ORA's HALO is a 3-dimensional digital object who's colour, form and behavioural characteristics are all determined by the data being streamed into the system. It is a digitally-organic representation of data-self – kind of like a Tamagotchi – but one driven by your personal data.

Our first product is for the Apple Watch where in partnership with the Mayo Clinic we are building a digital platform that allows for health to be pictured. Instead of going through the mental gymnastics of combining exercise type and calories burned to know if you're living optimally, you instead look at how your Health HALO is performing. Combinations of metrics, allows us to engineer health phenotypes – which are essentially characterisations of people depending on their lifestyles – athlete versus couch potato. Unique combinations of values result in unique looking HALOs, allowing the user to very quickly understand who they are based on how they live.

The Mayo Health HALO is composed of four primary axes :
1. SIZE (number of rings) = activity as steps
2. COLOUR (cold blues to warm yellows) = peak exercise heart rate and duration
3. COMPLEXITY (amplitude of sine-waves of rings) = moment to moment heart rate
4. MOVEMENT (symmetry of the generative animation) = stand indicator

The more active you are in a day, the larger and faster your HALO grows. As you exercise and elevate your heart rate, your HALO transforms from cool to warm colours and approaches yellow if your HR has reached the Mayo prescribed threshold for your age. A smooth and slow moving HALO indicates your heart rate at that moment is relatively low, while a spiky and fast moving HALO indicates a high heart rate. If you've exercised the minimum recommend 30mins per day, your HALO fills up to a solid bright colour. Your HALO is symmetrical if you move regularly throughout the day, and decays into a wobble motion if you are sedentary for long periods at a time.

HALO is a new approach to data visualisation that is object-oriented and whose primary function is aggregation and synthesis – bringing vast amounts of data into a single object that allows for correlations and higher-order relationships to be understood.

ORA's HALO is a 3-dimensional digital object who's colour, form and behavioural characteristics (how it transforms in time and space) are all determined by the data being streamed into the system.

HALO is a digitally-organic representation of data-self – kind of like a Tamagotchi – but one driven passively by your personal data. The approach we took in designing the system was not only to focus on hardcore data visualisation, but also to impart a "living" quality to the system. The computational and generative behaviour of the HALO is designed such as to look and feel alive – with the intention being to engineer a user experience where individuals feel invested in their own data. Very few people feel emotionally invested in bar graphs, which may explain why there is such a high rate of rejection of wearables after a couple months of use. The living quality of HALO and the physically proximate nature of the visualisation (it lives on your wrist), we believe creates an intimate connection between biological and data-self which in turn builds an empathic bond with the system.

Our first product is for the Apple Watch where in partnership with the Mayo Clinic we are building a digital platform that allows for health to be pictured. Instead of counting steps, you grow your Health HALO by increasing physical activity. Instead of going through the mental gymnastics of combining exercise type and calories burned to know if you're living optimally, you instead look at your Health HALO and see how brightly it glows. Combinations of metrics, allows us to engineer health phenotypes – which are essentially characterisations of people depending on their lifestyles – e.g. athlete versus couch potato. Unique combinations of values result in unique looking HALOs, allowing the user to very quickly understand who they are based on how they live.

The Mayo Health HALO is composed of four primary axes :
1. SIZE (number of rings) = activity in steps
2. COLOUR (cold blues to warm yellows) = peak exercise heart rate and duration
3. COMPLEXITY (amplitude of sine-waves of rings) = moment to moment heart rate
4. MOVEMENT (symmetry of the generative animation) = stand indicator

The more active you are in a day, the larger and faster your HALO grows. As you exercise and elevate your heart rate, your HALO transforms from cool to warm colours and approaches yellow if your HR has reached the Mayo prescribed threshold for your age. A smooth and slow moving HALO indicates your heart rate at that moment is relatively low, while a spiky and fast moving HALO indicates a high heart rate. If you've exercised the minimum recommend 30mins per day, your HALO fills up to a solid bright colour. Your HALO is symmetrical if you move regularly throughout the day, and decays into a wobble motion if you are sedentary for long periods at a time.

HALO is a new approach to data visualisation that is object-oriented and whose primary function is aggregation and synthesis – bringing vast amounts of data into a single object that allows for correlations and higher-order relationships to be understood.

HALO allows for meta-understanding of vast amounts of data by essentially allowing you to see the forest from the trees. The system absconds the typical pedantic focus-on-detail approach to data visualisation, and instead represents data relationships using symbolic and object-oriented relationships. HALO removes the abstraction in data visualisation and instead shortens the conceptual and cognitive distance between the data and its mode of representation – it looks like the data it is representing. This not only allows for ease of reading and comprehension, but also imparts an emotive quality to the digital object that makes the user much more invested in their personal data.

HALO is an intelligent digital object for a post-data world – one where the evolution of the modes of interaction with data and data signalling devices follows naturally after the widespread acceptance and widespread understanding of data language in society.