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WiCoSens: a wearable, intelligent color sensing platform for non-invasive storage shelf identification

Published: 11 September 2017 Publication History

Abstract

Reaching out onto a shelf or into a cabinet and taking something out is a fundamental part of many activities. In our daily life we carry out several picking and placing actions from/to shelves, cupboards, racks, containers, etc. With enough instrumentation identifying the shelf from which something has been taken is not a fundamental problem. However, reliably detecting specific shelf from which something has been taken with an unobtrusive and cheap setup remains, in general, an open problem. As a solution we developed WiCoSens - a wrist worn color sensor (CS) array to detect color coded surfaces. We describe the hardware design, the identification method and a simple color coded shelf setup to evaluate the system. Initial results show 100% accuracy with user independent training.

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  • (2023)Robots’ picking efficiency and pickers’ energy expenditureComputers and Industrial Engineering10.1016/j.cie.2022.108918176:COnline publication date: 1-Feb-2023

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  1. WiCoSens: a wearable, intelligent color sensing platform for non-invasive storage shelf identification

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    cover image ACM Conferences
    ISWC '17: Proceedings of the 2017 ACM International Symposium on Wearable Computers
    September 2017
    276 pages
    ISBN:9781450351881
    DOI:10.1145/3123021
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 11 September 2017

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    Author Tags

    1. activity recognition
    2. color sensor
    3. order picking
    4. warehouse management
    5. wearables

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    • SmartWerk project

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    UbiComp '17

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    Overall Acceptance Rate 38 of 196 submissions, 19%

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    • (2023)Robots’ picking efficiency and pickers’ energy expenditureComputers and Industrial Engineering10.1016/j.cie.2022.108918176:COnline publication date: 1-Feb-2023

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