Community Health Workers (CHWs) act as liaisons between health-care providers and patients in underserved or un-served areas. However, the lack of information sharing and training support impedes the effectiveness of CHWs and their ability to correctly diagnose patients. In this paper, we propose and describe a system for mobile and wearable computing devices called Rafiki which assists CHWs in decision making and facilitates collaboration among them. Rafiki can infer possible diseases and treatments by representing the diseases, their symptoms, and patient context in OWL ontologies and by reasoning over this model. The use of semantic representation of data makes it easier to share knowledge related to disease, symptom, diagnosis guidelines, and patient demography, between various personnel involved in health-care (e.g., CHWs, patients, health-care providers). We describe the Rafiki system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.
If you are a Google Glass user, you might have been greeted with concerned looks or raised eyebrows at public places. There has been a lot of chatter in the “interweb” regarding the loss of privacy that results from people taking your pictures with Glass without notice. Google Glass has simplified photography but as what happens with revolutionary technology people are worried about the potential misuse.
FaceBlock helps to protect the privacy of people around you by allowing them to specify whether or not to be included in your pictures. This new application developed by the joint collaboration between researchers from the Ebiquity Research Group at University of Maryland, Baltimore County and Distributed Information Systems (DIS) at University of Zaragoza (Spain), selectively obscures the face of the people in pictures taken by Google Glass.
Comfort at the cost of Privacy?
As the saying goes, “The best camera is the one that’s with you”. Google Glass suits this description as it is always available and can take a picture with a simple voice command (“Okay Glass, take a picture”). This allows users to capture spontaneous life moments effortlessly. On the flip side, this raises significant privacy concerns as pictures can taken without one’s consent. If one does not use this device responsibly, one risks being labelled a “Glasshole”. Quite recently, a Google Glass user was assaulted by the patrons who objected against her wearing the device inside the bar. The list of establishments which has banned Google Glass within their premises is growing day by day. The dos and donts for Glass users released by Google is a good first step but it doesn’t solve the problem of privacy violation.
Privacy-Aware pictures to the rescue
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts it into privacy-aware pictures. This output is generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by others (in this case ‘obscure my face in pictures from strangers’). The application would automatically generate a face identifier for this picture. The identifier is a mathematical representation of the image. To learn more about the working on FaceBlock, you should watch the following video.
Using Bluetooth, FaceBlock can automatically detect and share this policy with Glass users near by. After receiving this face identifier from a nearby user, the following post processing steps happen on Glass as shown in the images.
What promises does it hold?
FaceBlock is a proof of concept implementation of a system that can create privacy-aware pictures using smart devices. The pervasiveness of privacy-aware pictures could be a right step towards balancing privacy needs and comfort afforded by technology. Thus, we can get the best out of Wearable Technology without being oblivious about the privacy of those around you.
FaceBlock is part of the efforts of Ebiquity and SID in building systems for preserving user privacy on mobile devices. For more details, visit http://face-block.me
Memoto is a $279 lifelogging camera takes a geotagged photo every 30 seconds, holds 6K photos, and runs for several days without recharging. The company producing Memoto is a Swedish company intially funded via kickstarter and expects to start shipping the wearable camera in April 2013. The company will also offer “safe and secure infinite photo storage at a flat monthly fee, which will always be a lot more affordable than hard drives.”
The lifelogging idea has been around for many years but has yet to become propular. One reason is privacy concerns. DARPA’s IPTO office, for example, started a LifeLog program in 2004 which was almost immediately canceled after criticism from civil libertarians concerning the privacy implications of the system.
UMBC CSEE department members submitted a number of #ifihadglass posts hoping to get an invitation to pre-order a Google Glass device. Several came from the UMBC Ebiquity Lab including this one that builds on our work with context-aware mobile phones.
Reports are that as many as 8,000 of the submitted ideas will be invited to the first round of pre-orders. To get a rough idea of our odds, I tried using Google and Bing searches to estimate the number of submissions. A general search for pages with the #ifihadglass tag returned 249K hits on Google. Of these 21K were from twitter and less than 4K from Google+. I’m not sure which of the twitter and Google+ posts get indexed and how long it takes, but I do know that our entry above did not show up in the results. Bing reported 171K results for a search on the hash tag, but our post was not among them. I tried the native search services on both Twitter and Google+, but these are oriented toward delivering a stream of new results and neither gives an estimate of the total number of results. I suppose one could do this for Twitter using their custom search API, but even then I am not sure how accurately one could estimate the total number of matching tweets.
Can anyone suggest how to easily estimate the number of #ifihadglass posts on twitter and Google+?
UK semantic technology company True Knowledge has released Evi, a mobile app that competes with Siri.
The mobile app is available on the Android Market and on iTunes. You can pose queries to either by speaking or typing. The Android app uses Google’s ASR speech technology and the iTunes app uses Nuance.
True Knowledge has been developing a natural answering question answering system since 2007. You can query the True Knowledge online via a Web interface. Tty the following links for some examples:
Yesterday I made a purchase at the CVS store on Edmondson Avenue in Catonsville using Google Wallet on a Nexus S 4G phone with NFC.
NFC is near field communication, an RFID technology that allows communication and data exchange between two devices in close proximity, e.g., within a few inches.
Several current smartphones have NFC chips including the Samsung's Google-branded Nexus S 4G and more are expected to include it in the coming months and years.
The first, and perhaps most significant, use of NFC will be enabling mobile phones to serve as "virtual credit cards", especially for small amounts that don't require a signature. The range of potential applications is much greater and will no doubt evolve as mobile NFC-enabled devices become ubiquitous.
Buying something at the CVS (OK, … it was candy) this way was fun. My phone made satisfying noises as it talked to CVS's payment station and the clerk, who had not had anyone use a NFC device, was properly mystified. Using it was marginally easier than swiping a credit card, but maybe even a small amount of increased convenience is worth it for such an everyday transaction.
One limitation of Google Wallet is that it currently only works with Sprint on a Nexus S 4G and with either a Citi® MasterCard® card or a Google Prepaid Card. You can load money into the latter with most any credit card and Google will get you started by adding $10 to it as an incentive.
By the way, for what it’s worth, I only recently realized that the robots in Philip K. Dick’s novel “Do Androids Dream of Electric Sheep?” were called androids and the dangerously independent new model was the Nexus-6, developed by designed by the Tyrell Corporation.
Pervasive, context-aware computing technologies can significantly enhance and improve the coming generation of devices and applications for consumer electronics as well as devices for work places, schools and hospitals. Context-aware cognitive support requires activity and context information to be captured, reasoned with and shared across devices — efficiently, securely, adhering to privacy policies, and with multidevice interoperability.
The AAAI-11 conference will host a two-day workshop on Activity Context Representation: Techniques and Languages focused on techniques and systems to allow mobile devices model and recognize the activities and context of people and groups and then exploit those models to provide better services. The workshop will be held on August 7th and 8th in San Francisco as part of AAAI-11, the Twenty-Fifth Conference on Artificial Intelligence. Submission of research papers and position statements are due by 22 April 2011.
The workshop intends to lay the groundwork for techniques to represent context within activity models using a synthesis of HCI/CSCW and AI approaches to reduce demands on people, such as the cognitive load inherent in activity/context switching, and enhancing human and device performance. It will explore activity and context modeling issues of capture, representation, standardization and interoperability for creating context-aware and activity-based assistive cognition tools with topics including, but not limited to the following:
Activity modeling, representation, detection
Context representation within activities
Semantic activity reasoning, search
Security and privacy
Information integration from multiple sources, ontologies
There are three intended end results of the workshop: (1) Develop two-three key themes for research with specific opportunities for collaborative work. (2) Create a core research group forming an international academic and industrial consortium to significantly augment existing standards/drafts/proposals and create fresh initiatives to enable capture, transfer, and recall of activity context across multiple devices and platforms used by people individually and collectively. (3) Review and revise an initial draft of structure of an activity context exchange language (ACEL) including identification of use cases, domain-specific instantiations needed, and drafts of initial reasoning schemes and algorithms.
An issue on Reasoning with context in the Semantic Web seeks papers by June 15, 2011 and will be published in the Spring of 2012. The special issue will be edited by Alan Bundy and Jos Lehmann of the University of Edinburgh and Ivan Varzinczak of the Meraka Institute.
An issue on The Semantic Web in a Mobile World will accept submission until October 1, 2011 and will be published in September 2012. The special issue will be edited by Ansgar Scherp of the University of Koblenz-Landau and Anupam Joshi of the University of Maryland, Baltimore County.
The next iphone is rumored to have something similar.
Support for NFC in popular smart phones could unleash lots of interesting applications, many of which have already been explored in research prototypes in labs around the world. One interesting possibility is that this could be used to allow android devices to share RDF queries and data with other devices.
“Nokia plans to add antennas and RFID communications chips into its phones soon, and Apple has been patenting the heck out of the idea, but both companies were probably going to rely on an in-phone antenna loop. It seems increasingly certain Apple is going to bring RFID into common usage with the iPhone for 2011 (the iPhone 5) because there’s a new patent that shows just how far Apple has gone with design thinking for RFID. The patent shows how an RFID loop, powerful enough to act as both RFID tag or a tag-reader, can actually be built right into the complex layered circuitry of the iPhone (or iPod Touch) screen. We know Apple is fond of highly-polished design and integration, and this innovation is no exception. The screen has to be exposed by its very nature, which is good for RFID purposes — the wireless signal is unobstructed by other bulk in the smartphone, and it frees up Apple to do what it likes with the rest of the phone’s design.”
Maybe building RFID into smart phones will finally unleash the potential the technology offers for cool people oriented applications, as opposed to boring inventory management tasks. However, I don’t like the idea of not being able to use my credit card because my phone ran out of power.
“The results of a study conducted by researchers from Duke University, Penn State University, and Intel Labs have revealed that a significant number of popular Android applications transmit private user data to advertising networks without explicitly asking or informing the user. The researchers developed a piece of software called TaintDroid that uses dynamic taint analysis to detect and report when applications are sending potentially sensitive information to remote servers.
They used TaintDroid to test 30 popular free Android applications selected at random from the Android market and found that half were sending private information to advertising servers, including the user’s location and phone number. In some cases, they found that applications were relaying GPS coordinates to remote advertising network servers as frequently as every 30 seconds, even when not displaying advertisements. These findings raise concern about the extent to which mobile platforms can insulate users from unwanted invasions of privacy.”
TaintDroid is an experimental system that “analyses how private information is obtained and released by applications ‘downloaded’ to consumer phones”. A paper on the system will be presented at the 2010 USENIX Symposium on Operating Systems Design and Implementation later this month.
This is just one example of a rich and complex area full of trade-offs. We want our systems and devices to be smarter and to really understand us — our preferences, context, activities, interests, intentions, and pretty much everything short of our hopes and dreams. We then want them to use this knowledge to better serve us — selecting music, turing the ringer on and off, alerting us to relevant news, etc. Developing this technology is neither easy nor cheap and the developers have to profit from creating it. Extracting personal information that can be used or sold is one model — just as Google and others do to provide better ad placement on the Web.
Here’s a quote from the Ars Technical article that resonated with me.
We, and many others, are trying to prepare for the next step — when users can define their own privacy policies and these will be understood and enforced by their devices.
Abstract: The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.
The potential application is that a phone could recognize that it may have been stolen if it is being carried by a person with a different gait. I guess it would then phone home with it’s location, not unlike the golden harp in some version of Jack in the Beanstalk.
The accuracy would have to be improved to make this practical, of course, and it might not be a killer app, but it is a good example of how passive sensing by smart phones can acquire useful context information.