Unsupervised robot-assisted rehabilitation after stroke: feasibility, effect on therapy dose, and user experience.

BACKGROUND: Unsupervised robot-assisted rehabilitation is a promising approach to increase the dose of therapy after stroke, which may help promote sensorimotor recovery without requiring significant additional resources and manpower. However, the unsupervised use of robotic technologies is not yet a standard, as rehabilitation robots often show low usability or are considered unsafe to be used by patients independently. In this paper we explore the feasibility of unsupervised therapy with an upper limb rehabilitation robot in a clinical setting, evaluate the effect on the overall therapy dose, and assess user experience during unsupervised use of the robot and its usability. METHODS: Subacute stroke patients underwent a four-week protocol composed of daily 45 min-sessions of robot-assisted therapy. The first week consisted of supervised therapy, where a therapist explained how to interact with the device. The second week was minimally supervised, i.e., the therapist was present but intervened only if needed. After this phase, if participants learnt how to use the device, they proceeded to two weeks of fully unsupervised training. Feasibility, dose of robot-assisted therapy achieved during unsupervised use, user experience, and usability of the device were evaluated. Questionnaires to evaluate usability and user experience were performed after the minimally supervised week and at the end of the study, to evaluate the impact of therapists' absence. RESULTS: Unsupervised robot-assisted therapy was found to be feasible, as 12 out of the 13 recruited participants could progress to unsupervised training. During the two weeks of unsupervised therapy participants on average performed an additional 360 min of robot-assisted rehabilitation. Participants were satisfied with the device usability (mean System Usability Scale scores > 79), and no adverse events or device deficiencies occurred. CONCLUSIONS: We demonstrated that unsupervised robot-assisted therapy in a clinical setting with an actuated device for the upper limb was feasible and can lead to a meaningful increase in therapy dose. These results support the application of unsupervised robot-assisted therapy as a complement to usual care in clinical settings and pave the way to its application in home settings. TRIAL REGISTRATION: Registered on 13.05.2020 on clinicaltrials.gov (NCT04388891).

Mixed methods usability evaluation of an assistive wearable robotic hand orthosis for people with spinal cord injury.

BACKGROUND: Robotic hand orthoses (RHO) aim to provide grasp assistance for people with sensorimotor hand impairment during daily tasks. Many of such devices have been shown to bring a functional benefit to the user. However, assessing functional benefit is not sufficient to evaluate the usability of such technologies for daily life application. A comprehensive and structured evaluation of device usability not only focusing on effectiveness but also efficiency and satisfaction is required, yet often falls short in existing literature. Mixed methods evaluations, i.e., assessing a combination of quantitative and qualitative measures, allow to obtain a more holistic picture of all relevant aspects of device usability. Considering these aspects already in early development stages allows to identify design issues and generate generalizable benchmarks for future developments. METHODS: We evaluated the short-term usability of the RELab tenoexo, a RHO for hand function assistance, in 15 users with tetraplegia after a spinal cord injury through a comprehensive mixed methods approach. We collected quantitative data using the Action Research Arm Test (ARAT), the System Usability Scale (SUS), and timed tasks such as the donning process. In addition, qualitative data were collected through semi-structured interviews and user observations, and analyzed with a thematic analysis to enhance the usability evaluation. All insights were attributed and discussed in relation to specifically defined usability attributes such as comfort, ease of use, functional benefit, and safety. RESULTS: The RELab tenoexo provided an immediate functional benefit to the users, resulting in a mean improvement of the ARAT score by 5.8 points and peaking at 15 points improvement for one user (clinically important difference: 5.7 points). The mean SUS rating of 60.6 represents an adequate usability, however, indicating that especially the RHO donning (average task time = 295 s) was perceived as too long and cumbersome. The participants were generally very satisfied with the ergonomics (size, dimensions, fit) of the RHO. Enhancing the ease of use, specifically in donning, increasing the provided grasping force, as well as the availability of tailoring options and customization were identified as main improvement areas to promote RHO usability. CONCLUSION: The short-term usability of the RELab tenoexo was thoroughly evaluated with a mixed methods approach, which generated valuable data to improve the RHO in future iterations. In addition, learnings that might be transferable to the evaluation and design of other RHO were generated, which have the potential to increase the daily life applicability and acceptance of similar technologies.

Progressive Transition From Supervised to Unsupervised Robot-Assisted Therapy After Stroke: Protocol for a Single-Group, Interventional Feasibility Study.

BACKGROUND: Increasing the dose of therapy delivered to patients with stroke may improve functional outcomes and quality of life. Unsupervised technology-assisted rehabilitation is a promising way to increase the dose of therapy without dramatically increasing the burden on the health care system. Despite the many existing technologies for unsupervised rehabilitation, active rehabilitation robots have rarely been tested in a fully unsupervised way. Furthermore, the outcomes of unsupervised technology-assisted therapy (eg, feasibility, acceptance, and increase in therapy dose) vary widely. This might be due to the use of different technologies as well as to the broad range of methods applied to teach the patients how to independently train with a technology. OBJECTIVE: This paper describes the study design of a clinical study investigating the feasibility of unsupervised therapy with an active robot and of a systematic approach for the progressive transition from supervised to unsupervised use of a rehabilitation technology in a clinical setting. The effect of unsupervised therapy on achievable therapy dose, user experience in this therapy setting, and the usability of the rehabilitation technology are also evaluated. METHODS: Participants of the clinical study are inpatients of a rehabilitation clinic with subacute stroke undergoing a 4-week intervention where they train with a hand rehabilitation robot. The first week of the intervention is supervised by a therapist, who teaches participants how to interact and train with the device. The second week consists of minimally supervised therapy, where the therapist is present but intervenes only if needed as participants exercise with the device. If the participants properly learn how to train with the device, they proceed to the unsupervised phase and train without any supervision during the third and fourth weeks. Throughout the duration of the study, data on feasibility and therapy dose (ie, duration and repetitions) are collected. Usability and user experience are evaluated at the end of the second (ie, minimally supervised) and fourth (ie, unsupervised) weeks, allowing us to investigate the effect of therapist absence. RESULTS: As of April 2023, 13 patients were recruited and completed the protocol, with no reported adverse events. CONCLUSIONS: This study will inform on the feasibility of fully unsupervised rehabilitation with an active rehabilitation robot in a clinical setting and its effect on therapy dose. Furthermore, if successful, the proposed systematic approach for a progressive transition from supervised to unsupervised technology-assisted rehabilitation could serve as a benchmark to allow for easier comparisons between different technologies. This approach could also be extended to the application of such technologies in the home environment, as the supervised and minimally supervised sessions could be performed in the clinic, followed by unsupervised therapy at home after discharge. TRIAL REGISTRATION: ClinicalTrials.gov NCT04388891; https://clinicaltrials.gov/study/NCT04388891. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48485.

An online method to monitor hand muscle tone during robot-assisted rehabilitation.

Introduction: Robot-assisted neurorehabilitation is becoming an established method to complement conventional therapy after stroke and provide intensive therapy regimes in unsupervised settings (e.g., home rehabilitation). Intensive therapies may temporarily contribute to increasing muscle tone and spasticity, especially in stroke patients presenting tone alterations. If sustained without supervision, such an increase in muscle tone could have negative effects (e.g., functional disability, pain). We propose an online perturbation-based method that monitors finger muscle tone during unsupervised robot-assisted hand therapy exercises. Methods: We used the ReHandyBot, a novel 2 degrees of freedom (DOF) haptic device to perform robot-assisted therapy exercises training hand grasping (i.e., flexion-extension of the fingers) and forearm pronosupination. The tone estimation method consisted of fast (150 ms) and slow (250 ms) 20 mm ramp-and-hold perturbations on the grasping DOF, which were applied during the exercises to stretch the finger flexors. The perturbation-induced peak force at the finger pads was used to compute tone. In this work, we evaluated the method performance in a stiffness identification experiment with springs (0.97 and 1.57 N/mm), which simulated the stiffness of a human hand, and in a pilot study with subjects with increased muscle tone after stroke and unimpaired, which performed one active sensorimotor exercise embedding the tone monitoring method. Results: The method accurately estimates forces with root mean square percentage errors of 3.8% and 11.3% for the soft and stiff spring, respectively. In the pilot study, six chronic ischemic stroke patients [141.8 (56.7) months after stroke, 64.3 (9.5) years old, expressed as mean (std)] and ten unimpaired subjects [59.9 (6.1) years old] were tested without adverse events. The average reaction force at the level of the fingertip during slow and fast perturbations in the exercise were respectively 10.7 (5.6) N and 13.7 (5.6) N for the patients and 5.8 (4.2) N and 6.8 (5.1) N for the unimpaired subjects. Discussion: The proposed method estimates reaction forces of physical springs accurately, and captures online increased reaction forces in persons with stroke compared to unimpaired subjects within unsupervised human-robot interactions. In the future, the identified range of muscle tone increase after stroke could be used to customize therapy for each subject and maintain safety during intensive robot-assisted rehabilitation.

Automatic and Personalized Adaptation of Therapy Parameters for Unsupervised Robot-Assisted Rehabilitation: a Pilot Evaluation.

Growing evidence shows that increasing the dose of upper limb therapy after stroke might improve functional outcomes and unsupervised robot-assisted therapy may be a solution to achieve such an increase without adding workload on therapists. However, most of existing robotic devices still need frequent supervision by trained personnel and are currently not designed or ready for unsupervised use. One reason for this is that most rehabilitation devices are not capable of delivering and adapting personalized therapy without external intervention. Here we present a set of clinically-inspired algorithms that automatically adapt therapy parameters in a personalized way and guide the course of robot-assisted therapy sessions. We implemented these algorithms on a robotic device for hand rehabilitation and tested them in a pilot study with 5 subacute stroke subjects over 10 robot-assisted therapy sessions, some of which unsupervised. Results show that our algorithms could adapt the therapy difficulty throughout the whole study without requiring external intervention, maintaining performance around a predefined 70% target value (mean performance for all the subjects over all the sessions: 64.5%). Moreover, the algorithms could guide patients through the therapy sessions, minimizing the number of actions that subjects had to learn and perform. These results open the door to the use of robotic devices in an unsupervised setting to increase therapy dose after stroke.

A Novel Mixed-Method Approach to Identify Needs and Requirements for Upper Limb Assistive Technology for Persons after Stroke.

Following stroke, a significant portion of individuals suffer from upper limb impairments and struggle with activities of daily living. Dedicated assistive technology (AT), such as robotic hand orthoses (RHO), can help facilitate upper limb usage and allow users to regain independence in their daily lives. Often, users' needs and requirements are neglected in AT design, thereby contributing to poor technology acceptance. In this work, we propose and apply a mixed-method focus group combining qualitative and quantitative components to gather user expectations in view of a user-centred redesign of a RHO. Three main themes emerged from a thematic analysis of two focus groups (n=5): Experience after stroke, desired design features, and reflections and realisations. Participants listed device features they would look for in AT and ranked them relative to what they deem important and necessary for a satisfactory device. Participants primarily looked for AT that is effective, intuitive and easy to use. These insights complement traditional technical design requirements for RHO by considering user desires, aspects unfortunately often neglected in the early design process. This work provides guidelines allowing for the optimization of AT design to better match the needs of persons after stroke and improve technology acceptance.

Design and Preliminary Evaluation of a Robot-assisted Assessment-driven Finger Proprioception Therapy.

Neurological injuries such as stroke often lead to motor and somatosensory impairments of the hand. Deficits in somatosensation, especially proprioception, result in difficulties performing activities of daily living involving fine motor tasks. However, it is challenging to accurately detect those impairments due to the limitations of clinical assessments. Hence therapies rarely focus on proprioception specifically, while such training could promote functional benefits. In this work we propose and preliminarily evaluate a robot-assisted, assessment-driven therapy of finger proprioception. We designed and implemented two therapeutic exercises, one targeting passive and the other active position sense. The difficulty level of the therapy exercises was adapted to each patient's proprioceptive impairment. We evaluated the exercises and their usability with 7 stroke participants and 8 clinicians in a 45-minutes protocol. We found that the exercises were feasible for stroke participants, as 5 individuals progressed in difficulty levels over multiple exercise repetitions, indicating adequacy of the adaptation algorithm. Moreover, usability was rated mostly as satisfactory by the patients (System Usability Scale = 73), and they also found the exercises interesting. Clinicians rated the exercises as difficult but clinically meaningful. Overall, these promising preliminary results pave the way for further development and validation of the proposed therapy approach.

Towards a Platform for Robot-Assisted Minimally-Supervised Therapy of Hand Function: Design and Pilot Usability Evaluation.

BACKGROUND: Robot-assisted therapy can increase therapy dose after stroke, which is often considered insufficient in clinical practice and after discharge, especially with respect to hand function. Thus far, there has been a focus on rather complex systems that require therapist supervision. To better exploit the potential of robot-assisted therapy, we propose a platform designed for minimal therapist supervision, and present the preliminary evaluation of its immediate usability, one of the main and frequently neglected challenges for real-world application. Such an approach could help increase therapy dose by allowing the training of multiple patients in parallel by a single therapist, as well as independent training in the clinic or at home. METHODS: We implemented design changes on a hand rehabilitation robot, considering aspects relevant to enabling minimally-supervised therapy, such as new physical/graphical user interfaces and two functional therapy exercises to train hand motor coordination, somatosensation and memory. Ten participants with chronic stroke assessed the usability of the platform and reported the perceived workload during a single therapy session with minimal supervision. The ability to independently use the platform was evaluated with a checklist. RESULTS: Participants were able to independently perform the therapy session after a short familiarization period, requiring assistance in only 13.46 (7.69-19.23)% of the tasks. They assigned good-to-excellent scores on the System Usability Scale to the user-interface and the exercises [85.00 (75.63-86.88) and 73.75 (63.13-83.75) out of 100, respectively]. Nine participants stated that they would use the platform frequently. Perceived workloads lay within desired workload bands. Object grasping with simultaneous control of forearm pronosupination and stiffness discrimination were identified as the most difficult tasks. DISCUSSION: Our findings demonstrate that a robot-assisted therapy device can be rendered safely and intuitively usable upon first exposure with minimal supervision through compliance with usability and perceived workload requirements. The preliminary usability evaluation identified usability challenges that should be solved to allow real-world minimally-supervised use. Such a platform could complement conventional therapy, allowing to provide increased dose with the available resources, and establish a continuum of care that progressively increases therapy lead of the patient from the clinic to the home.

Neurocognitive robot-assisted rehabilitation of hand function: a randomized control trial on motor recovery in subacute stroke.

BACKGROUND: Hand function is often impaired after stroke, strongly affecting the ability to perform daily activities. Upper limb robotic devices have been developed to complement rehabilitation therapy offered to persons who suffered a stroke, but they rarely focus on the training of hand sensorimotor function. The primary goal of this study was to evaluate whether robot-assisted therapy of hand function following a neurocognitive approach (i.e., combining motor training with somatosensory and cognitive tasks) produces an equivalent decrease in upper limb motor impairment compared to dose-matched conventional neurocognitive therapy, when embedded in the rehabilitation program of inpatients in the subacute stage after stroke. METHODS: A parallel-group, randomized controlled trial was conducted on subjects with subacute stroke receiving either conventional or robot-assisted neurocognitive hand therapy using a haptic device. Therapy was provided for 15, 45-min sessions over four weeks, nested within the standard therapy program. Primary outcome was the change from baseline in the upper extremity part of the Fugl-Meyer Assessment (FMA-UE) after the intervention, which was compared between groups using equivalence testing. Secondary outcome measures included upper limb motor, sensory and cognitive assessments, delivered therapy dose, as well as questionnaires on user technology acceptance. RESULTS: Thirty-three participants with stroke were enrolled. 14 subjects in the robot-assisted and 13 subjects in the conventional therapy group completed the study. At the end of intervention, week 8 and week 32, the robot-assisted/conventional therapy group improved by 7.14/6.85, 7.79/7.31, and 8.64/8.08 points on the FMA-UE, respectively, establishing that motor recovery in the robot-assisted group is non-inferior to that in the control group. CONCLUSIONS: Neurocognitive robot-assisted therapy of hand function allows for a non-inferior motor recovery compared to conventional dose-matched neurocognitive therapy when performed during inpatient rehabilitation in the subacute stage. This allows the early familiarization of subjects with stroke to the use of such technologies, as a first step towards minimal therapist supervision in the clinic, or directly at home after hospital discharge, to help increase the dose of hand therapy for persons with stroke. TRIAL REGISTRATION: EUDAMED database (CIV-13-02-009921), clinicaltrials.gov (NCT02096445). Registered 26 March 2014 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02096445.

Method for Muscle Tone Monitoring During Robot-Assisted Therapy of Hand Function: A Proof of Concept.

Robot-assisted rehabilitation of hand function is becoming an established approach to complement conventional therapy after stroke, particularly in view of its possible unsupervised use to promote an increase in therapy dose. Given their intensive therapy regime, robots may promote a temporary increase in hand muscle tone and spasticity, which may cause pain and negatively affect recovery. To integrate hand muscle tone monitoring into an assessment-driven robot-assisted therapy concept, an online assessment of muscle tone is proposed and incorporated into an exercise. The exercise was preliminarily tested in a pilot study with five chronic stroke survivors (non-spastic at rest) and five healthy participants to identify the range of potential physiological muscle tone change that can happen also in a non-spastic population during a single exercise session. In both groups, the muscle tone level during hand opening was higher in fast 20 mm ramp-and-hold perturbations (150 ms) compared to slow (250 ms) perturbations, and corresponded to a force change of approximately 4-5 N. Despite not being statistically significantly different, in the stroke group the force change (and the speed dependency) increased with exercise time. This information could serve as a basis to develop strategies to continuously adapt the difficulty and activity level required in robot-assisted rehabilitation and to monitor or even control the muscle tone evolution over time.

Algorithm for improving psychophysical threshold estimates by detecting sustained inattention in experiments using PEST.

Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., [Formula: see text] 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The novel algorithm complements existing approaches and, besides being applicable post hoc, could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates, especially for clinical settings.

Bio-Inspired Adaptive Control for Active Knee Exoprosthetics.

On the quest to bring function of prosthetic legs closer to their biological counterparts, the intuitive interplay of their control with the user's impedance modulation is key. We present two control features to enable more physiological and more user-adaptive control of prosthetic legs: a neuromusculoskeletal impedance model ( ) including a reflexive component, and a human model reference adaptive controller ( ), which can be combined with the former. In stance-phase simulations, the allowed to control a prosthetic leg with physiological knee joint angle and moment. When perturbations were applied, the reduced the resulting root mean square error (RMSE) between simulated and physiological reference angle by 96%. In a pilot experiment with two unimpaired and one amputee subject, gait with the deviated more from a physiological reference than with a conventional visco-elastic impedance controller. Subjects, however, preferred the . When adding the to either of the two impedance controllers, the RMSE between the actual and the physiological reference angle was reduced by up to 54%. Subjects confirmed this finding and reported an easier stance-to-swing transition. Simulation and pilot experiment suggest that a reflex-based impedance controller combined with an adaptive controller may improve user-cooperative behavior of active knee exoprostheses.