<i>eNut</i>: A Sensing System to Measure the Acquisition of Foraging Proficiency in Wild Tree Squirrels

We present a three-part sensing system to measure the acquisition of foraging proficiency in wild tree squirrels. The first component is the eNut: a 3-D printed enclosure in the size and shape of a large food item, such as a walnut. The eNut contains an inertial measurement unit (IMU), consisting of an accelerometer and a gyroscope, along with a capsule containing a food reward (e.g., chopped nuts), motivating the squirrel to manipulate the eNut to open the reward capsule. The second component is a radio frequency identification (RFID) gate that can detect the presence of a known individual. The reader generates time-stamped labels for the analysis of simultaneous video recordings of the squirrel movements. Finally, the third component is a data collection pipeline, which aggregates the sensor data in a cloud backend server. The design process culminated in a fully functional prototype system tested under three field conditions: two outdoor sites used for juvenile squirrel rehabilitation and a third site with free-ranging adult squirrels. The accuracy of the collected data is assessed by leveraging laboratory settings in which acceleration and rotation were checked against reference values produced by a testing infrastructure, custom-built for this application. Finally, we present an inventory of food item manipulation movements (rotation and shaking) that are identified and quantified with the eNut system. This system allows a level of granular analyses of foraging decisions that was not feasible with prior technology, which would have important translational value in paradigms of animal behavior and wildlife rehabilitation.

Authors

Institutions

Publication Details

Journal
IEEE Sensors Journal
Published
2024-08-23
DOI
https://doi.org/10.1109/jsen.2024.3435834
Citations
1
Primary Topic
Animal Ecology and Behavior Studies
Type
article
Field-Weighted Citation Impact
0.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

eNut: A Sensing System to Measure the Acquisition of Foraging Proficiency in Wild Tree Squirrels

Lucia F. Jacobs, Avikam Chauhan, Ms. Simran Chauhan, Fangyu Wu et al.
1 citations
IEEE Sensors Journal
Animal Ecology and Behavior Studies
0.46
article

eNut: A Sensing System to Measure the Acquisition of Foraging Proficiency in Wild Tree Squirrels

Lucia F. Jacobs, Avikam Chauhan, Ms. Simran Chauhan, Fangyu Wu, Myriam Bayen, Michael T. Kaiser, Fahd A. Althukair
article en
1 citations

Abstract

We present a three-part sensing system to measure the acquisition of foraging proficiency in wild tree squirrels. The first component is the eNut: a 3-D printed enclosure in the size and shape of a large food item, such as a walnut. The eNut contains an inertial measurement unit (IMU), consisting of an accelerometer and a gyroscope, along with a capsule containing a food reward (e.g., chopped nuts), motivating the squirrel to manipulate the eNut to open the reward capsule. The second component is a radio frequency identification (RFID) gate that can detect the presence of a known individual. The reader generates time-stamped labels for the analysis of simultaneous video recordings of the squirrel movements. Finally, the third component is a data collection pipeline, which aggregates the sensor data in a cloud backend server. The design process culminated in a fully functional prototype system tested under three field conditions: two outdoor sites used for juvenile squirrel rehabilitation and a third site with free-ranging adult squirrels. The accuracy of the collected data is assessed by leveraging laboratory settings in which acceleration and rotation were checked against reference values produced by a testing infrastructure, custom-built for this application. Finally, we present an inventory of food item manipulation movements (rotation and shaking) that are identified and quantified with the eNut system. This system allows a level of granular analyses of foraging decisions that was not feasible with prior technology, which would have important translational value in paradigms of animal behavior and wildlife rehabilitation.

IEEE Sensors JournalVol. 24(19)
University of California, Berkeley (US)
Zero hunger
Openalex Percentile: Top 30%
Animal Ecology and Behavior Studies
0.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.