A long-term Human-Robot Proxemic study

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”A long-term Human-Robot Proxemic (HRP) study was performed using a newly developed Autonomous Proxemic System (APS) for a robot to measure and control the approach distances to the human participants. The main findings were that most HRP adaptation occurred in the first two interaction sessions, and for the remaining four weeks, approach distance preferences remained relatively steady, apart from some short periods of increased distances for some participants. There were indications that these were associated with episodes where the robot malfunctioned, so this raises the possibility of users trust in the robot affecting HRP distance. The study also found that approach distances for humans approaching the robot and the robot approaching the human were comparable, though there were indications that humans preferred to approach the robot more closely than they allowed the robot to approach them in a physically restricted area. Two participants left the study prematurely, stating they were bored with the repetitive experimental procedures. This highlights issues related to the often incompatible demands of keeping experimental controlled conditions vs. having realistic, engaging and varied HRI trial scenarios

Feature and Channel Selection Using Correlation Based Method for Hand Posture Classification in Multiple Arm Positions

In this work we propose a method based on correlation-based feature selection (CFS) to select features and channels for pattern recognition control of upper-limb prostheses. This method was applied on features extracted from myoelectric signals acquired from two able-bodied subjects and one individual with transradial amputation while contracting the muscles as to perform five functional hand postures in nine arm positions. The classification accuracy increased by using CFS for the able-bodied, while no statistical improvements has been highlighted for the amputee subject. The channels selected by this approach were mainly placed on the posterior side of the forearm which might reflect importance role of the extensor muscles over the flexor muscle when performing these hand postures. Further analysis with bigger dataset will be conducted to validate these preliminary findings