Compensations for an Assistive Robotic Interface

Abstract

The goal of assistive robotic devices, such as a wheelchair-mounted robotic arms (WMRA), is to increase users’ functional independence. At odds with this goal is the fact that device interfaces tend to be rigid, requiring the user to adapt, rather than adapting to the user. Paperno, et al. (2016) identified key physical, cognitive, and sensory capabilities that affect an individual’s performance of simulated activities of daily living (e.g. picking up an object from the floor) while using a WMRA. Greater visual abilities (visual acuity, contrast sensitivity, and depth perception), cognitive abilities (processing speed, working memory, and spatial ability) and physical abilities (dexterity) resulted in participants completing tasks more quickly and with fewer total moves. We propose that interfaces should adapt to compensate for deficits in these capabilities to support a wider range of users. A variety of compensations should be developed and tested in order to identify the most effective techniques. For instance, object segmentation, a computer vision technique that separates objects and background in a visual scene, may compensate for deficits in contrast sensitivity, depth perception, processing speed, and working memories. However, contrast sensitivity may be better compensated for by use of a simple yellow filter on the screen, mimicking yellow lenses in glasses used for the same purpose. Similarly, depth perception limitations may be better overcome through the use of multiple camera views or by automating the pick-up and release mechanisms of the gripper. Thus there may be one compensation that facilitates WMRA use for a multitude of decrements or each factor may be better served by a specific separate compensation. In incorporating the effective compensations into the interface software, there should also be a capability of identifying which specific compensations should be activated for an individual user. For this we propose testing for these important individual differences should be included within the software. Virtual or online testing already exist for many of the identified factors and can be modified to fit our purpose. This is especially the case if gamification principles are applied as testing will engage user interest. In this way, the software can adjust compensations as a user’s visual, cognitive, and physical abilities change over time. Future research ventures will include identifying the most beneficial compensation for each identified individual difference and developing virtual gamified measures for those individual differences.

Cyber Human Interaction

“…While the question of how an assistive robot will compensate for the human factors developed by Paperno et al (2016) [85] is still a subject of ongoing research it is possible to suggest some methods using current research into machine learning, computer vision, and psychological research [102]. It is also possible to consider adaptable ways in which the system can work along with the user to foster autonomy and competence.…”

“…Additionally, a computer vision technique called object segmentation and detection may also be used to provide a user with a simplified display or magnify the field of view [102]. This technique could be used to implement color coding to maximize foreground to background contrast (see Jang, Lee, & Kim, 2016 [106] for an example of this technique).…”

From Single 2D Depth Image to Gripper 6D Pose Estimation: A Fast and Robust Algorithm for Grabbing Objects in Cluttered Scenes

“…This matter forces some of the methods to make simplifying assumptions about an object's situation, e.g., [17] is only validated for objects standing on a planar surface. Finding a feasible grasp configuration subject to the given task and user constraints is required for a group of applications.As discussed by [25][26][27], suggesting desired grasp configurations, in assitive human-robot interaction, results in increasing the users' engagement and easing the manipulator trajectory adaptation.…”