Chandra’s Blog

Dec. 23, 2008

Got the Arudino Ethernet board.  Attached it to Arduino Diecimila board.

Set the ip[]={ 192.168.1.77 };

Used the Arduino-12 software and ran the Sketchbook-Examples-Library-Ethernet-WebServer.

This responds to the web request by displaying the following:

analog input 0 is 301
analog input 1 is 316
analog input 2 is 312
analog input 3 is 299
analog input 4 is 311
analog input 5 is 321

Reloading the page, shows the new values of the ADC values.

Reading up on more stuff on Sensors, detection and context

“Examining Task Engagement in Sensor-Based Statistical Models of Human Interruptibility” – James Fogarty, et. al.

http://www.cs.cmu.edu/afs/cs/Web/People/jfogarty/publications/chi2005.pdf

This is a paper on interrupting at inappropriate times a office worker.  They discuss a way to automatically extract features from low level events (in a restricted work environment)

Related older paper (2003)

“Predicting Human Interruptibility with Sensors: A Wizard of Oz Feasibility Study”

http://www.cs.cmu.edu/afs/cs/Web/People/jfogarty/publications/chi2003.pdf

Sensor based prediction of interruptibility.

List of some interesting products that are in the market

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Omron Satety Mat/Safety Mat Controller

• Pressure sensing method
• 30Kg min – 30msec response time
• Pads 300×300 mm to 1200×1800 mm

http://www.ia.omron.com/data_pdf/data_sheet/um_mc3_f087-e1-01.pdf

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OMRON – Safety Laser Scanner

• Operator Presence Detection in Work Areas with Complex Shapes

http://www.ia.omron.com/data_pdf/data_sheet/os3101_dsheet_csm1262.pdf

Smart Sensors with Ultra-High-Speed CCD cameras

In this blog, I will annotate the papers and presentations I have been reading on this subject.

Papers

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Don’t Sweat Your Privacy – Using Humidity to Detect Human Presence – July 2007, Carnegie Mellon University

http://sparrow.ece.cmu.edu/group/pub/han_jain_luk_perrig_privacy.pdf

– This paper talks about disclosing private information such as human presence by pervasive sensors that detect humidity, temperature and light.  It is an experimental system that uses TinyOS motes and has performance information on detection rates, based on humidity measurements.  They are able to infer what the subject is engaged in, such as talking, drinking hot beverages and breathing patterns by measuring humidity.

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Human Presence Detection By Smart Devices – UCE Group – Technical University of Endhoven

http://www.cvc.uab.es/~bogdan/Publications/raducanu_eis2004.pdf

In this paper the use of computer vision based approach to detect the distance and orientation of target is discussed.  It uses low cost camera and infrared technology.  They use 3D perspective geometry computational techniques to solve the dynamic environment with continuously changing illumination conditions.  One potential application of this could be in smart displays that can make use of the presence detection.  They briefly touch upon the use of ultrasound and use of badges to create an ‘aware environment’.

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Presence detection and person identification in Smart Homes – Univeristy of Bundeswehr Munich and University Passau

http://smarthome.unibw-muenchen.de/upload/publications/Iva02.pdf

In this short paper use of multiple sensors in a Smart Home to detect and identify person is discussed.  A nice comparison of various detection sensors are presented.  Person identification is done via ID badges. They have built a system using image recognition, vibration shock sensors (via the vibro-acoustical waves propagating through the floor surface) and infrared cameras.

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Presence versus Availability: The Design and Evaluation of a Context-Aware Communication Client – Carnegie Mellon University

http://www.cs.washington.edu/homes/jfogarty/publications/ijhcs2004.pdf

In this paper, MyVine, a client that integrate phone, instant messaging, and e-mail with automatically sensed information about the context and availability of the colleagues is discussed.  It employees speech detection (built-in microphone on the laptop), location information, computer activity, and calendar information to model a person’s availability.

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Sensor Evaluation for Human Presence Detection – University of Minnesota

http://www.cdc.gov/nasd/docs/d001501-d001600/d001503/d001503.pdf

In this project they are trying to setup a system to detect the presence of a person near a dangerous machine environment.  This work is sort of outdated (1998) and not very relevant to the Presence detection and more importantly identification.

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Human presence detection using millimeter-wave radiometry – Univ of Texas at Austin – Doctoral Thesis 2008

https://repositories.lib.utexas.edu/bitstream/handle/2152/3961/nanzerj49279.pdf?sequence=2

note: this is a large file

This thesis report has state of the art information; while it is specific to detect a stationary person in an outdoor environment from a moving platform, it does explore some interesting possibilities for presence detection.   The fact that all objects (that have some physical temperature) radiates certain frequencies is exploited.

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Slide Presentations

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Application of Control Concepts to the Smart Home of the Future

http://personal.tcu.edu/~lburnell/crescent/NSFEngineeringResearch2004.ppt

In this set of slides from TCU application of NSF grant, a good collection of sensors for Smart Home of the Future is discussed.  A simple control process is suggested for sensor information leading to identification.

In this blog, collection of information, application of pervasive detection – of people, things, and services.

Detection has been the key to context.  Context helps to create situation, applications, conversations and experience.

Detection, if make pervasive, will enable lot more interesting experiences.  Human beings are very good in adjusting to detection of things around them.  Once things (including people, surroundings,  and services) are detected and possibly identified, without much (or no) effort, could lead to compelling services and applications.

In control theory terms, detection, identification and measurement helps in creating a feedback loop to adjust the system.

There have been a number of examples of pervasive detection (and sometime identification) that has led to useful applications.  Here are few examples:

- In the 70′s the volume level of the radio in the car would automatically adjust to the ambient road noise.

- Street lights turned on at dusk.

- Handheld computers and mobile phones got connectivity to WiFi services by detecting wireless signals.

- Heating and Cooling systems adjusted the temperature based on outside temperature and number of people in the room.

- Escalators and Elevator adjusted their performance based on number of people using them at any given time.

- In the Web2.0 world, applications are smart enough to detect availability of services.  In distributed computing, detection of multiple resources to performa particular task was employed to get fast results.

The best example of detection is the human system, constantly our body detects temperature, danger, emotions, sounds, etc. and reacts to it.  Most of the detection is automatic and we don’t even think about it – that is the definition of ‘pervasive’.  ‘Pervasive’ is something we notice only when it is absent.

DETECTION:

There are various ways of detection.  Here we will assume what we are detecting include people, things, and services.  We will call them objects.

Detection can be ‘announced’ or ‘found’

Detection can be done by announcement.  The object that wants to be detected announces itself.  Examples of this are beacons, pingers, and similar techniques.  This mechanism is static and the object has to continuously announce its presence.  This method is also nice, because the absence of announcement can automatically mean that the object is not-present.

Automatically detecting the presence of an object is more interesting.  Here the detection takes place with no effort on the part of the object.

We will introduce another set of terms – observer and observered.  Both observer and observed can be objects.  As defined above objects can be people, things or services.

Technologies for Detection

I will cover this in the next blog…