Evidence map›Paper›PMID 42909115›Full record

ArticleFrontiers in behavioral neuroscience2026

eeeHive: a new HF RFID-based automated behavioral monitoring system enabling high spatiotemporal resolution tracking of group-housed animals.

Seico Benner, Suzuka Shiono, Toshinori Kagawa, Kiyohiko Hattori, Hidenori Yamasue, Hans-Peter Lipp, Toshihiro Endo

Abstract read
In one paragraph

Article in Frontiers in behavioral neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

7 authors.

Seico BennerCenter for Health and Environmental Risk Research, National Institute for Environmental Studies, Tsukuba, Japan.
Suzuka ShionoPhenovance LLC, Kashiwa, Japan.
Toshinori KagawaGrid and Communication Technology Division, Grid Innovation Research Laboratory, Central Research Institute of Electric Power Industry, Yokosuka, Japan.
Kiyohiko HattoriDepartment of Information Systems and Multimedia Design, School of Science and Technology for Future Life, Tokyo Denki University, Tokyo, Japan.
Hidenori YamasueDepartment of Psychiatry, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Hans-Peter LippFaculty of Medicine, Institute of Evolutionary Medicine, University of Zürich, Zürich, Switzerland.
Toshihiro EndoPhenovance LLC, Kashiwa, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long-term, automated tracking of group-housed social animals using RFID (radio frequency identification) is a promising approach in ethological neuroscience. However, low-frequency (LF) RFID, while long-established in the field, is constrained by its inherent low data rates, which lead to two critical limitations: (1) compromised spatiotemporal resolution, and (2) the inability to identify multiple tags (animals) simultaneously. To address these limitations, we developed eeeHive, a high-frequency (HF) RFID-based animal tracking system with a fully custom hardware architecture that enables high-speed, multiplexed antenna polling and concurrent multi-tag reading. The polling time per antenna in eeeHive was 5.9 ms, with an additional 8.2 ms read time per tag. We applied the system to track 24 mice for 1 week, and six common marmosets for 7 weeks. The system successfully tracked individuals even within dense clusters, revealing complex behavioral traits characterized by spatial utilization, temporal dynamics, behavioral regularity, and inter-individual relationships. Additional tests with Japanese fire-bellied newts and Nile tilapia juveniles demonstrated comparable tracking performance in aquatic environments. Taken together, eeeHive overcomes the inherent limitations of conventional LF RFID, establishing a powerful HF RFID-based platform for fine-scale behavioral tracking of group-housed animals across terrestrial and aquatic species.

Indexed as

animal welfareautomated home-cage monitoring (AHCM)eeeHiveethological approachgroup-housinghigh-frequency (HF) RFIDISO/IEC 15693RFID

Identifiers

PMID42909115
PMCPMC13683769

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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.