IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use
International Journal of Human-Computer Interaction
The FindIT Flashlight: Responsive Tagging Based on Optically Triggered Microprocessor Wakeup
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
LANDMARC: indoor location sensing using active RFID
Wireless Networks - Special issue: Pervasive computing and communications
A support system for finding lost objects using spotlight
Proceedings of the 7th international conference on Human computer interaction with mobile devices & services
MAX: human-centric search of the physical world
Proceedings of the 3rd international conference on Embedded networked sensor systems
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks
Proceedings of the workshop on Real-world wireless sensor networks
Objects calling home: locating objects using mobile phones
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Surviving wi-fi interference in low power ZigBee networks
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
DrawerFinder: finding items in storage boxes using pictures and visual markers
Proceedings of the 16th international conference on Intelligent user interfaces
IteMinder: finding items in a room using passive RFID tags and an autonomous robot (poster)
Proceedings of the 13th international conference on Ubiquitous computing
Find my stuff: a search engine for everyday objects
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
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Searching for misplaced keys, phones, or wallets is a common nuisance. Find My Stuff (FiMS) provides search support for physical objects inside furniture, on room level, and in multiple locations, e.g., home and office. Stuff tags make objects searchable while all other localization components are integrated into furniture. FiMS requires minimal configuration and automatically adapts to the user's furniture arrangement. Object search is supported with relative position cues, such as "phone is inside top drawer" or "the wallet is between couch and table," which do not require exact object localization. Functional evaluation of our prototype shows the approach's practicality with sufficient accuracy in realistic environments and low energy consumption. We also conducted two user studies, which showed that objects can be retrieved significantly faster with FiMS than manual search and that our relative position cues provide better support than map-based cues. Combined with audiovisual feedback, FiMS also outperforms spotlight-based cues.