Wearable Data Hack 2015 – Official Announcement

Wearable Data Hack 2015 – Official Announcement

Munich, 19 June 2015 – On the weekend of June, 19-21, Stylight, Datarella and Macromedia University proudly present the first hack day on wearable tech applications, data and design – the Wearable Data Hack 2015. the Wearable Data Hack 2015 will be the first occasion for most of the participants to share their views and ideas and jointly gather experience with the new data realm.

Apple calls the Apple Watch “Our most personal device ever”. And with good cause: The data from wearable tech, smartphones and smartwatches are really the most personal data ever. Our mobile devices accompany every step we take, every move we make. A plentitude of sensors on the devices draw multidimensional pictures of our daily lives. Applications of wearable data range from fitness to retail, from automotive to health. There is hardly an industry that cannot make direct use of it. And yet, wearable apps are still in their childhood.

Telling the stories of people’s lives

Wearable data is not an end in itself. Data is the raw material of our behavior. After data has been collected it has to be analyzed. There are algorithms which define the semi-finished results – these have to be enriched with other, contextual data. Analyzed and enriched data result in stories describing our lives.

Data & Design

For decades, designers have been seeking to design products to meet the seemingly never-ending rise of consumption. Since then, we appear to have evolved from an industrial economy to a knowledge- and data-driven economy. New models of thinking and new patterns of behavior and social values appear. Design thinking seems to provide some answers – it focuses on a human-centered approach, which combines design activities with research on human needs, and technological and business aspects, in order to create knowledge and solutions for highly complex problems.

The tracks

Hackers, designers and thinkers can choose from these tracks:

– Data-driven business models for wearables
– Data-driven wearables
– Smartphone app (Stand alone / comb with smartphone)
– User Experience
– Design
– API
– Open Data / Shared Data
– Medical data / mHealth

We invite hackers, designers and thinkers to meet each other, to discuss and find ways of using data in order to tell the people’s lives and create examples of socially relevant technology.

The date

Friday, 19 June – Sunday, 21 June

The location

Stylight, Nymphenburger Str. 86, München.

Stylight’s lofty office is well established as one of Munich’s coolest venues for Hackathons and perfectly suited to accommodate up to 100 participants.

The organizers

STYLIGHT is the first ‘shoppable’ fashion magazine in Germany. The STYLIGHT editorial team creates the best inspirational content every day, satisfying all the lifestyle news their users crave. No matter whether it’s fashion, beauty or stars – STYLIGHT is the number one website for inspirational excellence linked with innovative online shopping technology. With STYLIGHT you will never miss out on a trend again and can shop featured products instantly from all your favorite online stores.

Datarella is the mobile data technology company. The Munich-based startup taps into smartphones and wearable devices to harvest the data. Datarella supports data-driven analytics and product development for connected cars, smart home, mHealth, and retail.

Macromedia University follows these developments in its capacity as a university, and conceives itself as a place for reflecting on all aspects of media society. Our courses of study cover the broad spectrum of modern media careers, from management, and content jobs such as journalism, to the creative fields of design, gaming, and film and television. With over 80 professorships and over 2000 students in all five media centers of Munich, Stuttgart, Cologne, Hamburg, Berlin and Milan, the University trains the next generation of media talent for an international media society.

Telling the story of people’s lives – Strata+Hadoop, Feb 15, San Jose

We can draw a colorful picture of people’s everyday lives from the data we collect via smartphones. To tell the data-story, we need to translate the raw measurements into meaningful events, like “driving a car”, “strolling in a mall”, or even more intimate, like “being nervous”. We will show how to access the phone’s data, how to derive complex events from the phone’s raw data, and how to bring it into a meaningful story, and how to make it work for businesses.

Cases we’ll show: an app for the automotive industry to support ecological driving, learning about preferences of Chinese passengers at an international airport, and supporting people suffering from osteoporosis to stabelize their condition and maintain mobility.

More on Strata+Hadoop

 

Wearable Data Hack Munich 2015

Today, we would like to announce something special. Something we can’t wait to take place and until mid June it’s going to be tough to sit tight. Please, feel invited to our Wearable Data Hack Munich 2015!

The Wearable Data Hack Munich 2015 is the first hack day on wearable tech applications and data. It will take place right after the launch of Apple Watch – the gadget we expect to rise the tide for all wearables. Withe the Wearable Data Hack Munich 2105, we aim to kick-off app development for the emerging smartwatch and wearable tech market. During this weekend you will have the first occasion to share your views and ideas and jointly gather experience with the new data realm.

Apple calls the Apple Watch “Our most personal device ever”. And with good cause: The data from wearable tech, smartphones and smartwatches are really the most personal data ever. Our mobile devices accompany every step we take, every move we make. A plentitude of sensors on the devices draw a multidimensional picture of our daily lives. Applications of wearable data range from fitness to retail, from automotive to health. There is hardly an industry that cannot make direct use of it. And yet, wearable apps still are in their childhood. The Apple Watch will be hitting the street in April and will get the ball rolling.

The Wearable Data Hack Munich 2015 is jointly organized by Stylight and Datarella.

THREADS TO BE PURSUED
Developers, data geeks and artists will pursue one or more of these threads:
– Data-driven business models for wearables
– Data-driven wearables
– Smartphone app (Stand alone / combined with smartphone)
– User Experience
– API
– Open Data
– mHealth / Medical Data

So let’s explore what we can do with this data! Let’s play with the possibilities of our wearable gadgets and mobile sensors.

APPLICATION
To apply for the Wearable Data Hack Munich 2015, please send us an email with
– your name
– your profession
– your take on wearable data
– 3 tags describing yourself best.
Don’t wait for too long – the number of participants is limited.

For more information, please have a look here! See you at Wearable Data Hack Munich 2015!

The Datarella World Map Of Behavior

Every smartphone user produces more than 20 MB of data collected by her phone’s sensors per day. Now, imagine the sensor data of 2 billion smartphone users worldwide, translated into realtime human behavior, shown on a global map. That is the vision of the Datarella World Map of Behavior.

A typical 2015 generation smartphone sports up to 25 sensors, measuring activities as diverse as movements, noise, light, or magnetic flux. Most smartphone users aren’t even aware of the fact that their phone’s camera or microphone never are really „off“ but that they constantly collect data about the noise level or the intensity of light the user is experiencing.

Actions speak louder than words
Actions speak louder than words – if we want to really know a person we have to know how she behaves, and not only what she says. And that’s not only true for politicians. We all form our opinions on others by looking at their actions, more than their words. Many inter-personal problems result from NOT looking at people’s actions, but focusing on other aspects, such as their looks or their words. Behind superficial distinctions such as physical appearances, over time we often realize similarities with other people based on their and our actions.

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Our vision of a World Map of Behavior
At Datarella, our vision is to show the actions of people around the world on a global map. By displaying the actions of people of all continents, we want to tell stories about the differences and similarities of global human behavior – to draw a picture of human co-existence. There already are snapshots of global behavior, provided by data focused companies, such as Jawbone, who map sleep patterns worldwide. From that we know that Russians get up latest, and Japanese get the least sleep in total . And there are different behavior-related maps, showing the world’s most dangerous places, defined by the number and seriousness of crimes or actions of war.

Co-operation & Participation
To create the World Map of Behavior is our ambitious project for 2015 that we won’t complete alone. We need your support: if you are an expert in the field of mobile sensor data or if your company already focuses on collecting and interpreting mobile sensor data in the fields of mobility, finance, health or transport and travel. If you are interested to play a role in this project, please send us an email with a brief description of how you would like to contribute. We are looking forward to hearing from you!

The Datarella Data Timeline

If you live in the Americas, Europe, Asia-Pacific or Australia, you probably are one of 1.76 billion smartphone users. What do you do with your smartphone? You use apps for texting, social networking, gaming, etc. And you produce data. Lots of data. Every move you make, every breath you take, your behavior results in data: location, movement, body functions, external situational data. And you produce all this data on the fly, passively, without any effort.

But where is this data? Can you see it? Do you know exactly which data you produce? At which times or to what extent? Do you know that your data can be of tremendous value for you? That you can improve your health by using it? Can you imagine knowing all about your data, that you see your data, in realtime, presented in a meaningful way that lets you understand its value?

Welcome to the Datarella Data Timeline 

The Data Timeline  is an app-centric timeline of your own behavior data and that of your social network. Using the Data Timeline is like looking into a mirror: you see yourself – not your external appearance but the visualization of your body data, you see the most complete picture of your Self. he Data Timeline presents your data in a contextualized way: you see the data and its meaning in individual contexts – your personal data moments.

Reason Why

Each day, an individual produces about 20MB of smartphone data. This data can be of vital relevance for her: it can save her life. Or it can help her optimize her life. Or the life of her friends. In order to make meaning of this data, the user needs her own individual data timeline, she needs to see her data, in its contexts and visualized in a comprehensible way.

You and your friends

And it’s not only your Self – but you see real time snapshots of your friends, too! You know about their well-being, their movements or the noise levels they are exposed to. So you already know that your significant other is quite exhausted before she returns from work and you could prepare a candlelight dinner for her. You can share snapshots of your Self with your friends right away from the Data Timeline using the integrated Twitter, Facebook et al. sharing options. You decide with whom you want to share your data.

Own your body’s data

You should own your body’s data – because your data can reveal much more than even your doctors know. Your data is a very important part of you. You can use the Data Timeline to get the meaning of your data and – if you are a geek – you might even download all your raw data, dive into it and analyze it with your own tools. You have full ownership, full transparency and full visibility of your data.

The Data Timeline essentially is a platform matching users with Quantified Self services. It comes as in the user-centric format of an app timeline displaying the user’s individual behavior data and that of her social network. It shows an individual’s body and environmental data in its specific contexts. Passively tracked data are complemented with interaction data, such as status updates, comments and answered surveys.

The Data Timeline provides the user with her data itself and – via its API – the Data Timeline matches the user’s individual needs based on her data with respective Quantified Self services in the areas of health, fitness & well-being, travel & tourism as well as finance.

The Data Timeline’s technology is based on open source software, but all algorithms, especially for the Complex Event Processing Engine, are fully owned by Datarella. All raw personal data of individuals is owned by its respective users, according to German law. However, all aggregated data, all visualizations, and all analysis based on that data is owned by Datarella.

 

Sharing Goods And Sharing Data: Both Is Fun, Big Business And A Social Responsibility

Around 2010, Lisa Gansky coined the term Sharing Economy, or Mesh companies, offering their customers efficient shared access to their products instead of selling their products to them. Recently, it’s being called Collaborative Consumption or Collaborative Economy. It’s all about finding ways to make better use of valuable resources that have remained unused. Convenient access is being made affordable to people who can’t afford different products, or simply don’t need to own those products since they would only use them infrequently.

Typical mesh businesses like AirBnB, LendingClub or Cookening, demonstrate the power of sharing in very different ways: AirBnB is on the way to pass Hilton as the world’s largest hotelier in 2015, that is 7 years after its inception. The US peer-to-peer lending company Lending Club has originated over 4 billion USD in loans – it was originally founded as a Facebook app in 2006. The typical Mesh business runs a stylish app with a high usability. It’s service is new, easy to use and affordable. But all that does not fully explain the tremendous speed they conquer one market after the other. Who is the driver behind the Sharing Economy and it’s success?

It’s the user.
It’s the user. The user offers and asks for private overnight stays on AirBnB, the user provides and lends money on LendingClub. Even with services like Zipcar, when the product is provided by a company, the user „uses“ a product instead of buying and owning it. He has to rely on other users‘ good maintenance of Zipcars, since if there were too many ‚abusers‘ the company had to raise rates and the product would become unaffordable for most people. The same is true for AirBnB and others: users have to be sure that landlords don’t sell cubbyholes to them, whereas – vice versa – landlords have to trust their guests not to steal the TV or destroy the flat. So, the user has to use the service and she has to behave in an orderly manner – this is the foundation for a properly working and successful Sharing Economy.

The Sharing Economy

Image: The Sharing Economy, Latitude

Now let’s adopt the principles of the Sharing Economy to the individual who shares her statuses with her social graph on Facebook, discusses the latest news on Twitter and shares her preferred fashion designs on Pinterest. She dos it because she wants to express herself and she wants to communicate with a wider circle of friends than she can meet in person. She communicates in both, synchronous and asynchronous ways. She has learnt that the more she adds to discussions, the more she gets in return. In Social Media, she experiences the Pay-it-Forward principle in action at its best.

Sharing Economy has attained full age in 2014
Let’s assume we can all agree on that: communication openly and actively, sharing ideas, opinions, homes, cars, money and much more with others is not an extravagant imagination of Utopia, Inc., but a multi-billion dollar business eclipsing traditional business models around the world. Furthermore, it’s not just a gigantic business but a sympathetic and friendly way of matching supply and demand of individuals. Who wouldn’t prefer an individually furnished private home over a standard hotel room?

If we agree on the power of sharing the above mentioned martial and immaterial goods, can we also agree on the power of sharing data? Our data? Our own body’s data? Can we agree on the tremendously positive and socially relevant effects of sharing the data we produce ourselves, day by day? If you own a smartphone (you most probably will), you produce about 20 MB of smartphone data (i.e. data racked with your smartphone’s sensors) each day. Perhaps you haven’t been aware of that fact, or you just didn’t know how relevant this data could be for yourself, and for your social graph, respectively. Do you know how much you move each day? The U.S. Surgeon General wants you to move at least 10,000 steps a day to prevent and decrease overweight and obesity. (It’s very easy to know your steps: just get yourself one of those fitness trackers.)

And, did you know that your Vitamin D level is one of the key drivers of your well-being? Most North Americans, North and Central Europeans suffer from a Vitamin D deficiency. Do you actually know your Vitamin D level? Do you know that you can find it out yourself?

Let’s get more complex regarding data: microbes in the human body are responsible for how we digest food and synthesize vitamins, our overall health and metabolic disorders. The aggregate of microbes is called microbiome. Do you have any idea about your individual microbiome? Do you know that you can find out about your microbiome yourself, by using a simple kit?

Sharing Data still in its infancy, but…
So far, we have talked about the relevance and value of our data for ourselves. But – weren’t you interested in preventing your first stroke because thousands of other men at your age have provided their heart and respiratory rates anonymously and based on the analysis of this data you had been warned early enough to take appropriate action?

Wouldn’t you agree that the Pay-it-Forward principle works perfectly in the field of personal body data? The difference to the AirBnB model is that you provide your body data anonymously. It will be aggregated and used in a way that nobody knows that’s you behind your data. Since data analysis and respective actions or recommendations rely on big data, it’s necessary that many people participate and share their own data.

… will become a Social Responsibility in 2017
Today, in late summer of 2014, many people are sceptical and hesitate to provide their data. We think that personal body data sharing will be regarded as quite normal within a few years. If this movement takes up the same speed as the Sharing (Goods) Economy, it will be accepted as „normal“ within 2-3 years. We believe, that data sharing will become a social responsibility, comparable to fasten one’s seat belt or wearing a bike helmet. It probably won’t be called data sharing since this is a B2B term. There already is a very good term  – the Quantified Self, or QS. The term itself does not include the sharing element. But for every active members of the QS movement sharing is a relevant part of the quantification process because the value of an individual’s data is even bigger if used for general purposes.

We regard data sharing – our Quantified Self – as one of the most important movements of modern times and we would love to know how you think about it: please comment, provide us with your feedback: do you already share your data? How do you do it? Or, are you still sceptical?

Feature Image: Max Gotzler of Biotrakr, presenting findings of a Testosterone study at #QSEU14

Global Sleep Patterns

Sleep is one of the most interesting aspects of life: during sleep we don’t act consciously (apart from a few natural processes inside our body) and therefore some people try to minimize sleep to get most out of their lives. Others maximize their sleep: for them sleep simply is the greatest activity they could think of. The Quantified Self folks try to optimize their sleep; i.e. to maximize their sound sleep phases and minimize light sleep and times of being awake.

The guys from Jawbone looked at their UP band user’s sleep data and could provide us with this interesting global sleep pattern. Since Jawbone’s data are more detailed and accurate than the American Time Use Survey, this view on the different sleep patterns provides great insights in how inhabitants of cities behave, or how active a city is, seen as a whole. The average hours of sleep shown in the feature visual above do not include time awake in bed.

Science tells us that we should sleep between 7 to 8 hours per night and we should sleep during the same cycles in order to maximize recovery and relaxation from our daily routines. Now look at people living in Tokyo: with 5h 44min they sleep least, whereas Melburnians (yes, without the „o“) sleep most with 6h 58min – which is just the bottom end of the recommended length. Again Australians, this time the folks in Brisbane, go to bead earliest, at 10.57pm. The night-owls in Moscow hit the pillow almost 2 hours later, at 12:46am. But Muscovites (rise latest at 8:08am) sleep only 29 min less than the Brisbanian who raise at 6:29am.

sleep cycle

Image: The author’s sleep pattern on August, 19 – provided by the Jawbone UP app

How about you? Do you know how long you sleep? Do you know about the quality of your sleep?

Above, you see a snapshot of my own sleep: On August, 19, I had 5h 22min of sound sleep and 1h 52min of light sleep – which is a pretty good ratio. I reached my goal of 7 hours of sleep as well, which is partially due to the fact that we are in the middle of school vacation and there is no need to mange kids in the mornings. So, don’t worry if your sleep looks less sound – I wake 1 time every 3-4 nights.

The most relevant criteria defining my personal sleep are
– duration
– cycle; i.e do I go to bed and rise at roughly the same times?
– alcohol input
– time without staring at a display before going to bed
– general family mood during the evening

I’d love to know about your experiences with your sleep. Do you track? What makes you sleep sound or light, short or long?

A Bit of Data Science – What Your Battery Status Tells About You

Working with lots of data, the biggest challenge is not to store or handle this data – these jobs are far from being trivial, but there are solutions for nearly any kind of problem in this space. The real work with data starts when you ask yourself: what’s behind the data? How could you interpret this data? What story can you tell with this data? That’s what we do and we want to share some of our findings with you and motivate you to join our discussion about the meaning of the data . We want to create Data Fiction.

Today, we start with some sensor data collected by our explore app – the smartphone’s battery status including the loading process. Below you see sample data for our user’s behavior during the week (Feature Visual) and at the weekend (Figure 1).

Smartphone Battery Weekend
Figure 2: Smartphone Battery Status (weekend) (Datarella)

In Figure 1 you see that most users load their smartphones around 7 a.m. and (again) around 5 p.m. What does that tell us? First, we know when most users wake up in the morning – around 7 a.m.. Most probably they have used their smartphones‘ alarm functions and then connect their devices to the power supply. Late afternoon, they load their devices a second time – probably at their office desks – before they leave their workplaces. During weekends, the loading behavior is different: people get up later, and maybe use their devices for reading, social networking or gaming, before they reconnect them to their power supplies.

Late rising leads to an avarega minimum battery status of 60% during weekends, whereas during the week, users let their smartphones batteries go down to 50%. This 10% difference is interesting, but the real surprise is the absolute minimum battery status of 50% or 60%, respectively. It seems that the days of „zero battery“ and hazardous action to get your device „refilled“ are completely over.

For some, data is art. And often, it’s possible to create data visualizations resembling modern art. What do you think of this piece?

matrix
Figure 2: Battery Loading Matrix (Datarella)

This matrix shows the daily smartphone loading behavior of explore users per time of day. Each color value represents a battery status (red = empty, green = full). So, you either can print it and use it as data art on your office’s wall or you think about the different loading types: some people seem to „live on the edge“, others do everything (i.e. load) to staying on the safe side of smartphone battery status.

What are your thoughts on this? When and how often do you load your mobile device? Would you describe your loading behavior as „loading on the edge“ or „safe? We would love to read your thoughts! Come on – let’s create Data Fiction!

The design of the explore app – The Datarella Interview

Today, we speak with Kira Nezu (KN), Co-founder of Datarella, about the design of the explore app.

Q
The explore app is available for Android smartphones only. What is the reason not to launch an iPhone version, too?

KN
We started to develop explore as a so-called MVP, a Minimum Viable Product. We chose Android to start with since it offers more variety regarding sensor and phone data. So we only test and make mistakes on one platform. At some point, we will also launch an iPhone version.

Q
explore consists of two different elements: the sensor tracking and the interaction area with surveys, tasks and recommendations. Could you tell us more about the structure and the functionalities of the app?

KN
With the MVP we are trying to stay as flexible as possible to enable fast changes and bug fixing. So we decided to create a hybrid app which incorporates native and web elements. The native part basically is the container with most of the graphics. The content is dynamically fetched from our backend, whereas the result area is fully created with web views. This brings great flexibility: we can update our content within minutes.

Regarding the structure there are 3 areas:
– main content area – divided into the survey area and recommendations,
– menu area,
– result area.

Q
Regarding surveys: there are already mobile survey apps on the market. How does the explore app differ from those?

KN
Before designing the app, we did a lot of research on existing survey apps. We found that either the apps had a very technical design that reminded us of Windows 95. Other apps were very playful but done simply, i.e. one app would show two images – and the user could tap one of those to make a choice.

We want the user to have a playful experience while keeping the flexibility of different interaction formats.

Q
You call explore a Quantified Self app. Can you elaborate on that?

KN
The Quantified Self aspect of explore relies on regular interactions wich ask the same information from the user. In the result area we show the user her personal mood chart with her own results compared to other explore users. Currently we are working on a location heat map in which the user can see her personal location history of the past days – and also that of other users. We had some surprise moments in internal tests: it took quite a while to recall why we were at certain locations. You could compare that with cooking water for tea 5 times before finally remembering to brew your tea.

Q
So what are the next steps for explore?

KN
We will focus on adding more Quantified Self elements as results as well as offer an API for users to play with their own data. We are really looking forward to see what our users will come up with! If you are interested with playing with your data now, you are welcome to participate in our Call for Data Fiction.

Q
Thank you very much.