Human bodies and movements exhibit inherent symmetry. However, an important class of everyday movements, such as walking, does not maintain symmetry at every time instance. The symmetry in these movements is a spatiotemporal glide-reflection symmetry. The ability to measure this type of symmetry will provide us opportunities for various computer-aided applications including health monitoring, rehabilitation, and athletic training. In this paper we propose a method that uses the tools from elastic shape analysis to provide continuous symmetry scores which measure the degree of glide-reflection symmetry in movements. These scores can be updated online after each frame, and easily combined to drive comprehensible feedback. Our preliminary experiment demonstrates that our symmetry scores can well distinguish between a normal gait and simulated stroke and Parkinsonian gaits. Our results also suggest that using the Riemannian elastic metric provides better scores than Euclidean approaches.