List+of+Home+Datasets

**Instrumented living environments**
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 * Aware Home at Georgia Tech
 * Gator Tech Smart House
 * iSpace/iDorm at University of Essex
 * MARC Smart House at University of Virginia: Medical Automation Research Center
 * PlaceLab: an MIT and TIAX initiative for studying behavior in a residential condominum
 * Smart Home at Duke University: A live-in laboratory for students
 * Tiger Place at University of Missouri: Instrumented assistive living
 * The Multicom intelligent flat : an user-oriented smart home platform
 * Great Northern Haven: a purpose-built development of 16 smart apartments occupied by older adults on a full-time basis since June 2010.

**Data sets generated from instrumented living environments**

 * **Source** || **Scope** || **Sensors** || **Researcher's**
 * Home?** || **Annotation** || **Availability** ||
 * Intel Berkeley || 4 houses, 3 weeks each || UbiSense tags || No || Sparse self-report data || Unknown ||
 * Great Northern Haven, Dundalk, Ireland || 16 purpose-built smart apartments occupied on a full-time basis by older adults since June 2010 (real-world continuous data). || Over 2,300 sensors in the development including motion sensors, contact sensors, brightness sensors, temperature sensors, electricity sensors, water sensors. || No || None || Open to research collaboration ||
 * MARC || 1 house, 12 weeks || Motion/switches || No || Self report from PDA || Unknown ||
 * Intel Seattle || Subjects perform activities on list || RFID/bracelet || Yes || Researcher observation || Unknown ||
 * Intel Seattle, U. Washington || One author performs activities on list, 10 days || RFID/2 gloves || Yes || Researcher observation || Unknown ||
 * Intel Seattle, U. Washington || 3 subjects perform activities on list. 1 hour of data || RFID/bracelet || Yes || Researcher observation || Unknown ||
 * Intel Seattle, U. Washington || 2 subjects perform activities for 12 hours || Acceleration, light, temperature, barometric pressure || No || Researcher observation || Unknown ||
 * UC Berkeley || 13 male and 7 female between ages 19 and 75 perform repetitive actions in 13 action categories. || Accelerometer and gyroscope. (EIP, ECG sensors planned) || Lab || Category annotated || [|Yes] ||
 * Hiroshima U. || 1 house, 6 months || IR occupancy || Unknown || None || Unknown ||
 * Waseda U. || 3 houses for one month each (3 subjects) || Switches, IR occupancy, temperature, humidity, carbon dioxide || No || None || Unknown ||
 * CMU || 1 house, 5 days || 24 motion detectors, 24 door switches || Yes || Self report room location || Unknown ||
 * CMU || 1 home, 6 weeks || Acoustical on pipes || Yes || None || Case by case ||
 * GaTech || 1 year || 16 floor pressure || No || 6 weeks subject diaries || Unknown ||
 * Curtin U. of Technology || One author performs 30 examples of single activity (Breakfast) || Switches, pressure mats || Yes || Researcher observation || Unknown ||
 * MIT || Weeks || MITes: Binary switches || No || Yes || [|Yes] ||
 * MIT || Test datasets available; 2.5 months to be released || PlaceLab: Multi-modal, 800+ || No || Yes || [|Yes] ||
 * U. Rochester LACE || ADL simulations: 5 participants, 2 sessions, 6 activities, 5 trials || Video and RFID || Simulated home || Unknown || [|With Approval] ||
 * Washington State U. CASAS || Varies || Motion sensors, door sensors, VOIP usage, object sensors || No || Researcher observation || [|Yes] ||
 * U. Tokyo ISIS || Unknown || Motion capture || No || Researcher observation || [|Yes] ||
 * U. Amsterdam || 1 house, 4 weeks || Wireless sensor network of reed switches and water level sensor || Yes || Self-report through bluetooth headset || [|Yes] ||
 * CMU QoLT || 30 subjects cooking 5 recipes (brownie, sandwich, eggs, salad and pizza) in a kitchen || Video, Audio, Motion Capture, IMUs, bluetooth accelerometers, RFID/iBracelet, eWatch, Bodymedia || No || Yes || [|Yes] ||
 * UF / Washington State || Synthesized data set version of Washington State CASAS (with comparison) || Same as CASAS || No || Researcher observation || [|Yes] ||
 * UF || No dataset. A specification document of a proposed standard to represent datasets. Initiated by UF but we call for participation by workshop participants. || Document title: SDDL: Sensory Dataset Description Language. || No || - || [|Yes] ||
 * U. Edinburgh || Video dataset of detected people walking through the atrium of a building, where the data comes from an overhead camera. || Bounding boxes and colour histograms for moving targets; lists of detections and splines || Public space || No || [|Yes] ||
 * LIG/Multicom || 1 flat, 2 sets :
 * A set on comfort evaluation, with 20 samples including users inputs
 * A set with one year of various data || Presence, user interaction, indoor metrics, energy measurements, etc || Yes || Users self perceptions || Yes ||
 * TIMC-IMAG and LIG || 1 house, 15 participants perform activities of daily living || Microphones, presence infra-red sensors, webcams, thermometer and hygrometer, home-made worn accelerometer and magnetometer || No || Researcher observation || [|Yes (more with approval)] ||