SqueakSpeed: An Accurate, Open-Source Raspberry Pi System for Real-Time Circadian Analysis of Voluntary Wheel Running

In mice, voluntary wheel running (VWR) is a stress-free, naturalistic model for studying exercise behavior in rodents, yet commercial cyclometers used to quantify running parameters have not been validated at physiological rodent speeds. Our objective was to develop SqueakSpeed, an open-source Raspberry Pi-based system and compare its accuracy and linearity to commercial cyclometers. We hypothesize that SqueakSpeed will replicate the commercial cyclometer's recorded running parameters while providing superior circadian resolution. SqueakSpeed uses a hall sensor and neodymium magnets to record speed, distance, and acceleration, transmitting data to a cloud server in real time. VWR was recorded in CD-1 mice (~8-10 weeks old) over six days with both SqueakSpeed and a VDO M2.1 cyclometer. Discrepant distance measurements prompted mechanical validation using a DC motor across physiological running speeds (0.02-0.20 m/s). Surprisingly, the cyclometer showed overestimation at lower speeds with poor accuracy and linearity (RMSE = 0.061, MAE = 0.046, R² = 0.48) and a mean bias of -0.038, wide 95% limits of agreement (-0.136 to 0.060 m/s), significant speed-dependent proportional bias (slope=1.235, p=0.0003). In contrast, SqueakSpeed demonstrated high accuracy and , R² = 0.99), minimal mean bias (-0.00034 m/s), and narrower limits of agreement (-0.0039 to 0.0032 m/s). Commercial cyclometers are inaccurate and non-linear within the physiological range of rodent running speeds. SqueakSpeed provides accurate, real-time quantification of wheel running with high-resolution circadian analysis. These findings support its use as a precise and versatile tool for behavioral studies.