The development behind our Up Up algorithm were inspired by Micky Flanagan's comedy sketch about going "out out":
The challenge faced in a smart home is knowing if someone is properly up each morning, or as Micky would say Up Up. It is easy to detect when someone has got up in the night to get a drink from the kitchen or go to the toilet but, we don't want the house to decide that people are now Up Up and start doing things like running scenes, making voice announcements, opening curtains, switching off the alarm, unlocking doors, etc.
There are many things that we only want to occur in our smart home when it has determined that the household is Up Up. This is extremely valuable information in the contextual smart home and a significant contribution to the great user experience.
We have spent a lot of time researching and refining our smart home's Up Up algorithm based on detected occupancy and presence, people counting, time of day, event counting and other methods so that we have an accurate view of whether someone in our home has got up or whether someone in our home is Up Up.
The algorithm only works because it's part of our contextual smart home, which tracks occupancy and presence for each room in our home and for the whole house. The algorithm is particularly applicable to assisted living and telecare.
Our algorithm looks for important events that are indicative of the household being awake and assigns them different weightings, based on their significance. It then maintains a view of recent activity along with its significance and once this crosses a predetermined 'threshold', it will assume we are Up Up.
Our smart home then runs an Up Up scene when it decides the time is right.
A key input into this algorithm is provided by bed occupancy sensors connected to our smart home. We have developed our own sensor which provides a very accurate and timely view as to whether a bed is occupied or not. Sensors like these are key to getting quality occupancy and presence data in rooms where people sit or lie still for long periods of time.
We are also monitoring appliance usage in our smart home (e.g. kettle, dishwasher, coffee maker, tumble dryer, etc.). These are also an important input into our algorithm.
As well as smart home use, this algorithm has applications in telecare, hospitality and rented accommodation. Remote carers can get notifications to let them know that an elderly or vulnerable person is up or has failed to get up by a certain time.
Occasionally, our @smartest_home will tweet when it has decided the house is Up Up.
In this example tweet from our @smartest_home, energy monitoring data is used as part of our wider contextual smart home to provide occupancy and presence information, identify the user of the coffee machine and to then enable a personalised user experience in the kitchen. The use of the coffee machine is a clear indication of the house being Up Up.
