Disability to move hands perfectly is one of the most severe human physical disabilities, and it is mostly common among adults or those who have experienced serious accidents. It is desired to find a methodology to restore the motion of the hand. To this aim, we proposed and evaluated the use of wearable robots (i.e., a smart glove) for clinical therapy that is equipped with shape memory alloy (SMA) actuators. The developed robotic tool, which is a smart glove, uses the structure of human hand as a base mechanism. This glove compensates the weakness of the hand muscles utilizing the forces produced by SMA actuators. The attractive property of high force to weight ratio in SMAs makes this glove a good candidate to be used as a wearable system.
The glove actuation mechanism is tendon driven. For every finger, two active Degrees Of Freedom (DOFs) are supported in design. Consequently, four tendons are considered for activating each DOF in order to complete opening and closing phases of the fingers. Totally, twenty tendons are used for rehabilitation of a hand through the glove. Using kinematic relations between tendon length and finger movement, the required deflection of each tendon is extracted. Since a short length of SMA wires cannot provide an enough displacement of tendons in the glove, therefore, an extra mechanism was embedded to the developed glove to support the required length of SMA wires. The SMA actuators were selected and mounted on the system to support the tendons of the mechanism effectively. Moreover, the gripping force provided by the developed glove was also studied. To this end, an analysis was accomplished to extract the relationship between tendon actuation and gripping force of the glove.
The obtained results offered a proper model for such a tendon driven glove. Coupling the model of the SMA actuators to that of the tendon driven glove, a composite model of smart glove was extracted. The aforementioned model was simulated numerically. Furthermore, the results were compared with those of the real-world prototype. The obtained results revealed the accuracy of the developed model which will be then employed for both system optimization and model-based control design.