---

./20170404-0144-cet-state-of-the-art-24-lifelogging-presentation-play-pause-rewind-1.pdf

• Above is the presentation.
• This presentation is about archived lifetimes.
• This the link to the presentation, https://www.slideshare.net/cgurrin/mayo2040.

• This presentation started with a provoking question.
• The question is "what if you never had to forget anything again?".
• Sounds to ideal to be true though.

• The ideal expectation is to archive someone life memories.
• And then the user is able to search on whatever had been stored in the database that is related to their own experiences.

• Cellphone is an example of omnipresent access.
• Cellphone is a general computing device.
• It packed with a lot of sensors as well as output methods.

• This graph could be useful in my presentation and paper later on.
• In summary.
  • ~1980 desktop computing.
  • ~1990 portable computing.
  • ~2000 mobile computing.
  • ~2010 wearable computing.
  • ~2020 implants computing.
• Pervasive computing happened between the portable computing and mobile computing.
• What is pervasive computing?
  • Pervasive computing is an emerging trend associated with embedding microprocessors in day-to-day objects.
  • This is more into ubiquitous computing.
  • This definition is taken from, https://www.techopedia.com/definition/667/pervasive-computing.

• Wearable computing is expected to be mainstream in 2015.
• However, I think it is not yet there.
• We need more streamlined hardware to application to pack very specific needs.
• Consumer would not buy anything physical for just very specific need.
• I think the ideal case would be to use smartphone.
• I think would be better for use to have an electronics development platform that uses resource from consumer existing computing power.
  • Laptop.
  • Smart phone.

• This project thinks for smart phone to be an omni directional sensors.

• Omnipresent sensors enables new era of service that understand individual.
• Omnipresent sensors means that sensor that can answer into so many questions.

• Example table of omnipresent sensors.

• An example of implementation would be in health care.

• As I have mentioned before, context need to be given or build.
• 1 example of context agent is machine learning.

• As whole, this "lifelogging" project aims to know the context of the user.

• Another example will be an augmented display.
• Like Google Glass but we need a device that is more powerful.

• The principle of archived lifetimes or lifelogging.
• Using mobile devices that automatically records everything.
• And then a system that sort things out. I suggest that this is a machine learning ecosystem.
• It says in this slide that there is some device to achieve something like this, that is already in the market.

• Lifelogging enables a surrogate memory.
• What is "surrogate"?
  • Based on the definition found here, http://www.dictionary.com/browse/surrogate.
  • Surrogate means a substitution on human role.
  • For example a woman carry out pregnancy of other woman.

• State of the art of lifelogging.
• There is Memex in 1950.
  • https://en.wikipedia.org/wiki/Memex.
• There is MyLifeBits in 2000.
  • https://www.microsoft.com/en-us/research/project/mylifebits/.

• In 2014, lifelogging can generate thousands of images per day.
• As well as other media as well like audio and tons of data from sensors.
• This means that we are dealing with big data.
• Even worse, a personal big data.

• Life enriching value.
• One person, one machine learning agent.
• Personal search engine for life experience.

• There are a lot of opportunity in many areas.
  • Health.
    • Personalized diet.
    • Personalized treatment.
  • Productivity.
    • Enhancing knowledge access.
    • Enhancing productivity.
    • Greater understanding of self.
    • Sociometric Badge.
  • Personal.
    • Never to forget anything.
  • Security.
    • Alibi.
    • Personal black box.
    • Self protection.
    • Your own security data.
  • Society.
    • Healthy population in general.
    • Machine learned specific population.
    • Sociometric Badge.

• The example of personal automatic diaries from lifelogging activities.

• Color of Life implementation.

• Personal health.
• Personal robot that understand its users.
  • In this example is coffee robotics.

• Better understanding on what makes one depressed.

• Machine learned on population.

• Augmented reality.
• Like Google Glass, but we definitely needs more processing power and use of Google Glass.

• Physical addressbook.
• Physical Facebook.