Telerehabilitation Devices for Motion Detection and Trunk Training
Nervous system injuries lead to various dysfunctions that needs to be restored through rehabilitation training. Not all patients are privileged to receive rehabilitation training in time. Telerehabilitation improves the accessibility of rehabilitation trainings for all patients. Yet telerehabilitation in practice is limited in the aspect of assessment and training. This dissertation documents the author's work done at UA to enable telerehabilitation. The dissertation chapters cover aspects of motion assessment, risk assessment, training independency, and device compliance. Chapter one presents a low-cost and portable walker-based human motion estimation system, which enables distance supervised walker training with objective motion and force data collected. Preliminary testing is conducted to validate the concept. Chapter two designs an RGB-camera-based fall detection algorithm in complex home environments. With low-cost components, the system maintained an accuracy of 94.5%. Chapter three presented a design of a semi-active assist as needed four-bar linkage trunk rehabilitation device. This work tested the concept of semi-active activation force, which can be implemented in future devices to increase independency of trainings. Chapter four studied flexible joints with compliance to increase user safety during rehabilitation trainings. A mechanical model of a bending spring joint actuated by a tendon is developed and simplified. With the four works contributed and future works to come, the author believes that telerehabilitation can become low cost, independent, safe, and accessible.