Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform.

Background: Stroke is a leading cause of lifelong disability worldwide, partially driven by a reduced ability to use the upper limb in daily life causing increased dependence on caregivers. However, post-stroke functional impairments have only been investigated using limited clinical scores, during short-term longitudinal studies in relatively small patient cohorts. With the addition of technology-based assessments, we propose to complement clinical assessments with more sensitive and objective measures that could more holistically inform on upper limb impairment recovery after stroke, its impact on upper limb use in daily life, and on overall quality of life. This paper describes a pragmatic, longitudinal, observational study protocol aiming to gather a uniquely rich multimodal database to comprehensively describe the time course of upper limb recovery in a representative cohort of 400 Asian adults after stroke. Particularly, we will characterize the longitudinal relationship between upper limb recovery, common post-stroke impairments, functional independence and quality of life. Methods: Participants with stroke will be tested at up to eight time points, from within a month to 3 years post-stroke, to capture the influence of transitioning from hospital to community settings. We will perform a battery of established clinical assessments to describe the factors most likely to influence upper limb recovery. Further, we will gather digital health biomarkers from robotic or wearable sensing technology-assisted assessments to sensitively characterize motor and somatosensory impairments and upper limb use in daily life. We will also use both quantitative and qualitative measures to understand health-related quality of life. Lastly, we will describe neurophysiological motor status using transcranial magnetic stimulation. Statistics: Descriptive analyses will be first performed to understand post-stroke upper limb impairments and recovery at various time points. The relationships between digital biomarkers and various domains will be explored to inform key aspects of upper limb recovery and its dynamics using correlation matrices. Multiple statistical models will be constructed to characterize the time course of upper limb recovery post-stroke. Subgroups of stroke survivors exhibiting distinct recovery profiles will be identified. Conclusion: This is the first study complementing clinical assessments with technology-assisted digital biomarkers to investigate upper limb sensorimotor recovery in Asian stroke survivors. Overall, this study will yield a multimodal data set that longitudinally characterizes post-stroke upper limb recovery in functional impairments, daily-life upper limb use, and health-related quality of life in a large cohort of Asian stroke survivors. This data set generates valuable information on post-stroke upper limb recovery and potentially allows researchers to identify different recovery profiles of subgroups of Asian stroke survivors. This enables the comparisons between the characteristics and recovery profiles of stroke survivors in different regions. Thus, this study lays out the basis to identify early predictors for upper limb recovery, inform clinical decision-making in Asian stroke survivors and establish tailored therapy programs. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05322837.

The Evolution of Hand Proprioceptive and Motor Impairments in the Sub-Acute Phase After Stroke.

BACKGROUND: Hand proprioception is essential for fine movements and therefore many activities of daily living. Although frequently impaired after stroke, it is unclear how hand proprioception evolves in the sub-acute phase and whether it follows a similar pattern of changes as motor impairments. OBJECTIVE: This work investigates whether there is a corresponding pattern of changes over time in hand proprioception and motor function as comprehensively quantified by a combination of robotic, clinical, and neurophysiological assessments. METHODS: Finger proprioception (position sense) and motor function (force, velocity, range of motion) were evaluated using robotic assessments at baseline (<3 months after stroke) and up to 4 weeks later (discharge). Clinical assessments (among others, Box & Block Test [BBT]) as well as Somatosensory/Motor Evoked Potentials (SSEP/MEP) were additionally performed. RESULTS: Complete datasets from 45 participants post-stroke were obtained. For 42% of all study participants proprioception and motor function had a dissociated pattern of changes (only 1 function considerably improved). This dissociation was either due to the absence of a measurable impairment in 1 modality at baseline, or due to a severe lesion of central somatosensory or motor tracts (absent SSEP/MEP). Better baseline BBT correlated with proprioceptive gains, while proprioceptive impairment at baseline did not correlate with change in BBT. CONCLUSIONS: Proprioception and motor function frequently followed a dissociated pattern of changes in sub-acute stroke. This highlights the importance of monitoring both functions, which could help to further personalize therapies.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation.

Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

Digital health metrics reveal upper limb impairment profiles in ARSACS.

OBJECTIVE: Adults with autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) often present with reduced upper limb coordination affecting their independence in daily life. Previous studies in ARSACS identified reduced performance in clinical assessments requiring fine and gross dexterity as well as prehension. However, the kinematic and kinetic aspects underlying reduced upper limb coordination in ARSACS have not been systematically investigated yet. In this work, we aimed to provide a detailed characterization of alterations in upper limb movement patterns and hand grip forces in 57 participants with ARSACS. METHODS: We relied on a goal-directed technology-aided assessment task, which provides eight previously validated digital health metrics describing movement efficiency, smoothness, speed, and grip force control. RESULTS: First, we observed that 98.3% of the participants were impaired in at least one of the metrics, that all metrics are significantly impaired on a population level, and that grip force control during precise manipulations is most commonly and strongly impaired. Second, we identified high inter-participant variability in the kinematic and kinetic impairment profiles, thereby capturing different clinical profiles subjectively observed in this population. Lastly, abnormal goal-directed task performance in ARSACS could be best explained by reduced movement speed, efficiency, and especially force control during precise manipulations, while abnormal movement smoothness did not have a significant effect. INTERPRETATION: This work helped to refine the clinical profile of ARSACS and highlights the need for characterizing individual kinematic and kinetic impairment profiles in clinical trials in ARSACS.

Reliability, validity and clinical usability of a robotic assessment of finger proprioception in persons with multiple sclerosis.

BACKGROUND: Multiple sclerosis often leads to proprioceptive impairments of the hand. However, it is challenging to objectively assess such deficits using clinical methods, thereby also impeding accurate tracking of disease progression and hence the application of personalized rehabilitation approaches. OBJECTIVE: We aimed to evaluate test-retest reliability, validity, and clinical usability of a novel robotic assessment of hand proprioceptive impairments in persons with multiple sclerosis (pwMS). METHODS: The assessment was implemented in an existing one-degree of freedom end-effector robot (ETH MIKE) acting on the index finger metacarpophalangeal joint. It was performed by 45 pwMS and 59 neurologically intact controls. Additionally, clinical assessments of somatosensation, somatosensory evoked potentials and usability scores were collected in a subset of pwMS. RESULTS: The test-retest reliability of robotic task metrics in pwMS was good (ICC=0.69-0.87). The task could identify individuals with impaired proprioception, as indicated by the significant difference between pwMS and controls, as well as a high impairment classification agreement with a clinical measure of proprioception (85.00-86.67%). Proprioceptive impairments were not correlated with other modalities of somatosensation. The usability of the assessment system was satisfactory (System Usability Scale ≥73.10). CONCLUSION: The proposed assessment is a promising alternative to commonly used clinical methods and will likely contribute to a better understanding of proprioceptive impairments in pwMS.

Design and validation of a novel online platform to support the usability evaluation of wearable robotic devices.

Wearable robotic devices (WRD) are still struggling to fulfill their vast potential. Inadequate daily life usability is one of the main hindrances to increased technology acceptance. Improving usability evaluation practices during the development of WRD could help address these limitations. In this work, we present the design and validation of a novel online platform aiming to fill this gap, the Interactive Usability Toolbox (IUT). This platform consists of a public website that offers an interactive, context-specific search within a database of 154 user research methods and educational information about usability. In a dedicated study, the effect of this platform to support usability evaluation was investigated. Twelve WRD experts were asked to complete the task of defining usability evaluation protocols for two specific use cases. The platform was provided to support one of the use cases. The quality and composition of the proposed protocols were assessed by (i) two blinded reviewers, (ii) the participants themselves, and (iii) the study coordinators. We showed that using the IUT significantly affected the proposed evaluation focus, shifting protocols from mainly effectiveness-oriented to more user-focused studies. The protocol quality, as rated by the external reviewers, remained equivalent to those designed with conventional strategies. A mixed-method usability evaluation of the platform yielded an overall positive image, with detailed suggestions for further improvements. The IUT is expected to positively affect the evaluation and development of WRD through its educational value, the context-specific recommendations supporting ongoing benchmarking endeavors, and highlighting the value of qualitative user research.

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.

Goal-directed upper limb movement patterns and hand grip forces in multiple sclerosis.

Background: Upper limb disability in persons with Multiple Sclerosis (pwMS) leads to increased dependence on caregivers. To better understand upper limb disability, observer-based or time-based clinical assessments have been applied. However, these only poorly capture the behavioural aspects underlying goal-directed task performance. Objective: We aimed to document alterations in goal-directed upper limb movement patterns and hand grip forces in a cohort of pwMS (n = 123) with mild to moderate upper limb impairments. Methods: We relied on the Virtual Peg Insertion Test (VPIT), a technology-aided assessment with a goal-directed pick-and-place task providing a set of validated digital health metrics. Results: All metrics indicated significant differences to an able-bodied reference sample (p < 0.001), with smoothness, speed, and grip force control during object manipulation being most affected in pwMS. Such abnormalities negatively influenced the time to complete the goal-directed task (p < 0.001, R2 = 0.77), thereby showing their functional relevance. Lastly, abnormalities in movement patterns and grip force control were consistently found even in pwMS with clinically normal gross dexterity and grip strength. Conclusion: This work provides a systematic documentation on goal-directed upper limb movement patterns and hand grip forces in pwMS, ultimately paving the way for an early detection of MS sign using digital health metrics.

Reliable and Valid Robotic Assessments of Hand Active and Passive Position Sense in Children With Unilateral Cerebral Palsy.

Impaired hand proprioception can lead to difficulties in performing fine motor tasks, thereby affecting activities of daily living. The majority of children with unilateral cerebral palsy (uCP) experience proprioceptive deficits, but accurately quantifying these deficits is challenging due to the lack of sensitive measurement methods. Robot-assisted assessments provide a promising alternative, however, there is a need for solutions that specifically target children and their needs. We propose two novel robotics-based assessments to sensitively evaluate active and passive position sense of the index finger metacarpophalangeal joint in children. We then investigate test-retest reliability and discriminant validity of these assessments in uCP and typically developing children (TDC), and further use the robotic platform to gain first insights into fundamentals of hand proprioception. Both robotic assessments were performed in two sessions with 1-h break in between. In the passive position sense assessment, participant's finger is passively moved by the robot to a randomly selected position, and she/he needs to indicate the perceived finger position on a tablet screen located directly above the hand, so that the vision of the hand is blocked. Active position sense is assessed by asking participants to accurately move their finger to a target position shown on the tablet screen, without visual feedback of the finger position. Ten children with uCP and 10 age-matched TDC were recruited in this study. Test-retest reliability in both populations was good (intraclass correlation coefficients (ICC) >0.79). Proprioceptive error was larger for children with uCP than TDC (passive: 11.49° ± 5.57° vs. 7.46° ± 4.43°, p = 0.046; active: 10.17° ± 5.62° vs. 5.34° ± 2.03°, p < 0.001), indicating discriminant validity. The active position sense was more accurate than passive, and the scores were not correlated, underlining the need for targeted assessments to comprehensively evaluate proprioception. There was a significant effect of age on passive position sense in TDC but not uCP, possibly linked to disturbed development of proprioceptive acuity in uCP. Overall, the proposed robot-assisted assessments are reliable, valid and a promising alternative to commonly used clinical methods, which could help gain a better understanding of proprioceptive impairments in uCP, facilitating the design of novel therapies.

Hypothalamic Control of Forelimb Motor Adaptation.

The ability to perform skilled arm movements is central to everyday life, as limb impairments in common neurologic disorders such as stroke demonstrate. Skilled arm movements require adaptation of motor commands based on discrepancies between desired and actual movements, called sensory errors. Studies in humans show that this involves predictive and reactive movement adaptations to the errors, and also requires a general motivation to move. How these distinct aspects map onto defined neural signals remains unclear, because of a shortage of equivalent studies in experimental animal models that permit neural-level insights. Therefore, we adapted robotic technology used in human studies to mice, enabling insights into the neural underpinnings of motivational, reactive, and predictive aspects of motor adaptation. Here, we show that forelimb motor adaptation is regulated by neurons previously implicated in motivation and arousal, but not in forelimb motor control: the hypothalamic orexin/hypocretin neurons (HONs). By studying goal-oriented mouse-robot interactions in male mice, we found distinct HON signals occur during forelimb movements and motor adaptation. Temporally-delimited optosilencing of these movement-associated HON signals impaired sensory error-based motor adaptation. Unexpectedly, optosilencing affected neither task reward or execution rates, nor motor performance in tasks that did not require adaptation, indicating that the temporally-defined HON signals studied here were distinct from signals governing general task engagement or sensorimotor control. Collectively, these results reveal a hypothalamic neural substrate regulating forelimb motor adaptation.SIGNIFICANCE STATEMENT The ability to perform skilled, adaptable movements is a fundamental part of daily life, and is impaired in common neurologic diseases such as stroke. Maintaining motor adaptation is thus of great interest, but the necessary brain components remain incompletely identified. We found that impaired motor adaptation results from disruption of cells not previously implicated in this pathology: hypothalamic orexin/hypocretin neurons (HONs). We show that temporally confined HON signals are associated with skilled movements. Without these newly-identified signals, a resistance to movement that is normally rapidly overcome leads to prolonged movement impairment. These results identify natural brain signals that enable rapid and effective motor adaptation.

A low-dimensional representation of arm movements and hand grip forces in post-stroke individuals.

Characterizing post-stroke impairments in the sensorimotor control of arm and hand is essential to better understand altered mechanisms of movement generation. Herein, we used a decomposition algorithm to characterize impairments in end-effector velocity and hand grip force data collected from an instrumented functional task in 83 healthy control and 27 chronic post-stroke individuals with mild-to-moderate impairments. According to kinematic and kinetic raw data, post-stroke individuals showed reduced functional performance during all task phases. After applying the decomposition algorithm, we observed that the behavioural data from healthy controls relies on a low-dimensional representation and demonstrated that this representation is mostly preserved post-stroke. Further, it emerged that reduced functional performance post-stroke correlates to an abnormal variance distribution of the behavioural representation, except when reducing hand grip forces. This suggests that the behavioural repertoire in these post-stroke individuals is mostly preserved, thereby pointing towards therapeutic strategies that optimize movement quality and the reduction of grip forces to improve performance of daily life activities post-stroke.

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy.

Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level ( ICC single = 0.81 and correlation r = 0.81 ). SCR increased the reproducibility from 0.64 to 0.81 ( ICC single ) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.

Reliability and validity of digital health metrics for assessing arm and hand impairments in an ataxic disorder.

OBJECTIVES: Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is the second most frequent recessive ataxia and commonly features reduced upper limb coordination. Sensitive outcome measures of upper limb coordination are essential to track disease progression and the effect of interventions. However, available clinical assessments are insufficient to capture behavioral variability and detailed aspects of motor control. While digital health metrics extracted from technology-aided assessments promise more fine-grained outcome measures, these have not been validated in ARSACS. Thus, the aim was to document the metrological properties of metrics from a technology-aided assessment of arm and hand function in ARSACS. METHODS: We relied on the Virtual Peg Insertion Test (VPIT) and used a previously established core set of 10 digital health metrics describing upper limb movement and grip force patterns during a pick-and-place task. We evaluated reliability, measurement error, and learning effects in 23 participants with ARSACS performing three repeated assessment sessions. In addition, we documented concurrent validity in 57 participants with ARSACS performing one session. RESULTS: Eight metrics had excellent test-retest reliability (intraclass correlation coefficient 0.89 ± 0.08), five low measurement error (smallest real difference % 25.4 ± 5.7), and none strong learning effects (systematic change η -0.11 ± 2.5). Significant correlations (ρ 0.39 ± 0.13) with clinical scales describing gross and fine dexterity and lower limb coordination were observed. INTERPRETATION: This establishes eight digital health metrics as valid and robust endpoints for cross-sectional studies and five metrics as potentially sensitive endpoints for longitudinal studies in ARSACS, thereby promising novel insights into upper limb sensorimotor control.

Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning.

Predicting upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) is essential to optimize therapy allocation. Previous research identified population-level predictors through linear models and clinical data. This work explores the feasibility of predicting individual neurorehabilitation outcomes using machine learning, clinical data, and digital health metrics. Machine learning models were trained on clinical data and digital health metrics recorded pre-intervention in 11 pwMS. The dependent variables indicated whether pwMS considerably improved across the intervention, as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT). Improvements in ARAT or BBT could be accurately predicted (88% and 83% accuracy) using only patient master data. Improvements in NHPT could be predicted with moderate accuracy (73%) and required knowledge about sensorimotor impairments. Assessing these with digital health metrics over clinical scales increased accuracy by 10%. Non-linear models improved accuracy for the BBT (+ 9%), but not for the ARAT (-1%) and NHPT (-2%). This work demonstrates the feasibility of predicting upper limb neurorehabilitation outcomes in pwMS, which justifies the development of more representative prediction models in the future. Digital health metrics improved the prediction of changes in hand control, thereby underlining their advanced sensitivity. Graphical Abstract This work explores the feasibility of predicting individual neurorehabilitation outcomes using machine learning, clinical data, and digital health metrics.

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke.

BACKGROUND: Neurological injuries such as stroke often differentially impair hand motor and somatosensory function, as well as the interplay between the two, which leads to limitations in performing activities of daily living. However, it is challenging to identify which specific aspects of sensorimotor function are impaired based on conventional clinical assessments that are often insensitive and subjective. In this work we propose and validate a set of robot-assisted assessments aiming at disentangling hand proprioceptive from motor impairments, and capturing their interrelation (sensorimotor impairments). METHODS: A battery of five complementary assessment tasks was implemented on a one degree-of-freedom end-effector robotic platform acting on the index finger metacarpophalangeal joint. Specifically, proprioceptive impairments were assessed using a position matching paradigm. Fast target reaching, range of motion and maximum fingertip force tasks characterized motor function deficits. Finally, sensorimotor impairments were assessed using a dexterous trajectory following task. Clinical feasibility (duration), reliability (intra-class correlation coefficient ICC, smallest real difference SRD) and validity (Kruskal-Wallis test, Spearman correlations [Formula: see text] with Fugl-Meyer Upper Limb Motor Assessment, kinesthetic Up-Down Test, Box & Block Test) of robotic tasks were evaluated with 36 sub-acute stroke subjects and 31 age-matched neurologically intact controls. RESULTS: Eighty-three percent of stroke survivors with varied impairment severity (mild to severe) could complete all robotic tasks (duration: <15 min per tested hand). Further, the study demonstrated good to excellent reliability of the robotic tasks in the stroke population (ICC>0.7, SRD<30%), as well as discriminant validity, as indicated by significant differences (p-value<0.001) between stroke and control subjects. Concurrent validity was shown through moderate to strong correlations ([Formula: see text]=0.4-0.8) between robotic outcome measures and clinical scales. Finally, robotic tasks targeting different deficits (motor, sensory) were not strongly correlated with each other ([Formula: see text]0.32, p-value>0.1), thereby presenting complementary information about a patient's impairment profile. CONCLUSIONS: The proposed robot-assisted assessments provide a clinically feasible, reliable, and valid approach to distinctly characterize impairments in hand proprioceptive and motor function, along with the interaction between the two. This opens new avenues to help unravel the contributions of unique aspects of sensorimotor function in post-stroke recovery, as well as to contribute to future developments towards personalized, assessment-driven therapies.

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions.

BACKGROUND: Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. CONTRIBUTION: This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.

Neurorehabilitation From a Distance: Can Intelligent Technology Support Decentralized Access to Quality Therapy?

Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient's home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient's homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.

Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals.

BACKGROUND: Assessing arm and hand sensorimotor impairments that are functionally relevant is essential to optimize the impact of neurorehabilitation interventions. Technology-aided assessments should provide a sensitive and objective characterization of upper limb impairments, but often provide arm weight support and neglect the importance of the hand, thereby questioning their functional relevance. The Virtual Peg Insertion Test (VPIT) addresses these limitations by quantifying arm and hand movements as well as grip forces during a goal-directed manipulation task requiring active lifting of the upper limb against gravity. The aim of this work was to evaluate the ability of the VPIT metrics to characterize arm and hand sensorimotor impairments that are relevant for performing functional tasks. METHODS: Arm and hand sensorimotor impairments were systematically characterized in 30 chronic stroke patients using conventional clinical scales and the VPIT. For the latter, ten previously established kinematic and kinetic core metrics were extracted. The validity and robustness of these metrics was investigated by analyzing their clinimetric properties (test-retest reliability, measurement error, learning effects, concurrent validity). RESULTS: Twenty-three of the participants, the ones with mild to moderate sensorimotor impairments and without strong cognitive deficits, were able to successfully complete the VPIT protocol (duration 16.6 min). The VPIT metrics detected impairments in arm and hand in 90.0% of the participants, and were sensitive to increased muscle tone and pathological joint coupling. Most importantly, significant moderate to high correlations between conventional scales of activity limitations and the VPIT metrics were found, thereby indicating their functional relevance when grasping and transporting objects, and when performing dexterous finger manipulations. Lastly, the robustness of three out of the ten VPIT core metrics in post-stroke individuals was confirmed. CONCLUSIONS: This work provides evidence that technology-aided assessments requiring goal-directed manipulations without arm weight support can provide an objective, robust, and clinically feasible way to assess functionally relevant sensorimotor impairments in arm and hand in chronic post-stroke individuals with mild to moderate deficits. This allows for a better identification of impairments with high functional relevance and can contribute to optimizing the functional benefits of neurorehabilitation interventions.

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments.

Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.