Robot-assisted intermanual transfer of handwriting skills

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Training modalities in robot-mediated upper limb rehabilitation in stroke : A framework for classification based on a systematic review

© 2014 Basteris et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The work described in this manuscript was partially funded by the European project ‘SCRIPT’ Grant agreement no: 288698 (http://scriptproject.eu). SN has been hosted at University of Hertfordshire in a short-term scientific mission funded by the COST Action TD1006 European Network on Robotics for NeuroRehabilitationRobot-mediated post-stroke therapy for the upper-extremity dates back to the 1990s. Since then, a number of robotic devices have become commercially available. There is clear evidence that robotic interventions improve upper limb motor scores and strength, but these improvements are often not transferred to performance of activities of daily living. We wish to better understand why. Our systematic review of 74 papers focuses on the targeted stage of recovery, the part of the limb trained, the different modalities used, and the effectiveness of each. The review shows that most of the studies so far focus on training of the proximal arm for chronic stroke patients. About the training modalities, studies typically refer to active, active-assisted and passive interaction. Robot-therapy in active assisted mode was associated with consistent improvements in arm function. More specifically, the use of HRI features stressing active contribution by the patient, such as EMG-modulated forces or a pushing force in combination with spring-damper guidance, may be beneficial.Our work also highlights that current literature frequently lacks information regarding the mechanism about the physical human-robot interaction (HRI). It is often unclear how the different modalities are implemented by different research groups (using different robots and platforms). In order to have a better and more reliable evidence of usefulness for these technologies, it is recommended that the HRI is better described and documented so that work of various teams can be considered in the same group and categories, allowing to infer for more suitable approaches. We propose a framework for categorisation of HRI modalities and features that will allow comparing their therapeutic benefits.Peer reviewedFinal Published versio

Lag-lead based assessment and adaptation of exercise speed for stroke survivors

This document is the Accepted Manuscript version of the following article: Angelo Basteris, Sharon M. Mijenhuis, Jaap H. Buurke, Gerdienke B. Prange, and Farshid Amirabdolllahian, ‘Lag–lead based assessment and adaptation of exercise speed for stroke survivors’, Robotics and Autonomous Systems, Vol. 73: 144-154, November 2015. The final, published version is available online at doi: https://doi.org/10.1016/j.robot.2014.08.013.The SCRIPT project aims at delivering machine-mediated hand and wrist exercises to people with stroke in their homes. In this context, adapting the exercise to the individual needs potentially enhances recovery. We designed a system composed of a passive-actuated wearable device, a personal computer and an arm support. The system enables users to exercise their hand and wrist movements by playing interactive games which were developed as part of the project. Movements and their required speed are tailored on the individual's capabilities. During the exercise the system assesses whether the subject is in advance (leading) or in delay (lagging) with respect to a reference trajectory. This information provides input to an adaptive mechanism which changes the required movement speed in order to make the exercise neither too easy nor too challenging. In this paper, we show results of the adaptation process in a study involving seven persons with chronic stroke who completed a six weeks training in their homes. Based on the patterns observed in difficulty and lag-lead score, we defined five session types (challenging, challenging-then supporting, supporting, under-supporting and under-challenging). We show that the mechanism of adaptation has been effective in 195 of 248 (78.6%) sessions. Based on our results, we propose the lag-lead based assessment and adaptation as an auto-tuning tool for machine based exercise, with particular focus on rehabilitation robotics. Also, the classification of sessions among different types can be applied to other studies in order to better understanding the progression of therapy in order to maximize its outcome.Peer reviewe

Grasps recognition and evaluation of stroke patients for supporting rehabilitation therapy

Copyright © 2014 Beatriz Leon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Stroke survivors often suffer impairments on their wrist and hand. Robot-mediated rehabilitation techniques have been proposed as a way to enhance conventional therapy, based on intensive repeated movements. Amongst the set of activities of daily living, grasping is one of the most recurrent. Our aim is to incorporate the detection of grasps in the machine-mediated rehabilitation framework so that they can be incorporated into interactive therapeutic games. In this study, we developed and tested a method based on support vector machines for recognizing various grasp postures wearing a passive exoskeleton for hand and wrist rehabilitation after stroke. The experiment was conducted with ten healthy subjects and eight stroke patients performing the grasping gestures. The method was tested in terms of accuracy and robustness with respect to intersubjects' variability and differences between different grasps. Our results show reliable recognition while also indicating that the recognition accuracy can be used to assess the patients' ability to consistently repeat the gestures. Additionally, a grasp quality measure was proposed to measure the capabilities of the stroke patients to perform grasp postures in a similar way than healthy people. These two measures can be potentially used as complementary measures to other upper limb motion tests.Peer reviewedFinal Published versio

A deep learning saliency model for exploring viewers' dwell-time distributions over Areas Of Interest on webcam-based eye-tracking data

Visual saliency is a common computational method to detect attention-drawing regions in images, abiding by top-down and bottom-up processes of visual attention. Computer vision algorithms generate saliency maps, which often undergo a validation step in eye-tracking sessions with human participants in controlled labs. However, due to the covid-19 pandemic, experimental sessions have been difficult to roll out. Thus, new webcam-based tools, powered by the developments in machine learning, come into play to help track down onscreen eye movements. Claimed error rates of recent webcam eye trackers can be as low as 1.05°, comparable to sophisticated infrared-based eye-trackers, opening new paths to explore. Using webcams allows reaching a broader participant pool and collecting data over different experiments (e.g., free viewing or task-driven). In our work, we collect webcam eye-tracking data over a collection of images with 2-4 salient objects against a homogenous background. Objects within the images represent our AOIs (areas of interest). We have two main goals: a) Check how eye movements vary on AOIs across all spatial permutations of the same AOI in a given image; b) Extract correlations for a given image containing N 224 Perception 50(1S) objects between viewers’ eye movement dwell times over the N AOIs and the corresponding AOIs saliency maps. We will show relationships between viewers’ dwell time over each AOI throughout all factorial N spatial permutations and variance of AOIs’ salient pixels. Based on this relationship, eventually, object-oriented saliency models can be used to predict dwell-time distributions over AOIs for a given image

Designing motivational games for stroke rehabilitation

Motivation plays in important role in rehabilitation after stroke. Multi-modal games can provide an engaging and interactive platform to motivate people to actively participate in the therapy. Designing games for rehabilitation requires input from both multiple stakeholders such as the medical, bioengineering and game design fields. In order to bridge this gap, we implemented and tested games specifically designed for upper limb rehabilitation and observed their effects on players. This paper presents the design process for three rehabilitation games and their effects on motivation of a single stroke patient. These results indicate that engagement and enjoyment vary for the three different games while managing to achieve repetitive number of hand and wrist gestures in the background. In our summative evaluation planned to be conducted in patients' homes, 9 different games, including the three presented here, are provided to allow for providing a better range and a wider choice. Use-logs as well as the questionnaire trialed in this study will be used to assess preference and motivation, and to explore if current feedback will be repeated by a larger number of stroke patients and a wider range of impairments and preferences. These will further inform the process of game design for rehabilitation and personal well-being

Comparing Recognition Methods to Identify Different Types of Grasps for Hand Rehabilitation

Abstract—Grasping activities are extremely frequent in the set of activities of daily living. This causes severe impairments for stroke survivors, whose wrist and hand may suffer from a variety of symptoms such as spasticity, hypertone and muscular weakness. Intensive repeated movement performance is at the base of robot-therapy. Thus, patients may benefit, in terms of functional recovery, from the integration of grasp gestures in robot mediated exergaming. In this feasibility study, we developed and tested three methods for recognizing four different grasp postures performed while wearing an exoskeleton for hand and wrist rehabilitation after stroke. The three methods were based on the statistics of the produced postures, on neural networks and on support vector machines. The experiment was conducted with healthy subjects, with no previous injuries on the hand, during grasping of actual objects and then repeated using imaginary objects. We compared the three methods in terms of accuracy, robustness with respect to the size of the training sample, inter-subjects ’ variability, differences between different postures and evaluating the presence of real objects. Our results show that the support vector machine method is preferable in terms of both accuracy and robustness, even with a small training sample, with training times on the order of milliseconds. Keywords—grasp posture recognition; stroke rehabilitation; Sup-port Vector Machines; Neural Networks. I

Evaluating the neck joint position sense error with a standard computer and a webcam

Joint Position Sense Error (JPSE) is a measure of cervical spine proprioception, and a simple method for measuring the JPSE could help in monitoring and evaluating the outcomes of rehabilitation of people with neck pain.In this study we demonstrate preliminary results of a method for measuring JPSE that does not require the participant to wear any equipment. Based on free publicly available head tracking software, compatible with any webcam, we developed a webpage which instructs the participant in performing a self-administered version of the test. The aim of this proof-of-concept study was to demonstrate the viability of this system.We compared our absolute error values (3.68 +/- 1.2 degrees after extension, 3.46 +/- 1.66 degrees after flexion, 3.89 +/- 2.34 degrees after rotation to the left and 4.02 +/- 1.82 degrees after rotation to the right) to values from literature, finding that our results do not differ from those of 6 out of 11 studies (which used more complex and expensive setups).The results indicate that our system allows assessment of the JPSE with a standard computer. Being based on a website, the system has potential for telemedicine use. Further research is required to validate the system before it can be recommended for use in clinical practice. (C) 2016 Elsevier Ltd. All rights reserved