Background and Purpose— Assessing upper limb movements poststroke is crucial to monitor and understand sensorimotor recovery. Kinematic assessments are expected to enable a sensitive quantification of movement quality and distinguish between restitution and compensation. The nature and practice of these assessments are highly variable and used without knowledge of their clinimetric properties. This presents a challenge when interpreting and comparing results. The purpose of this review was to summarize the state of the art regarding kinematic upper limb assessments poststroke with respect to the assessment task, measurement system, and performance metrics with their clinimetric properties. Subsequently, we aimed to provide evidence-based recommendations for future applications of upper limb kinematics in stroke recovery research. Methods— A systematic search was conducted in PubMed, Embase, CINAHL, and IEEE Xplore. Studies investigating clinimetric properties of applied metrics were assessed for risk of bias using the Consensus-Based Standards for the Selection of Health Measurement Instruments checklist. The quality of evidence for metrics was determined according to the Grading of Recommendations Assessment, Development, and Evaluation approach. Results— A total of 225 studies (N=6197) using 151 different kinematic metrics were identified and allocated to 5 task and 3 measurement system groups. Thirty studies investigated clinimetrics of 62 metrics: reliability (n=8), measurement error (n=5), convergent validity (n=22), and responsiveness (n=2). The metrics task/movement time, number of movement onsets, number of movement ends, path length ratio, peak velocity, number of velocity peaks, trunk displacement, and shoulder flexion/extension received a sufficient evaluation for one clinimetric property. Conclusions— Studies on kinematic assessments of upper limb sensorimotor function are poorly standardized and rarely investigate clinimetrics in an unbiased manner. Based on the available evidence, recommendations on the assessment task, measurement system, and performance metrics were made with the goal to increase standardization. Further high-quality studies evaluating clinimetric properties are needed to validate kinematic assessments, with the long-term goal to elucidate upper limb sensorimotor recovery poststroke. Clinical Trial Registration— URL: https://www.crd.york.ac.uk/prospero/ . Unique identifier: CRD42017064279.

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.

The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed noninfected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

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 $$\rho$$ ρ 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 ($$\rho$$ ρ =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 ($$\rho \le$$ ρ ≤ 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.

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.

Falls are a major cause for morbidity and mortality in the ageing society. Inertial sensor based gait assessment including the analysis of the heel and toe clearance can be an indicator for the risk of falling. This paper presents a method for calculating the continuous heel and toe clearance without the knowledge of the shoe dimensions as well as the foot angle in the sagittal plane. These gait parameters were validated using an optical motion capture system. 20 healthy subjects from 3 different age groups (young, mid age, old) performed gait trials with different stride lengths and stride velocities. We obtained low mean absolute errors, low standard deviations and high Pearson correlations (0.91-0.99) for all gait parameters. In summary, we implemented a viable algorithm for the calculation of the heel and toe clearance without knowing the shoe dimensions as well as the foot angle in sagittal plane. We conclude that the given method is applicable for a mobile and unobtrusive gait assessment for healthy subjects from all age classes.

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.

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.