Gait behaviour is a key health metric. Temporal, spatial and kinetic walking gait parameters are valuable in enhancing sport performance and early health diagnostics Full gait assessment requires a gait clinic and existing wearable gait tracking systems typically measure isolated subsets of parameters tailored to specific applications. This is useful when the condition to be monitored is known, but fails to offer a comprehensive view of an individual’s gait traits when their pathology is unknown or changing, or a general assessment is required. To support holistic walking gait tracking, we introduce WalkEar, a novel sensing platform designed to simultaneously track gait parameters using commodity earbuds. WalkEar operates by detecting gait events to derive temporal gait parameters and segment the IMU data. WalkEar then progresses earable gait assessment by, for the first time, estimating kinetic gait parameters and reconstructing the vGRF curve using machine learning. Each parameter is calculated on a step-to-step basis for gait variability and asymmetry. We developed an earbud prototype and collected data from 13 participants using gold standard force plates and instrumented treadmill ground truth. Extensive experiments demonstrate the promising performance of WalkEar, achieving an overall MAPE of 5.1% in estimating gait, 2.0% MAPE on kinetic gait parameters, and an NRMSE of 5.3% for vGRF curve reconstruction.