Evidence map›Paper›PMID 36236730›Full record

ArticleSensors (Basel, Switzerland)2022

Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs.

Marc Foster, Tianfu Wu, David L Roberts, Alper Bozkurt

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Marc FosterDepartment of Electrical and Computer Engineering, NC State University, Raleigh, NC 27695, USA.ORCID 0000-0001-5493-0535
Tianfu WuDepartment of Computer Science, NC State University, Raleigh, NC 27695, USA.ORCID 0000-0001-8911-5506
David L RobertsDepartment of Computer Science, NC State University, Raleigh, NC 27695, USA.ORCID 0000-0001-7872-4840
Alper BozkurtDepartment of Electrical and Computer Engineering, NC State University, Raleigh, NC 27695, USA.

Funding

National Science Foundation 1554367National Science Foundation ECC-1160483National Science Foundation IIS-1329738
6 · The paper itself

Abstract

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance.

Indexed as

ElectrocardiographyWorking DogsAlgorithmsAnimalsDogsHeart RateMonitoring, Physiologic3D printingdroneECGelectrodesembedded systemheart rate variabilitymachine learningUAVwearable

Identifiers

PMID36236730
PMCPMC9572382

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.