To foster diagnosis of gait abnormalities as well as tracking the recovery rate in the course of healing, automated gait classification methods have great added value. Therefore, gait classification based on a dictionary learning approach was developed and tested. With an average classification rate of about 93%, the proposed method offers great potential to be deployed in support of digital healthcare and therapy. Moreover, by providing efficient data storage as well as low runtime, it is ideal for use in portable diagnostic tools.