A robotic workstation for stroke rehabilitation of the upper extremity using FES

Two decades of research on robots and upper extremity rehabilitation has resulted in recommendations from systematic reviews and guidelines on their use in stroke. Robotics are often cited for their ability to encourage mass practice as a means to enhance recovery of movement. Yet, stroke recovery is a complex process occurring across many aspects of neurologic function beyond movement. As newer devices are developed and enhanced assessments are integrated into treatment protocols, the potential of robotics to advance rehabilitation will continue to grow.

Download Full-text

Automated movement assessment in stroke rehabilitation

10.1101/2021.06.25.449936 ◽

2021 ◽

Author(s):

Tamim Ahmed ◽

Kowshik Thopalli ◽

Thanassis Rikakis ◽

Pavan Turaga ◽

Aisling Kelliher ◽

...

Keyword(s):

Upper Extremity ◽

Hierarchical Model ◽

Stroke Rehabilitation ◽

Expert Knowledge ◽

Low Cost ◽

Data Driven ◽

Task Completion ◽

Convolutional Network ◽

Rgb Images ◽

High Level

We are developing a system for long-term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high-level constraints relating to activity structure (i.e. type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high-level priors to data-driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data-driven techniques. We use a transformer-based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complementary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce robust segmentation and task assessment results on noisy, variable, and limited data, which is characteristic of low-cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification, and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e. lower extremity training for neurological accidents).

Download Full-text

Optimization of Upper Extremity Rehabilitation by Combining Telerehabilitation With an Exergame in People With Chronic Stroke: Protocol for a Mixed Methods Study

JMIR Research Protocols ◽

10.2196/14629 ◽

2020 ◽

Vol 9 (5) ◽

pp. e14629

Author(s):

Dorra Rakia Allegue ◽

Dahlia Kairy ◽

Johanne Higgins ◽

Philippe Archambault ◽

Francois Michaud ◽

...

Keyword(s):

Quality Of Life ◽

Decision Making ◽

Mixed Methods ◽

Upper Extremity ◽

Shared Decision Making ◽

Stroke Rehabilitation ◽

Mixed Methods Study ◽

Decision Making Process ◽

Shared Decision

Background Exergames have the potential to provide an accessible, remote approach for poststroke upper extremity (UE) rehabilitation. However, the use of exergames without any follow-up by a health professional could lead to compensatory movements during the exercises, inadequate choice of difficulty level, exercises not being completed, and lack of motivation to pursue exercise programs, thereby decreasing their benefits. Combining telerehabilitation with exergames could allow continuous adjustment of the exercises and monitoring of the participant’s completion and adherence. At present, there is limited evidence regarding the feasibility or efficacy of combining telerehabilitation and exergames for stroke rehabilitation. Objective This study aims to (1) determine the preliminary efficacy of using telerehabilitation combined with exergames on UE motor recovery, function, quality of life, and motivation in participants with chronic stroke, compared with conventional therapy (the graded repetitive arm supplementary program; GRASP); (2) examine the feasibility of using the technology with participants diagnosed with stroke at home; and (3) identify the obstacles and facilitators for its use by participants diagnosed with stroke and stroke therapists and understand the shared decision-making process. Methods A mixed methods study protocol is proposed, including a randomized, blinded feasibility trial with an embedded multiple case study. The intervention consists of the provision of a remote rehabilitation program, during which participants will use the Jintronix exergame for UE training and the Reacts Application to conduct videoconferenced sessions with the therapists (physical or occupational therapists). We plan to recruit 52 participants diagnosed with stroke, randomly assigned to a control group (n=26; 2-month on-paper home exercise program: the GRASP with no supervision) and an experimental group (n=26; 2-month home program using the technology). The primary outcome is the Fugl-Meyer UE Assessment, a performance-based measure of UE impairment. The secondary outcomes are self-reported questionnaires and include the Motor Activity Log-28 (quality and frequency of use of the UE), Stroke Impact Scale-16 (the quality of life), and Treatment Self-Regulation Questionnaire (motivation). Feasibility data include process, resources, management, and scientific outcomes. Qualitative data will be collected by interviews with both participants and therapists. Results At present, data collection was ongoing with one participant who had completed the exergame- telerehabilitation based intervention. We expect to collect preliminary efficacy data of this technology on the functional and motor recovery of the UE, following a stroke; collect feasibility data with users at home (adherence, safety, and technical difficulties); and identify the obstacles and facilitators for the technology use and understand the shared decision-making process. Conclusions This paper describes the protocol underlying the study of a telerehabilitation-exergame technology to contribute to understanding its feasibility and preliminary efficacy for UE stroke rehabilitation. Trial Registration ClinicalTrials.gov NCT03759106; http://clinicaltrials.gov/show/NCT03759106. International Registered Report Identifier (IRRID) DERR1-10.2196/14629

Download Full-text

Abstract WP153: Establishing Kinimatic Age-referenced Normative Values of the Upper Extremity Using the InMotion 2 Robot

Stroke ◽

10.1161/str.47.suppl_1.wp153 ◽

2016 ◽

Vol 47 (suppl_1) ◽

Author(s):

Carly A Creelman ◽

Grace J Kim ◽

Michael Taub ◽

Michael W O'Dell

Keyword(s):

Upper Extremity ◽

Stroke Rehabilitation ◽

Age Groups ◽

Kinematic Parameter ◽

Normative Values ◽

Healthy Population ◽

Dominant Hand ◽

Kinematic Parameters ◽

Kinematic Assessment ◽

Group 5

Introduction: The use of technology in stroke rehabilitation is growing rapidly. Upper extremity robotic devices provide both therapeutic intervention as well as objective kinematic assessment to evaluate arm ability of individuals with stroke. The InMotion 2® is a shoulder/elbow robot widely used in the clinic and within stroke rehabilitation research. It has the capability to provide kinematic assessment of the arm, however there are no age-referenced normative values available for comparison to a healthy population. The aim of this study was to establish normative kinematic values for the InMotion 2 robot. Hypothesis: Not Applicable. Methods: Forty healthy individuals with no history of stroke or other neurological conditions with full passive and active range of motion in both upper extremities were recruited from the community. Subjects were recruited based on age (40-49, 50-59, 60-69, 70-80), 10 subjects per group (5 males and 5 females). Subjects were assessed on circle and clock drawing tasks with their dominant and non-dominant arm over three trials. The kinematic parameters measured included smoothness, joint independence, hold deviation, and displacement. The mean (M) and standard deviation (SD) of dominant hand test 1 and 2 were averaged together for combined scores across age groups. Results: The following age referenced normative values were determined for each kinematic parameter: smoothness (m/s), mean and SD for 40-49 age group (.585; .027), 50-59 (.566; .033), 60-69 (.540; .048), and 70-80 (.561; .033); joint independence: 40-49 (.861; .019), 50-59 (.858; .0394), 60-69 (.839; .030), and 70-80 (.853; 024); hold deviation (meters), 40-49 (.017; .005), 50-59 (.019; .004), 60-69 (.019; .006), and 70-80 (.022; .004); displacement (meters), 40-49 (.132; .001), 50-59 (.132; .001), 60-69 (.132; .001), and 70-80 (.131; .0004). Conclusion: The analysis demonstrated that age, sex, and hand dominance did not have a significant effect on normative kinematic outcomes, however age referenced normative values establish baseline and ceiling levels which provide more meaning when interpreting scores for individuals with stroke. Further research investigating the reliability of the kinematic parameters is currently underway.

Download Full-text

Simultaneous Heterogeneous Sensor Localization, Joint Tracking, and Upper Extremity Modeling for Stroke Rehabilitation

IEEE Systems Journal ◽

10.1109/jsyst.2020.2963842 ◽

2020 ◽

Vol 14 (3) ◽

pp. 3570-3581

Author(s):

Jiun-Fu Chen ◽

Chieh-Chih Wang ◽

Eric Hsiao-Kuang Wu ◽

Cheng-Fu Chou

Keyword(s):

Upper Extremity ◽

Stroke Rehabilitation ◽

Sensor Localization ◽

Joint Tracking

Download Full-text

A Model of the Upper Extremity Using FES for Stroke Rehabilitation

Journal of Biomechanical Engineering ◽

10.1115/1.3005332 ◽

2009 ◽

Vol 131 (3) ◽

Cited By ~ 46

Author(s):

Chris T. Freeman ◽

Ann-Marie Hughes ◽

Jane H. Burridge ◽

Paul H. Chappell ◽

Paul L. Lewin ◽

...

Keyword(s):

Clinical Trials ◽

Electrical Stimulation ◽

Upper Extremity ◽

Stroke Rehabilitation ◽

Horizontal Plane ◽

Stroke Patients ◽

Unknown Parameters ◽

Triceps Muscle ◽

Tracking Tasks ◽

Short Period

A model of the upper extremity is developed in which the forearm is constrained to lie in a horizontal plane and electrical stimulation is applied to the triceps muscle. Identification procedures are described to estimate the unknown parameters using tests that can be performed in a short period of time. Examples of identified parameters obtained experimentally are presented for both stroke patients and unimpaired subjects. A discussion concerning the identification’s repeatability, together with results confirming the accuracy of the overall representation, is given. The model has been used during clinical trials in which electrical stimulation is applied to the triceps muscle of a number of stroke patients for the purpose of improving both their performance at reaching tasks and their level of voluntary control over their impaired arm. Its purpose in this context is threefold: Firstly, changes occurring in the levels of stiffness and spasticity in each subject’s arm can be monitored by comparing frictional components of models identified at different times during treatment. Secondly, the model is used to calculate the moments applied during tracking tasks that are due to a patient’s voluntary effort, and it therefore constitutes a useful tool with which to analyze their performance. Thirdly, the model is used to derive the advanced controllers that govern the level of stimulation applied to subjects over the course of the treatment. Details are provided to show how the model is applied in each case, and sample results are shown.

Download Full-text