Teaching physical motions such as riding, exercising, swimming, etc. to human beings is hard. Coaches face difficulties in communicating their feedback verbally and cannot correct the student mid-action; teaching videos are two dimensional and suffer from perspective distortion. Systems that track a user and provide him real-time feedback have many potential applications: as an aid to the visually challenged, improving rehabilitation, improving exercise routines such as weight training or yoga, teaching new motion tasks, synchronizing motions of multiple actors, etc. It is not easy to deliver real-time feedback in a way that is easy to interpret, yet unobtrusive enough to not distract the user from the motion task. I have developed motion feedback systems that provide real-time feedback to achieve or improve human motion tasks. These systems track the user’s actions with simple sensors, and use tiny vibration motors as feedback devices. Vibration motors provide feedback that is both intuitive and minimally intrusive. My systems’ designs are simple, flexible, and extensible to large-scale, full-body motion tasks. The systems that I developed as part of this thesis address two classes of motion tasks: configuration tasks and trajectory tasks. Configuration tasks guide the user to a target configuration. My systems for configuration tasks use a motion-capture system to track the user. Configuration-task systems restrict the user’s motions to a set of motion primitives, and guide the user to the target configuration by executing a sequence of motion-primitives. Trajectory tasks assume that the user understands the motion task. The systems for trajectory tasks provide corrective feedback that assists the user in improving their performance. This thesis presents the design, implementation, and results of user experiments with the prototype systems I have developed.

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