Background: Objective gait analysis is fundamental to the clinical management of motor function in individuals with Cerebral Palsy (CP), guiding therapeutic interventions and assessing surgical outcomes. The current gold standard, laboratory-based 3D motion capture, is expensive, resource-intensive, and provides only a brief snapshot of performance in an artificial environment, limiting its utility for routine monitoring. This study addresses the need for an accessible, ecologically valid alternative.

Objective: To develop, validate, and demonstrate the clinical feasibility of a low-cost, wearable system using two Inertial Measurement Units (IMUs) for the objective analysis of key spatio-temporal gait parameters in ambulatory adolescents with CP.

Methods: Twenty ambulatory adolescents (mean age 14.2 ± 2.1 years) with a diagnosis of spastic diplegic CP (GMFCS Levels I-III) were recruited. Each participant wore two IMU sensors, affixed to the lateral aspect of each ankle. Participants performed a 10-Metre Walk Test (10MWT). Raw sensor data were processed using a custom script to identify gait events and calculate spatio-temporal parameters, including walking speed, cadence, step length, and a gait asymmetry index. The IMU- derived walking speed was validated against the manually timed 10MWT. Gait parameters were compared across GMFCS levels.

Results: A very strong, positive correlation was found between the walking speed calculated by the IMU system and the manually timed 10MWT (r=0.98, p<0.001). The IMU system detected statistically significant differences in gait parameters across GMFCS levels. Mean gait speed decreased significantly with increasing functional impairment (GMFCS I: 1.28 m/s, GMFCS II: 1.05 m/s, GMFCS III: 0.81 m/s; p=0.002). Similarly, cadence and step length were significantly reduced, while the gait asymmetry index was significantly higher in participants with greater motor impairment (p<0.01).

Conclusion: A simple, low-cost, two-sensor IMU system can provide valid, reliable, and clinically meaningful data on gait in adolescents with CP. This technology offers a practical and scalable solution for moving gait analysis from the specialised laboratory into community clinics and home environments, facilitating objective, long-term monitoring and supporting the delivery of personalised healthcare.