Multi-motion robots control based on bioelectric signals from single-channel dry electrode

Abstract

This article presents a multi-motion control system to help severe disabled people operate an auxiliary appliance using neck-up bioelectric signals measured by a single-channel dry electrode on the forehead. The single-channel dry-electrode multi-motion control system exhibits several practical advantages over its conventional counterparts that use multi-channel wet-electrodes; among the challenges is an effective technique to extract bioelectric features for reliable implementation of multi degrees-of-freedom motion control. Using both time and frequency characteristics of the single-channel dry-electrode measurements, motion commands are derived from multiple feature signals associated with concentration demands and different eye-blink actions for use in a two-level control strategy that has been developed to control predefined multi degrees-of-freedom motion trajectories. Test paradigms were designed to pre-calibrate the users' feature signals to statistically account for individual variances. Experimental trials were then carried out on able-bodied and disabled volunteers to validate the universal applicability of the algorithms. The classification success rates for two different eye-blink feature signals were approximately 95% with an average time of 2.4 s for executing a concentration feature signal. The single-channel dry-electrode-based technique has been validated on a 6-degree-of-freedom robot arm demonstrating its significant potentials to help patients suffering severe motor dysfunctions operate a multi-motion auxiliary appliance in everyday living where the ease of use is a priority.

Hybrid Control of a Vision-Guided Robot Arm by EOG, EMG, EEG Biosignals and Head Movement Acquired via a Consumer-Grade Wearable Device

“…We aimed at delivering a compromise between ease of control and flexibility for assistive applications: according to the proposed hybrid approach, the user retains unconstrained control in steering the robot toward the target object, and engaging autonomous guidance afterwards relieves the user from the burden of fine adjustment of the joints to attain intended postures, maintaining the goal focus. We demonstrated this approach using a desktop robotic arm whose 5+1 degree-of-freedom kinematics are analogous to existing assistive robotic manipulators, aiding clinical translation of the results [3], [5], [7], [38], [50], [65]. This experiment relied on elementary object detection via hue and geometric features, but the approach is viable with arbitrary vision systems, e.g., capable of recognizing objects belonging to specific classes through deep learning techniques; furthermore, it is, in principle, applicable to both image-based and positionbased vision servoing [5], [44], [46], [66]- [68].…”

“…Yet, many practical applications of assistive robotics would benefit from control of complex mechanics based on a heavily constrained set of bio-signals acquired non-invasively, while allowing the user to focus on their goal rather than on kinematics. This need has driven recourse to either degreesof-freedom reduction approaches, namely, linking axes via pre-determined relationships or postures, or ''sparse'' control schemes, wherein the user selects and activates preestablished motor sequences via ''high-level'' commands with or without the support of a graphical user interface [25], [33], [38]- [42]. Such approach has proven particularly effective when combined with computer vision systems implementing object recognition and partially-autonomous guidance, which can drastically simplify and accelerate the performance of object manipulation tasks using robotic arms and even humanoid robots [5], [42]- [46].…”

Tongue–Computer Interface Prototype Design Based on T-Type Magnet Localization for Smart Environment Control

“…To facilitate interactions between them and smart infrastructure with more efficiency and reliability, intuitive interface using control signals according to these individuals' intention have begun to gain increasing attention in recent years. Much of the recent effort is focused on the remaining body functions of these individuals for interface establishment, including movements of tongue [4], eyeball [5,6], head [7], facial muscle activity [8], and even brain activity signals [9][10][11][12].…”