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Transitioning from one activity to another is oneof the key challenges of prosthetic control. Vision sensorsprovide a glance into the environment’s desired and futuremovements, unlike body sensors (EMG, mechanical). This couldbe employed to anticipate and trigger transitions in prosthesisto provide a smooth user experience.A significant bottleneck in using vision sensors has beenthe acquisition of large labeled training data. Labeling theterrain in thousands of images is labor-intensive; it would beideal to simply collect visual data for long periods withoutneeding to label each frame. Toward that goal, we apply anunsupervised learning method to generate mode labels forkinematic gait cycles in training data. We use these labels withimages from the same training data to train a vision classifier.The classifier predicts the target mode an average of 2.2 secondsbefore the kinematic changes. We report 96.6% overall and99.5% steady-state mode classification accuracy. These resultsare comparable to studies using manually labeled data. Thismethod, however, has the potential to dramatically scale withoutrequiring additional labeling.