Quantified Self ,Personalized Medicine & Big Data

The Quantified Self is a movement to incorporate technology into data acquisition on aspects of a person’s daily life in terms of inputs (e.g. food consumed, quality of surrounding air), states (e.g. mood, arousal, blood oxygen levels), and performance (mental and physical). Such self-monitoring and self-sensing, which combines wearable sensors (EEG, ECG, video, etc.) and wearable computing, is also known as lifelogging. The primary methodology of self-quantification is data collection, followed by visualization, cross-referencing, and discovery of correlations. If we add genetic data to all the social and personal medical sensors data then the dream of data driven personalized medicine in healthcare is not too far.

Personalized medicine or PM is a medical model that proposes the customization of healthcare – with medical decisions, practices, and/or products being tailored to the individual patient. Traditional clinical diagnosis and management focuses on the individual patient’s clinical signs and symptoms, medical and family history, data from laboratory and imaging evaluation to diagnose and treat illnesses. This is often a reactive approach to treatment, i.e., treatment/medication starts after the signs and symptoms appear. A proactive approach using big data technologies and application of predictive analytics can help us take effective preventive measures.

Life and health tracking information from various applications can be difficult to integrate and find meaning. But innovative and readily available open source technologies have delivered efficient tools in the hands of data scientists. Now we can combine, visualize, and analyze the cacophony data to identify correlations between specific input factors and treatments (independent variables) and psychological or physiological outcomes (dependent variables).

Today, creative data scientists can arrive at amazing conclusions about how various factors correlate with one another and affect a person’s psychology or physiology. We can combine this insight and apply predictive algorithms to build models, tools to predict how a person would respond to a device, situation, treatment or medicine. Open source innovators behind path breaking  frameworks such as Hadoop, Hive, MapReduce, Yarn, KNIME, WEKA and Apache Mahout have built a set of components that can help us achieve the goals of personalized medicine.

Learning all these technologies, building the analytical tool from scratch and then running data analysis is a set of daunting tasks. The team at Big Data Cognition  is helping  data scientists face this challenge. We have integrated all these technologies together  to build a single platform, Saarus. Using Saarus,data scientist can focus on creative use of data without wasting precious time and effort on the technology plumbing.

Saarus, as is the case with all it’s component technologies, remains open source and is designed to make life easier for data scientists. It helps data scientists harness the capabilities of all these technologies and provide a cost-effective graphical analytic work bench. Big Data Cognition is committed to work on integration of all promising statistical algorithms and technologies to help healthcare data scientists achieve the goal of personalized medicine and preventive care.

Mobile Gaming – the titanic shift

The industry is undergoing a titanic shift, factors like the ease of publishing to the App Store, the extremely low cost to become a developer, the growth of Freemium games (games given away for free with revenues coming from alternative sources) and the rise of the female gamers (53% of mobile gamers are female) are rewriting the rules.

Anywhere, any-time, high quality gaming, that fits nicely in our pocket, is yours to have .  The mobile entertainment industry was worth $33 billion last year, which is due in part to the success of smartphones like the iPhone and the plethora of Android devices like the HTC Thunderbolt.  These high powered computers were just waiting for people to turn them into gaming consoles.

An interesting infographic by Geekaphone:

 

 

Game Heuristics – Part 2

Knowing your audience is half the battle won.But the other half was yet to be won. Now the focus was on defining the game specifications.Google search did turn up lot of material about general game heuristics.But there was one Powerpoint presentation from a research paper published by a Canadian university that really pulled everything together.I will soon track down that url and post it “here”.

In a nutshell here are the bullet points of each of the game design heuristics that we considered –

  • Accessibility – Making the game easy to approach, understand and play.
  • Interruptability – Taking advantage of asynch, spontaneous and irregular play sessions.
  • Continuity – Providing continuous game world which attracts the player to come back.
  • Discovery – Providing new experiences, content and surprises.
  • Virality – Supporting viral growth in the player’s social network.
  • Narrativity – Creating in-game and off-game narratives that elicit curiosity.
  • Expression – Supporting self-discovery, customization and virtual spaces
  • Sharing – Collaborating with friends by gifting and boosting
  • Sociability – Supporting sociability among friends in the game dynamics
  • Competition – Promoting playful social competition with others.

All our efforts were directed towards crafting game features in ways that are unique to the game.SEDUCTION is our maiden effort.We welcome all feedback from our players and will continue to improve .All bouquets and brickbats are most welcome.

Try out the game and let us know about your experiences.

Next.. team building..

Genesis of ‘SEDUCTION’ … a Facebook game

These blog posts will follow the timeline of SEDUCTION, our first Facebook game title, from concept to launch. In this long series I will share my views ,experiences & challenges about design of the game,characterization,stories,look and feel,heuristics,technology,team building,graphics,game construction, quality testing ,in-game player analytics, monetization ,final launch and marketing.

Work on SEDUCTION started in March of 2010. I had never played a Facebook game nor had any prior experience in social media space. I had spent previous six years as IT architect/Project manager/Business development manager for another start-up,a dual shore technology consulting firm that I founded in September 2004.All my previous experience was in managing technology projects for telecom,insurance & retail banking domains but had no clue about social media gaming.But I responded to Mickey’s call and we ended up discussing his idea of “SEDUCTION” sipping latte at Starbucks.Needless to say,I was floored by Mickey’s narration of his idea.Somehow it seemed strikingly similar to a rule based enterprise application that I worked on few years ago. By the end of the meeting, I was excited about this concept.It was a unique concept,different from anything I had ever attempted.

It became evident early on that the real value of our venture lies not just in one game, genre, title or a nice set of graphics but a reusable rule based gaming framework. We needed a framework that could enable us weave multiple virtual worlds of interesting never ending story lines, capture and retain player’s interest with a family of games of different genres. This framework had to designed to provide flexibility ,re-usability and follow all well proven design patterns.The end goal was to support the launch of a series of innovative games quickly and efficiently. SEDUCTION seemed to be the best candidate in that series of games from our gaming studio “CherryBelly”.

Next.. Game heuristics