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Mobile Context Inference Using Low-Cost Sensors

Mobile Context Inference Using Low-Cost Sensors. Evan Welbourne, 591hk - 1/28/05. Mobile Context Inference. “Mobile context”: a term that encompasses many types of sub-context computing: connectivity, communication cost, nearby devices

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Mobile Context Inference Using Low-Cost Sensors

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  1. Mobile Context Inference Using Low-Cost Sensors Evan Welbourne, 591hk - 1/28/05

  2. Mobile Context Inference • “Mobile context”: a term that encompasses many types of sub-context • computing: connectivity, communication cost, nearby devices • user: user’s profile, activities, goals, social context • physical: location, sound, other people, traffic conditions • time: time of day, date, context histories • Current focus: a user’s significant places and mode of transit between them • computing: depends on the application, probably nearby APs • user: significance of places, goals during transit • physical: location, elements of surrounding place, mode of transit • time: patterns of residence in, and transit between places; low-level context histories for high-level context inference

  3. Related Work: Significant Places and Transit • Most past work uses GPS • Significant places: loss of signal or clustered over time - Marmasse et. al. - signal lost and then regained w/in radius r - Ashbrook et. al. - cluster loss spots, cluster over time outside - Patterson et. al. - knowledge, lost signal, cluster over time • Transit: speed, schedules, past patterns - Marmasse et. al. - past patterns, speed (recently) - Ashbrook et. al. - past patterns - Patterson et. al. - past patterns, speed, bus schedules • Some work uses GSM cells (Laasonen et. al.) • Significant places: cell clustering • Transit: past patterns of movement

  4. Related Work: Multi-Sensor Mobile Context Systems • Recent projects use multi-sensor platforms in phone form factor • TEA - 2-axis accel, light, temp, touch, IR sensors in a Nokia cell phoneSchmidt et. al. (began at Starlab, now at TecO) • SenSay - 3-axis accel, light, temp, voice, ambient noise; phone, box, 2 mics Siewiorek et. al. • WatchMe - GPS, 3-axis accel, mic; iPaq, big watch, microphone Marmasse et. al. • Trend: cell phones with many sensors, big storage, and fast computation

  5. Technology: Place Lab • Low-cost, indoor-outdoor, privacy-observant location system • Database of (802.11 AP)(lat,lon) and (GSM tower)(lat,lon) mappings • Calculate your current (lat,lon) using your device’s current readings

  6. Technology: Place Extraction with Place Lab • Similar to previous work, uses time-based clustering • Works indoors and outdoors • Use location traces to find significant places • Input: User’s day-to-day location traces • Output: Areas where most time is spent • Use a time-based clustering algorithm - 10 minutes- 45 minutes- 5 hours- 3 hours- 7 hours

  7. Technology: Multimodal Sensor Board (MSB) • 2” x 1” multimodal sensor platform • Capable of running independently and then relaying it to another device • All sensors grouped together as the would be on a small mobile device

  8. Our System • iPaq hx4705 + Nokia 6600 + MSB • iPaq and phone run place lab, MSB to iPaq which does processing/storage • Low-cost sensors found in an everyday devices like cell phones • Could eventually collapse into a cell phone

  9. Initial Experiments: Enhanced Place Extraction • Use other sensors to enhance our place extraction capabilities • “Zoom-in and fingerprint”: • accelerometers suggest moving / not moving • stationary for > k minutes, then “zoom-in” and fingerprint spot • treat this high-resolution fingerprint as a sub-place • Might be able to classify places based on characteristic sensor readings

  10. Initial Experiments: Mode of Transit Inference • Use other sensors to assist in mode of transit inference • 25 – 120 second Place Lab history can give us approximate speed • faster than human speeds  vehicle • moving at human speeds  look at accelerometers: running, walking • Other sensors (e.g. sound) can probably help with other distinctions

  11. MobileCAES • Main software artifact: Mobile context-aware experience sampling program • Gathers user-annotated sensor traces over a long period of time in daily life • iPaq allows checkbox questionnaires and audio notes • phone allows digital photos • Context-aware element allows us to focus on situations of interest • Architecture for supporting quick, simple context-triggers

  12. User Deployment for Data Collection • Next week: • deploying MobileCAES to 3 job coaches at UW CHDD • deploying sensor board to 3 Intel employees (probably) • I’ll use MobileCAES as well

  13. Future Directions: Applications • Studies of prospective memory • place/time-based memory, when and why do we remember? (Sellen et. al.) • design and implementation of a prospective memory aid (Lamming et. al.) • Interruptability • related to memory • use activity-level and place (Siewiorek et. al.) • Infrastructure assist • Use mode of transit to choose a motion model for particle filters • Additional sensors could help with end-user mapping efforts

  14. Future Directions: System • Integrate with other systems at UW • Assist Opportunity Knocks • Augment RFID reminder system • Collapse system into fewer components

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