Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

Encapsulating and representing the knowledge on the evaluation of an engineering system

This paper proposes a cross-disciplinary methodology for a fundamental question in product development: How can the innovation patterns during the evolution of an engineering system (ES) be encapsulated, so that it can later be mined through data mining analysis methods? Reverse engineering answers the question of which components a developed engineering system consists of, and how the components interact to make the working product. TRIZ answers the question of which problem-solving principles can be, or have been employed in developing that system, in comparison to its earlier versions, or with respect to similar systems. While these two methodologies have been very popular, to the best of our knowledge, there does not yet exist a methodology that reverseengineers and encapsulates and represents the information regarding the complete product development process in abstract terms. This paper suggests such a methodology, that consists of mathematical formalism, graph visualization, and database representation. The proposed approach is demonstrated by analyzing the design and development process for a prototype wrist-rehabilitation robot

Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation

The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver\'s policy. End-to-end imitation learning is a popular method for computing self-driving car policies. The standard approach relies on collecting pairs of inputs (camera images) and outputs (steering angle, etc.) from an expert policy and fitting a deep neural network to this data to learn the driving policy. Although this approach had some successful demonstrations in the past, learning a good policy might require a lot of samples from the expert driver, which might be resource-consuming. In this work, we develop a novel framework based on the Safe Dateset Aggregation (safe DAgger) approach, where the current learned policy is automatically segmented into different trajectory classes, and the algorithm identifies trajectory segments or classes with the weak performance at each step. Once the trajectory segments with weak performance identified, the sampling algorithm focuses on calling the expert policy only on these segments, which improves the convergence rate. The presented simulation results show that the proposed approach can yield significantly better performance compared to the standard Safe DAgger algorithm while using the same amount of samples from the expert.Comment: 6 pages, 6 figures, IROS2019 conferenc

Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities (BBA tabanlı üst uzuv rehabilitasyon sisteminin sonsal olasılık değerleri kullanılarak kontrolü)

In this paper, an electroencephalogram (EEG) based Brain-Computer Interface (BCI) is integrated with a robotic system designed to target rehabilitation therapies of stroke patients such that patients can control the rehabilitation robot by imagining movements of their right arm. In particular, the power density of frequency bands are used as features from the EEG signals recorded during the experiments and they are classified by Linear Discriminant Analysis (LDA). As one of the novel contributions of this study, the posterior probabilities extracted from the classifier are directly used as the continuous-valued outputs, instead of the discrete classification output commonly used by BCI systems, to control the speed of the therapeutic movements performed by the robotic system. Adjusting the exercise speed of patients online, as proposed in this study, according to the instantaneous levels of motor imagery during the movement, has the potential to increase efficacy of robot assisted therapies by ensuring active involvement of patients. The proposed BCI-based robotic rehabilitation system has been successfully implemented on physical setups in our laboratory and sample experimental data are presented

Pre-movement contralateral EEG low beta power is modulated with motor adaptation learning

Various neuroimaging studies aim to understand the complex nature of human motor behavior. There exists a variety of experimental approaches to study neurophysiological correlates of performance during different motor tasks. As distinct from studies based on visuomotor learning, we investigate changes in electroencephalographic (EEG) activity during an actual physical motor adaptation learning experiment. Based on statistical analysis of EEG signals collected during a force-field adaptation task performed with the dominant hand, we observe a modulation of pre-movement upper alpha (10-12 Hz) and lower beta (13-16 Hz) powers over the contralateral region. This modulation is observed to be stronger in lower beta range and, through a regression analysis, is shown to be related with motor adaptation performance on a subject-specific level

Online generation of velocity fields for passive contour following

A new approach to online generation of velocity fields for parametric curves is presented for implementation in passive velocity field controllers (PVFC) that enable human-in-the-loop contour following tasks. In particular, a feedback stabilized closest point tracking algorithm is utilized for real-time determination of the contour error and online construction of the velocity field. The algorithm augments the system dynamics with a new state, and implements a uniformly asymptotically stable controller to update this new state to continual track the closest point to the robot end-effector. Thanks to its feedback-stabilized core, the algorithm is immune to initialization errors, and robust against drift and numerical noise. Furthermore, requiring simple evaluations of the curve and its unit tangents, the approach is computationally efficient. Applicability and effectiveness of the approach to implement passive contour following tasks have been demonstrated through simulations and experiments with a two degrees-of-freedom haptic interface

AssistOn-Mobile: A series elastic holonomic mobile platform for upper extremity rehabilitation

We present the design, control, and human–machine interface of a series elastic holonomic mobile platform, AssistOn-Mobile, aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. The proposed mobile platform is a low-cost, portable, easy-to-use rehabilitation device targeted for home use. In particular, AssistOn-Mobile consists of a holonomic mobile platform with four actuated Mecanum wheels and a compliant, low-cost, multi-degrees-of-freedom series elastic element acting as its force sensing unit. Thanks to its series elastic actuation, AssistOn-Mobile is highly backdriveable and can provide assistance/resistance to patients, while performing omni-directional movements on plane. AssistOn-Mobile also features Passive Velocity Field Control (PVFC) to deliver human-in-the-loop contour tracking rehabilitation exercises. PVFC allows patients to complete the contour-tracking tasks at their preferred pace, while providing the proper amount of assistance as determined by the therapists. PVFC not only minimizes the contour error but also does so by rendering the closed-loop system passive with respect to externally applied forces; hence, ensures the coupled stability of the human-robot system. We evaluate the feasibility and effectiveness of AssistOn-Mobile with PVFC for rehabilitation and present experimental data collected during human subject experiments under three case studies. In particular, we utilize AssistOn-Mobile with PVFC (a) to administer contour following tasks where the pace of the tasks is left to the control of the patients, so that the patients can assume a natural and comfortable speed for the tasks, (b) to limit compensatory movements of the patients by integrating a RGB-D sensor to the system to continually monitor the movements of the patients and to modulate the task speeds to provide online feedback to the patients, and (c) to integrate a Brain–Computer Interface such that the brain activity of the patients is mapped to the robot speed along the contour following tasks, rendering an assist-as-needed protocol for the patients with severe disabilities. The feasibility studies indicate that AssistOn-Mobile holds promise in improving the accuracy and effectiveness of repetitive movement therapies, while also providing quantitative measures of patient progress

Ön kol-bilek rehabilitasyon sisteminin çok-taraflı kontrolü

Bu makale, paralel kinematik yapıda bir dıs¸ iskeletin ön kol ve bilek rehabilitasyonuna, çok yanlı ve sanal gerc¸eklik destekli olarak, uygulanması ele alınmıştır. Üretilen dış iskeletin kinematik ve dinamik özellikleri deneysel olarak belirlendikten sonra, cihaz, ön kol ve bilek için çok yanlı, paylaşımlı fizik tedavi uygulayabilecek şekilde kontrol edilmiştir. Sisteme, çift kullanıcılı, kuvvet geri-beslemeli teleoperasyon kontrol mimarisi sanal gerçeklik destekli ve hakimiyet paylaşımlı olarak uygulanmıştır. Rehabilitasyon uygulamalarında daha önce kullanılmamış olan bu kontrol mimarisi sayesinde hasta, terapist ile işbirliği halinde, sanal bir görevi kuvvet geribeslemeli olarak yerine getirebilmektedir. Ayrıca kontrol otoritesi, bir hakimiyet katsayısı yardımıyla etmenler arasında bölüştürülebilmektedir. Bu sayede hasta ile terapist arasında çeşitli terapi ve değerlendirme yöntemleri gerçekleştirilebilmektedir

Design of a reconfigurable ankle rehabilitation robot and its use for the estimation of the ankle impedance

This paper presents the design, analysis, and a clinical application of a reconfigurable, parallel mechanism based, force feedback exoskeleton for the human ankle. The device can either be employed as a balance/proprioception trainer or configured to accommodate range of motion (RoM)/strengthening exercises. The exoskeleton can be utilized as a clinical measurement tool to estimate dynamic parameters of the ankle and to assess ankle joint properties in physiological and pathological conditions. Kinematic analysis and control of the device are detailed and a protocol for utilization of the exoskeleton to determine ankle impedance is discussed. The prototype of the device is also presented