Movements during sleep characterize sleep disorders, which can disturb sleep or its onset, impacting sleep quantity and quality. Video-polysomnography is the current gold standard to assess movements during sleep, but its availability is limited. Using data recorded with a 3D time of flight sensor, we developed a novel method of encoding temporal and spatial information of automatically identified movements during sleep. In a cohort of 20 insomnia patients and 18 controls, we showed that this novel method holds important information able to discriminate the groups. Future studies will explore the methodology in the context of other sleep disorders.