A transparent wheel-based platform for locomotion-on-demand and multi-view body and facial kinematics in head-fixed mice

Understanding how the brain transforms sensory input and internal state into coordinated action requires behavioral paradigms that provide precise, multimodal measurements of movement and arousal while remaining compatible with neural recording techniques. Here we present a modular behavioral platform that enables stimulus-evoked locomotion in head-fixed mice using a transparent running wheel combined with air-stream stimulation. The design provides direct ventral access for imaging paw movements while simultaneously capturing body kinematics, facial motion, and eye-related signals from multiple camera views. The system integrates Arduino-based stimulus control, rotary encoder measurements, Raspberry Pi-based videography, and LED-based visual markers for event-level alignment across independently acquired data streams. Using a proof-of-principle dataset from well-trained animals, we show that brief air delivery reliably induces structured locomotion with reproducible trial timing. Optical-flow-based motion metrics and DeepLabCut pose estimation reveal robust, stimulus-locked increases in paw, limb, and facial movements during air-on epochs relative to air-off periods. LED-based event markers enable consistent event-level identification of air-on and air-off epochs across video streams despite differences in sampling rates. Together, these features provide a flexible framework for studying stimulus-driven locomotion and multi-view behavioral dynamics under head fixation, with straightforward compatibility for integration with neural imaging and electrophysiology recording approaches.