Tetsuo Yamabe, Ayako Takagi, and Tatsuo Nakajima. 2005. Citron: A Context Information Acquisition Framework for Personal Devices. In Proceedings of the 11th IEEE international Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’05, full paper)
Citron : Context Information Acquisition Framework on Personal Devices
Citron : Context Information Acquisition Framework on Personal Devices Distributed Computing Laboratory Waseda University Tetsuo Yamabe, Ayako Takagi, Tatsuo Nakajima
Outline1. Introduction2. Muffin3. Sensors on Muffin4. Context acquisition on Muffin5. Citron6. Sample application7. Experiments result8. Conclusion and future direction
Introduction• It is expected that personal devices acquire a perceptual ability and recognize a user’s context information. – Why personal devices? • Tight partnership with a user • Connectivity to a user and context-aware services – How they recognize? • Incorporate sensors and analyze acquired values " What type of sensors are useful to acquire a user’s context ? " What is required in the process of context acquisition ?
• We have developed Muffin, which is a prototype of a sensory personal device, to investigate sensors’ characteristics and data processing process.• Also, we have developed a framework named Citron… – to utilize the advantage of multiple sensory personal device. – to implement context analysis modules on it.• By running context-aware application on top of Citron, we present… – how Citron bring out Muffin’s capability – possibilities of personal devices fabricated with multiple sensors
What is Muffin??• Muffin is a prototype of the future sensor device for research on ubiquitous computing area. – Developed by a collaboration work with Nokia Research Center – Sensing capability for context-awareness • 15 kinds of sensors in a PDA size box – Linux OS – Wired / Wireless interface • Bluetooth, IrDA, WLAN • USB, Serial port, PCMCIA slot
Sensors on Muffin• Sensors on Muffin are roughly divided into 4 categories. • Environmental sensors Alcohol gas sensor • Physiological sensors Relative humidity sensor • Motion/Location sensors Air temperature sensor • Other sensorsSkin resistance sensor Rear cameraGrip sensorFront cameraRFID reader GPSMicrophonePulse sensorBarometerCompass / Tilt sensor3D Linear accelerometerSkin temperature sensor Ultrasonic range finder
Context acquisition on Muffin• We performed some experiments about context acquisition on Muffin; and found that… " Validity of sensor value and analysis algorithm changes frequently according to a user’s taking style. " Some sensors’ characteristics require long term data logging. Muffin Waist-mounted Held Held User Not watching Not watching Watching Pulse invalid valid valid Standing or not ? invalid invalid valid
" Multiple sensors enable reliable context acquisition by analyzing information from multiple aspects of view. Walking or running or not Under watch or not Moving or stop Activity(1- 5) Held or not Top side Skin resistance Activity(1 - 5) Accel Ultra range finder" We should reflect the already recognized context… – to select an appropriate set of sensors and analysis algorithms – by modeling relationships among other context → Middleware support should be offered to application programmers.
Citron: architecture overview Application• A framework for context acquisition Citron API on sensory personal devices – Citron Worker • Context analysis module put • Work independently Citron Worker Citron Space • Enable parallel context processing – Citron Space • Shared space for storing context read • Core module of a blackboard model Output • Citron supports … - Analyzed context – Hierarchical context abstraction Context – Context analysis from multiple aspects of view Sensor – Switching analysis module Input - Sensor data according to context - Context
Sample application : StateTracer• StateTracer displays the track of walking route with user’s state in real time. – Not only walking or not, but also walking speed and resting time – No location systems or infrastructure Walking At rest
Working modules on StateTracer Orientation Walking (state, speed)Can detect speed,but time consuming Walking (FFT) Walking (Threshold)Citronworker Watching Activity Can not detect speed,Sensor Holding Top_side but responsive compass skin resistance accel_x, accel_y, accel_z Orientation : “N”, “NW”, “W”, “SW”, “S”, “SE”, “E”, “EW” Walking_State : “walking”, “resting” Walking_Speed : “0”, “1”, “2”, “3”, “4”
Experimental result• Walk around a lot (50m x 100m) Start and Goal – Change walking speed – Two stop point Stop point• Change working analysis modules Walk fast – Case1 : Walking (threshold) worker only Walk slowly – Case2 : Walking (FFT) worker only – Case3 : Both workers Case 1 Case 2 Case 3
Conclusion and future direction• Coordination among analysis module with sharing context information is flexible and effective way to acquire context on Muffin. – Bring out capability of Muffin and its perceptual ability – Enable reliable context acquisition in practical usage• We continue to research on context acquisition on personal devices based on Muffin and Citron. – Rearrange placement of sensors and reshape its form – Distribute sensors as a wearable sensor device – Coordination with remote resource over network
Cookie : Coin size wearable sensor• Size • Sensor – 24mm x 22mm x 8~10mm – Compass – Almost same size as 10 Yen coin. – Ambient Light Sensor• Three stacked board structure – Pulse sensor – Main board – Skin temperature sensor – Sensor board – GSR sensor – Extension board – UV sensor• Running time – RGB color sensor – About 1 hr (with 2032 size battery) – 3-Axis Linear Accelerometer – Vibration motor