This is the first active and intuitive support system for arm, hand and wrist support that exists on the market controlled by eyetracking. Our system is modular and can be used in many different compositions, tailored to the individual patients’ needs.
The user requirement identification phase resulted in a framework for crucial themes to be addressed when designing Assistive Technology (AT) for the affected arm/hand. This framework added a number of themes from the literature that are usually overlooked, but identified as central topics in users’ views on the use and adoption of AT.
Two versions of the ENHANCE arm support were developed over the course of the project. Five controlled and actuated DOF’s and an innovative human model was implemented in the developed arm support. The second, the ExoArm has got an early version of eyetracking on a wheelchair mounted arm support. This goes far beyond the state of the art for arm and hand support.
The robotic glove is the only one that currently exists on the market that can be controlled with the eyes. So it the wrist support with active pro-supination within a small volume. The actuator for the hand support has integrated brushless motors where they are typically are not used for exoskeletons but have a number of benefits.
For the first time, we addressed transparency of a soft-robotic glove in similar ways as done with lower limbs rehab robotics, to assess whether the presence of the robot interferes with normal movement. This shows that although the glove is not completely transparent, its influence on normal movement is limited, while its added support doesn’t interfere with movement patterns. Yet, it does increase grip strength by over 15%.
A support level controller that can automatically adjust the amount of support to the need of the patient was developed. The algorithm, as well as the integrated monitoring tool that supports the decision making of Physical Therapists, are unique on the market.
Achieving gaze-contingent, action grammar-based interaction has led to a complex system that is simple to operate by naïve users; the successful use of the system by a disabled person is evidence to this and goes beyond the results obtained with more intrusive, and training intensive methods such as direct brain interfacing. The 3D gaze estimation method produces a more stable, and more accurate 3D estimation of gaze than existing methods. The modular architecture created for gaze-contingent human-robot interaction is unique and expandable. The human action grammars-based intention decoding results in more intuitive and comfortable human-robot interaction.
Testing and comparing user performance between different ways of controlling robotic arms and gloves to inform the design hasn’t been done before in rehab or assistive technology. Concluding, contrary to what is generally assumed, (subjective) user preferences don’t follow (objective) user performance. This is new and highly valuable information.
When the eNHANCE system is fully developed and funded, it can impact the lives of up to 50% of all stroke patients, as well as patients with reduced grip, spinal cord injuries, CP and ALS patients.
