Biomechanical motion data involving impacts are not adequately represented using 31 conventional low-pass filters (CF). Time-frequency filters (TFF) are a viable alternative, but 32 have been largely overlooked by movement scientists. We modified Georgakis and 33 Subramaniam’s (2009) fractional Fourier filter (MFrFF) and demonstrated it performed better 34 than CFs for obtaining lower leg accelerations during football instep kicking. The MFrFF 35 displayed peak marker accelerations comparable to a reference accelerometer during foot-to-36 ball impact (peak % error = -5.0 ± 11.4%), whereas CFs severely underestimated these peaks 37 (30 - 70% error). During the non-impact phases, the MFrFF performed comparably to CFs 38 using an appropriate (12 - 20Hz) cut-off frequency (RMSE = 37.3 ± 7.6 m/s2 vs. 42.1 ± 11.4 39 m/s2, respectively). Since accuracy of segmental kinematics is fundamental for understanding 40 human movement, the MFrFF should be applied to a range of biomechanical impact scenarios 41 (e.g. locomotion, landing and striking motions) to enhance the efficacy of study in these areas.