Within this work we present AMiCUS, a Human-Robot Interface that enables tetraplegics to control a multi-degree of freedom robot arm in real-time using solely head motion, empowering them to perform simple manipulation tasks independently. The article describes the hardware, software and signal processing of AMiCUS and presents the results of a volunteer study with 13 able-bodied subjects and 6 tetraplegics with severe head motion limitations. As part of the study, the subjects performed two different pick-and-place tasks. The usability was assessed with a questionnaire. The overall performance and the main control elements were evaluated with objective measures such as completion rate and interaction time. The results show that the mapping of head motion onto robot motion is intuitive and the given feedback is useful, enabling smooth, precise and efficient robot control and resulting in high user-acceptance. Furthermore, it could be demonstrated that the robot did not move unintendedly, giving a positive prognosis for safety requirements in the framework of a certification of a product prototype. On top of that, AMiCUS enabled every subject to control the robot arm, independent of prior experience and degree of head motion limitation, making the system available for a wide range of motion impaired users.
The assistive robot system adaptive head motion control for user-friendly support (AMiCUS) has been developed to increase the autonomy of motion impaired people. The six degrees of freedom robot arm with gripper is controlled with head motion and head gestures only, so especially tetraplegics benefit from collaboration with AMiCUS. In this paper, a usability study with a total number of 30 subjects was conducted to validate the AMiCUS interaction technology and design. 24 able-bodied subjects of demographically diverse groups and 6 tetraplegics participated in this paper. All subjects performed different pick and place tasks by controlling AMiCUS. The evaluation of the interaction design was carried out subjectively with a questionnaire as well as objectively by measurement of time, completion rate, and number of trials for correct head gesture performance. The influence of several factors like age, sex, motion impairment, and previous experience on head motion-based human-robot interaction was analyzed. The interaction design has been proven successful in laboratory environment and assessed overall positive by the subjects. The results of the presented paper confirm the usability of the assistive robot AMiCUS. AMiCUS has the potential to benefit tetraplegics by improving their independence in activities of daily living and adapted workplaces.
This paper presents a lightweight, infrastructureless head-worn interface for robust and real-time robot control in Cartesian space using head- and eye-gaze. The interface comes at a total weight of just 162 g. It combines a state-of-the-art visual simultaneous localization and mapping algorithm (ORB-SLAM 2) for RGB-D cameras with a Magnetic Angular rate Gravity (MARG)-sensor filter. The data fusion process is designed to dynamically switch between magnetic, inertial and visual heading sources to enable robust orientation estimation under various disturbances, e.g., magnetic disturbances or degraded visual sensor data. The interface furthermore delivers accurate eye- and head-gaze vectors to enable precise robot end effector (EFF) positioning and employs a head motion mapping technique to effectively control the robots end effector orientation. An experimental proof of concept demonstrates that the proposed interface and its data fusion process generate reliable and robust pose estimation. The three-dimensional head- and eye-gaze position estimation pipeline delivers a mean Euclidean error of 19.0±15.7 mm for head-gaze and 27.4±21.8 mm for eye-gaze at a distance of 0.3–1.1 m to the user. This indicates that the proposed interface offers a precise control mechanism for hands-free and full six degree of freedom (DoF) robot teleoperation in Cartesian space by head- or eye-gaze and head motion.
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