Human motion lies within a range of low frequencies. Filtered and down-sampled motion capture (mocap) data can thus provide meaningful representations for computational models. However, little is known about the kinematic bandwidth of Sign Language (SL), apart from isolated signs. Studies examining isolated signs suggested that SL could be limited to relatively low frequencies. This is unlikely to be appropriate for real-life conditions where signs are produced faster and are combined with several other rapid motion features. The present study investigated the spectral content of a multi-signer mocap dataset of continuous signing in French Sign Language. Across six different signers, Power Spectral Density estimation and residual analysis of the mocap data revealed that SL motion can be limited to a 0-12-Hz bandwidth, which is substantially wider than stateof-the-art estimates on isolated signs. More specifically, filtering the movements below 6 Hz caused distortion of the rapid motion, which suggests that SL motion involves higher frequencies in real-life conditions. The importance of kinematic bandwidth estimation is further addressed with a machine learning model trained to identify the six signers of the dataset. The performance of the model significantly decreased when using inappropriate bandwidths.