A Smart Collar for Assessment of Activity Levels and Environmental Conditions for Guide Dogs

Evan Williams, Zachary Cleghern, Marc Foster, Timothy Holder, David Roberts, Alper Bozkurt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


This paper demonstrates the design and manufacturing of a smart and connected internet-of-things collar system for the collection of behavioral and environmental information from working canines. The environmental factors of ambient light, ambient temperature, ambient noise levels, barometric pressure and relative humidity are recorded by the smart collar system in addition to behavioral information about barking incidences and activity levels. The data are collected from the sensors and transmitted via Bluetooth to the handler's smartphone where the custom app also acquires GPS positioning using the on-board smartphone sensors. The stored data on the smartphone are uploaded to the IBM Cloud once the user is connected to a WiFi network. The low power design of the smart collar system permits it to be used continuously for 27 hours with a 290 mAh lithium polymer battery. The cost of the system is low enough to let the handlers have multiple collars and exchange it if needed or recharge it overnight when not in use. This system is currently being scaled up to be tested on hundreds of canine puppies by a preeminent guide dog school in the US. As a result, the design emphasis here has been on the cost and power reduction, comfortable ergonomics, user friendliness, and robustness of data streaming. We expect the system to provide continuous quantitative data for improving guide dog training programs in addition to contributing the well-being of other working dogs in the future.
Original languageEnglish (US)
Title of host publication42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)
Number of pages4
StatePublished - 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-24
Acknowledgements: The authors acknowledge the support from KAUST and NSF through CCSS-1554367, ECC-1160483, IIS-1329738. This material is also based upon work supported by IBM Faculty Awards.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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