eNut: A Sensing System to Measure the Acquisition of Foraging Proficiency in Wild Tree Squirrels
Year: 2024
Type: article
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.
Source: IEEE Sensors Journal
Authors: Mihir S. Chauhan, Avikam Chauhan, Myriam Bayen, Fangyu Wu, Fahd A. Althukair, Michael T. Kaiser, Lucia F. Jacobs
Institution: University of California, Berkeley
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,495
OpenAlex ID: W4401806128