic.