Method for estimation of distance between objects and its application for finding lost objects

Konishi, Satoshi; Kawakita, Yuusuke; Ichikawa, Haruhisa

doi:10.1109/ccnc.2012.6181152

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…GPS and UMTS cell of origin are used to provide a rough position when the user is not in range. Konishi et al [10] avoid active search by assuming usercarried RFID readers, which periodically sense tagged objects in the user's proximity and store those snapshots. When searching for an object, their system returns a list of surrounding objects from matching snapshots.…”

Section: Related Workmentioning

confidence: 99%

“…Lamming and Bohm [11] equip objects with small batterypowered devices (SPECs FindIT [14] n/a FETCH [6] n/a Frank et al [4] n/a Konishi et al [10] n/a IteMinder [9] Hallberg et al [5] Nakada et al [15] Brownie [17] SearchLight [2] DrawerFinder [8] n/a SPECs [11] Snoogle [19] MAX [20] FiMS not supported ( ), partially supported ( ), supported ( ) Table 1: Comparison of existing object search approaches.…”

Section: Related Workmentioning

confidence: 99%

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Nickels

Knierim

Könings

et al. 2013

Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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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 mapbased cues. Combined with audiovisual feedback, FiMS also outperforms spotlight-based cues.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Find my stuff

Nickels

Knierim

Könings

et al. 2013

Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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“…The user can determine the location of the lost object from the information about its group presented by the system. The basic concept of this system is similar to that of Konishi et al 's system [2]. Unlike his system, ours uses the RSSIs to estimate object groups and employs a smartphone to collect sensing data.…”

Section: Support System For Finding Lost Objectsmentioning

confidence: 99%

Active RFID Attached Object Clustering Method with New Evaluation Criterion for Finding Lost Objects

Tanbo

Nojiri

Kawakita

et al. 2017

Mobile Information Systems

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An active radio frequency identification (RFID) tag that can communicate with smartphones using Bluetooth low energy technology has recently received widespread attention. We have studied a novel approach to finding lost objects using active RFID. We hypothesize that users can deduce the location of a lost object from information about surrounding objects in an environment where RFID tags are attached to all personal belongings. To help find lost objects from the proximity between RFID tags, the system calculates the proximity between pairs of RFID tags from the RSSI series and estimates the groups of objects in the neighborhood. We developed a method for calculating the proximity of the lost object to those around it using a distance function between RSSI series and estimating the group by hierarchical clustering. There is no method to evaluate whether a combination is suitable for application purposes directly. Presently, different combinations of distance functions and clustering algorithms yield different clustering results. Thus, we propose the number of nearest neighbor candidates (NNNC) as the criterion to evaluate the clustering results. The simulation results show that the NNNC is an appropriate evaluation criterion for our system because it is able to exhaustively evaluate the combination of distance functions and clustering algorithms.

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“…Konishi et al [3] avoid active search by users by assuming that they carry RFID readers periodically sensing tagged objects in their environment and storing those snapshots in a database. When searching a specific object, the system returns the list of surrounding objects.…”

Section: Related Workmentioning

confidence: 99%

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Knierim

Nickels

Musiol

et al. 2012

Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia

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Searching for lost keys, wallets or mobile phones is a common nuisance. Compared to digital information, search support for physical objects is very limited. We propose Find My Stuff (FiMS) as a search engine for physical objects. We built a fully functional Arduino-based prototype. FiMS offers the users a simple search interface to locate tagged physical items in different indoor environments. A hierarchical search process ensures energy efficient and effective searches. Instead of a fixed search infrastructure, the localization system is based on SmartFurniture equipped with RFID readers and ZigBee modules. Search results provide intuitive search cues based on relative positioning to support users in the physical retrieval of their lost objects. The system requires no manual calibration and is robust against rearrangement of SmartFurniture. Safety mechanisms prevent abuse of the system and protect user privacy.

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Method for estimation of distance between objects and its application for finding lost objects

Cited by 5 publications

References 3 publications

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Active RFID Attached Object Clustering Method with New Evaluation Criterion for Finding Lost Objects

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