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A new measurement of the ratio of the CP-violating amplitudes for KL → 2π0 and KL → π+π- is reported. The measured value for R = {divides}η00/η+-{divides}2 is 0.9878±0.0026±0.0030, where the first error is the statistical uncertainty and the second is the estimate of the systematic uncertainty. This gives a value for the parameter describing direct CP violation: Re ε{lunate}'/ε{lunate} = (2.0 ± 0.7) × 10-3. © 1993.

Background and aims: Poststroke shoulder pain is a common complication. We aimed to investigate the prevalence of poststroke shoulder pain, with attention to the neuropathic component, and the relationship between poststroke shoulder pain and upper limb improvement in motor function, strength, disability, and quality of life after upper limb rehabilitation. Methods: This is a secondary analysis of a multicenter randomized controlled trial to compare upper limb conventional or robotic rehabilitation on 224 patients enrolled in eight rehabilitation centers. We assessed poststroke shoulder pain (using the Numerical Rating Scale and the Douleur Neuropathique 4), and upper limb motor function, strength, disability, and quality of life at baseline (T0), after 30 rehabilitation sessions (T1), and three months after the end of rehabilitation (T2). Results: A moderate/severe poststroke shoulder pain was reported by 28.9% of patients, while 19.6% of them showed a neuropathic component. At T0, the intensity of pain was higher in women and in patients with neglect syndrome, positively correlated with the time since stroke and disability and negatively correlated with motor function, strength, and the physical aspects of the quality of life. Moderate/severe pain and neuropathic component significantly reduced after both treatments and this reduction was maintained at T2. Finally, the intensity of pain at baseline was negatively correlated with the improvement of upper limb motor function. Conclusions: Poststroke shoulder pain negatively impact on motor performance, strength, disability, and physical aspects of the quality of life as well as on upper limb motor recovery; however, it can be reduced after a robotic or a conventional rehabilitation. Therefore, we suggest considering poststroke shoulder pain when planning the rehabilitation intervention. [ABSTRACT FROM AUTHOR]

Background: Dexterous prosthetic hands that were developed recently, such as SmartHand and i-LIMB, are highly sophisticated; they have individually controllable fingers and the thumb that is able to abduct/adduct. This flexibility allows implementation of many different grasping strategies, but also requires new control algorithms that can exploit the many degrees of freedom available. The current study presents and tests the operation of a new control method for dexterous prosthetic hands.Methods: The central component of the proposed method is an autonomous controller comprising a vision system with rule-based reasoning mounted on a dexterous hand (CyberHand). The controller, termed cognitive vision system (CVS), mimics biological control and generates commands for prehension. The CVS was integrated into a hierarchical control structure: 1) the user triggers the system and controls the orientation of the hand; 2) a high-level controller automatically selects the grasp type and size; and 3) an embedded hand controller implements the selected grasp using closed-loop position/force control. The operation of the control system was tested in 13 healthy subjects who used Cyberhand, attached to the forearm, to grasp and transport 18 objects placed at two different distances.Results: The system correctly estimated grasp type and size (nine commands in total) in about 84% of the trials. In an additional 6% of the trials, the grasp type and/or size were different from the optimal ones, but they were still good enough for the grasp to be successful. If the control task was simplified by decreasing the number of possible commands, the classification accuracy increased (e.g., 93% for guessing the grasp type only).Conclusions: The original outcome of this research is a novel controller empowered by vision and reasoning and capable of high-level analysis (i.e., determining object properties) and autonomous decision making (i.e., selecting the grasp type and size). The automatic control eases the burden from the user and, as a result, the user can concentrate on what he/she does, not on how he/she should do it. The tests showed that the performance of the controller was satisfactory and that the users were able to operate the system with minimal prior training. [ABSTRACT FROM AUTHOR]

Introduction: People with Parkinson's Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects' awareness of their gait disturbances. Moreover, the portability and applicability in clinical and home settings for gait rehabilitation increase the efficiency in the management of PD. The Wearable Vibrotactile Bidirectional Interface (BI) is a biofeedback device designed to extract gait features in real-time and deliver a customized vibrotactile stimulus at the waist of PD subjects synchronously with specific gait phases. The aims of this study were to measure the effect of the BI on gait parameters usually compromised by the typical bradykinetic gait and to assess its usability and safety in clinical practice., Methods: In this case series, seven subjects (age: 70.4 ± 8.1 years; H&Y: 2.7 ± 0.3) used the BI and performed a test on a 10-meter walkway (10mWT) and a two-minute walk test (2MWT) as pre-training (Pre-trn) and post-training (Post-trn) assessments. Gait tests were executed in random order with (Bf) and without (No-Bf) the activation of the biofeedback stimulus. All subjects performed three training sessions of 40 min to familiarize themselves with the BI during walking activities. A descriptive analysis of gait parameters (i.e., gait speed, step length, cadence, walking distance, double-support phase) was carried out. The 2-sided Wilcoxon sign-test was used to assess differences between Bf and No-Bf assessments (p < 0.05)., Results: After training subjects improved gait speed (Pre-trn&#95;No-Bf: 0.72(0.59,0.72) m/sec; Post-trn&#95;Bf: 0.95(0.69,0.98) m/sec; p = 0.043) and step length (Pre-trn&#95;No-Bf: 0.87(0.81,0.96) meters; Post-trn&#95;Bf: 1.05(0.96,1.14) meters; p = 0.023) using the biofeedback during the 10mWT. Similarly, subjects' walking distance improved (Pre-trn&#95;No-Bf: 97.5 (80.3,110.8) meters; Post-trn&#95;Bf: 118.5(99.3,129.3) meters; p = 0.028) and the duration of the double-support phase decreased (Pre-trn&#95;No-Bf: 29.7(26.8,31.7) %; Post-trn&#95;Bf: 27.2(24.6,28.7) %; p = 0.018) during the 2MWT. An immediate effect of the BI was detected in cadence (Pre-trn&#95;No-Bf: 108(103.8,116.7) step/min; Pre-trn&#95;Bf: 101.4(96.3,111.4) step/min; p = 0.028) at Pre-trn, and in walking distance at Post-trn (Post-trn&#95;No-Bf: 112.5(97.5,124.5) meters; Post-trn&#95;Bf: 118.5(99.3,129.3) meters; p = 0.043). SUS scores were 77.5 in five subjects and 80.3 in two subjects. In terms of safety, all subjects completed the protocol without any adverse events., Conclusion: The BI seems to be usable and safe for PD users. Temporal gait parameters have been measured during clinical walking tests providing detailed outcomes. A short period of training with the BI suggests improvements in the gait patterns of people with PD. This research serves as preliminary support for future integration of the BI as an instrument for clinical assessment and rehabilitation in people with PD, both in hospital and remote environments., Trial Registration: The study protocol was registered (DGDMF.VI/P/I.5.i.m.2/2019/1297) and approved by the General Directorate of Medical Devices and Pharmaceutical Service of the Italian Ministry of Health and by the ethics committee of the Lombardy region (Milan, Italy)., (© 2024. The Author(s).)

Objectives: To verify whether trunk control test (TCT) upon admission to intensive inpatient post-stroke rehabilitation, combined with other confounding variables, is independently associated with discharge mBI., Design: Multicentric retrospective observational cohort study., Setting: Two Italian inpatient rehabilitation units., Participants: A total of 220 post-stroke adult patients, within 30 days from the acute event, were consecutively enrolled., Interventions: Not applicable., Main Outcome Measure: The outcome measure considered was the modified Barthel Index (mBI), one of the most widely recommended tools for assessing stroke rehabilitation functional outcomes., Results: All variables collected at admission and significantly associated with mBI at discharge in the univariate analysis (TCT, mBI at admission, pre-stroke modified Rankin Scale [mRS], sex, age, communication ability, time from the event, Cumulative Illness Rating Scale, bladder catheter, and pressure ulcers) entered the multivariate analysis. TCT, mBI at admission, premorbid disability (mRS), communication ability and pressure ulcers (P<.001) independently predicted discharge mBI (adjusted R 2 =68.5%). Concerning the role of TCT, the model with all covariates and without TCT presented an R 2 of 65.1%. On the other side, the model with the TCT only presented an R 2 of 53.1%. Finally, with the inclusion of both TCT and all covariates, the model showed an R 2 increase up to 68.5%., Conclusions: TCT, with other features suggesting functional/clinical complexity, collected upon admission to post-acute intensive inpatient stroke rehabilitation, independently predicted discharge mBI., (Copyright © 2023 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Individuals with Parkinson's disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects' activities. In this study, we verified the capability of a pair of sensorized shoes (including pressure-sensitive insoles and one inertial measurement unit) in assessing ground-level walking and body weight shift exercises. The shoes can potentially be combined with a sensory biofeedback module that provides vibrotactile cues to individuals. Sensorized shoes have been assessed in terms of the capability of detecting relevant gait events (heel strike, flat foot, toe off), estimating spatiotemporal parameters of gait (stance, swing, and double support duration, stride length), estimating gait variables (vertical ground-reaction force, vGRF; coordinate of the center of pressure along the longitudinal axes of the feet, yCoP; and the dorsiflexion angle of the feet, Pitch angle). The assessment compared the outcomes with those extracted from the gold standard equipment, namely force platforms and a motion capture system. Results of this comparison with 9 PD subjects showed an overall median absolute error lower than 0.03 s in detecting the foot-contact, foot-off, and heel-off gait events while performing ground-level walking and lower than 0.15 s in body weight shift exercises. The computation of spatiotemporal parameters of gait showed median errors of 1.62 % of the stance phase duration and 0.002 m of the step length. Regarding the estimation of vGRF, yCoP, and Pitch angle, the median across-subjects Pearson correlation coefficient was 0.90, 0.94, and 0.91, respectively. These results confirm the suitability of the sensorized shoes for quantifying biomechanical features during body weight shift and gait exercises of PD and pave the way to exploit the biofeedback modules of the bidirectional interface in future studies.

Objective: The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The analysis of the acoustic signals through the implementation of machine learning models can be a viable solution aimed at offering support for clinical diagnosis., Materials and Methods: In this study, a support vector machine was trained and cross-validated (10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients with polyps, nodules, oedema, and cysts. The model's performance was presented as accuracy and average F1-score. The results were also analysed in male (M) and female (F) subgroups., Results: The validation accuracy was 55%, 80%, and 54% on the overall cohort, and in M and F, respectively. Better performances were observed in the detection of cysts and nodules (58% and 62%, respectively) vs polyps and oedema (47% and 53%, respectively). The results on each lesion and the different patterns of the model on M and F are in line with clinical observations, obtaining better results on F and detection of sensitive polyps in M., Conclusions: This study showed moderately accurate detection of four types of BLVF using acoustic signals. The analysis of the diagnostic results on gender subgroups highlights different behaviours of the diagnostic model., (Copyright © 2023 Società Italiana di Otorinolaringoiatria e Chirurgia Cervico-Facciale, Rome, Italy.)

Purpose: To assess the intra- and inter-rater reliability motor and sensory functioning, balance, joint range of motion and joint pain subscales of the Italian Fugl-Meyer Assessment (FMA) Upper Extremity (FMA-UE) and Lower Extremity (FMA-LE) at the item- subtotal- and total-level in patients with sub-acute stroke., Materials and Methods: The FMA was administered to 60 patients with sub-acute stroke (mean age ± SD = 75.4 ± 10.7 years; 58.3% men) and independently rated by two physiotherapists on two consecutive days. Intra- and inter-reliability was studied by a rank-based statistical method for paired ordinal data to detect any systematic or random disagreement., Results: The item-level intra- and inter-rater reliability was satisfactory (>70%). Reliability level >70% was achieved at subscale and total score level when one- or two-points difference was considered. Systematic disagreements were reported for five items of the FMA-UE, but not for FMA-LE., Conclusions: The Italian version of the FMA showed to be a reliable instrument that can therefore be recommended for clinical and research purposes.Implications for rehabilitationThe FMA is the gold standard for assessing stroke patients' sensorimotor impairment worldwide.The Italian Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and Lower Extremity (FMA-LE) is substantially reliable within and between two raters at the item, subtotal, and total score level in patients with sub-acute stroke.The use of FMA in the Italian context will provide an opportunity for international comparisons and research collaborations.

The use of stereophotogrammetry systems is challenging when targeting children's gait analysis due to the time required and the need to keep physical markers in place. For this reason, marker-less photoelectric systems appear to be a solution for accurate and fast gait analysis in youth. The aim of this study is to validate a photoelectric system and its configurations (LED filter setting) on healthy children, comparing the kinematic gait parameters with those obtained from a three-dimensional stereophotogrammetry system. Twenty-seven healthy children were enrolled. Three LED filter settings for the OptoGait were compared to the BTS P6000. The analysis included the non-parametric 80% limits of agreement and the intraclass correlation coefficient (ICC). Additionally, normalised limits of agreement and bias (NLoAs and Nbias) were compared to the clinical experience of physical therapists (i.e., assuming an error lower than 5% is acceptable). ICCs showed excellent consistency for most of the parameters and filter settings; NLoAs varied between 1.39% and 12.62%. An inverse association between the number of LEDs for filter setting and the bias values was also observed. Observations confirm the validity of the OptoGait system for the evaluation of spatiotemporal gait parameters in children.

Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments., (© 2023. The Author(s).)

Background: Trunk control plays a crucial role in the stroke rehabilitation, but it is unclear which factors could influence the trunk control after an intensive rehabilitation treatment., Objectives: To study which demographic, clinical and functional variables could predict the recovery of trunk control after intensive post-stroke inpatient rehabilitation., Methods: Subjects with acute, first-ever stroke were enrolled and clinical and data were collected at admission and discharge. The primary outcome was considered the trunk control measured by the Trunk Control Test (TCT). The data were analyzed by a univariate and multivariate logistic regressions., Results: Two hundred forty-one post-stroke patients were included. All baseline variables significantly associated to TCT at discharge in the univariate analysis (i.e. gender, NIHSS neglect item at admission, presence of several complexity markers, TCT total score at admission, NIHSS total score, pre-stroke modified Rankin Scale, Fugl-Meyer Assessment motor and sensitivity score) were entered in the multivariate analysis. The multivariate regression showed that age ( p = .003), admission NIHSS total score ( p = .001), admission TCT total score ( p < .001) and presence of depression ( p = .027) independently influenced the TCT total score at discharge ( R 2  = 61.2%)., Conclusions: Age, admission neurological impairment (NIHSS total score), trunk control at the admission (TCT total score), and presence of depression independently influenced the TCT at discharge. These factors should be carefully assessed at the baseline to plan a tailoring rehabilitation treatment achieving the best trunk control performance at discharge.

Objective: Disorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting., Methods: We conducted a systematic literature search of EEG-based strategies for consciousness recovery prognosis in five electronic databases., Results: The search resulted in 2964 papers. After screening, 15 studies were included in the review. Our analyses revealed that simpler experimental settings and similar filtering cut-off frequencies are preferred. The results of studies were categorised by extracting qualitative and quantitative features. The quantitative features were further classified into evoked/event-related potentials, spectral measures, entropy measures, and graph-theory measures. Despite the variety of methods, features from all categories, including qualitative ones, exhibited significant correlations with DoC prognosis. Moreover, no agreement was found on the optimal set of EEG-based features for the multivariate prognosis of patients with DoC, which limits the computational methods applied for outcome prediction and correlation analysis to classical ones. Nevertheless, alpha power, reactivity, and higher complexity metrics were often found to be predictive of consciousness recovery., Conclusions: This study's findings confirm the essential role of qualitative EEG and suggest an important role for quantitative EEG. Their joint use could compensate for their reciprocal limitations., Significance: This study emphasises the need for further efforts toward guidelines on standardised EEG analysis pipeline, given the already proven role of EEG markers in the recovery prognosis of patients with DoC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Background: Stroke represents the second preventable cause of death after cardiovascular disease and the third global cause of disability. In countries where national registries of the clinical quality of stroke care have been established, the publication and sharing of the collected data have led to an improvement in the quality of care and survival of patients. However, information on rehabilitation processes and outcomes is often lacking, and predictors of functional outcomes remain poorly explored. This paper describes a multicenter study protocol to implement a Stroke rehabilitation Registry, mainly based on a multidimensional assessment proposed by the Italian Society of Physical and Rehabilitation Medicine (PMIC2020), in a pilot Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation, to provide a systematic assessment of processes and outcomes and develop data-driven prediction models of functional outcomes., Methods: All patients with a diagnosis of ischemic or haemorrhagic stroke confirmed by clinical assessment, admitted to intensive rehabilitation units within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled. Measures will be taken at admission (T0), at discharge (T1), and at follow-up, 3 months (T2) and 6 months (T3) after the stroke. Assessment variables include anamnestic data, clinical and nursing complexity information and measures of body structures and function, activity and participation (PMIC2020), rehabilitation interventions, adverse events and discharge data. The modified Barthel Index will be our primary outcome. In addition to classical biostatistical analysis, learning algorithms will be cross-validated to achieve data-driven prognosis prediction models., Conclusions: This study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization., Clinical Tial Registration: The registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chiavilli, Campagnini, Baretta, Castagnoli, Paperini, Politi, Pellicciari, Baccini, Basagni, Marignani, Bardi, Sodero, Lombardi, Guolo, Navarro, Galeri, Montesano, Falco, Rovaris, Carrozza, Macchi, Mannini and Cecchi.)

Background: Rehabilitation treatments and services are essential for the recovery of post-stroke patients' functions; however, the increasing number of available therapies and the lack of consensus among outcome measures compromises the possibility to determine an appropriate level of evidence. Machine learning techniques for prognostic applications offer accurate and interpretable predictions, supporting the clinical decision for personalised treatment. The aim of this study is to develop and cross-validate predictive models for the functional prognosis of patients, highlighting the contributions of each predictor., Methods: A dataset of 278 post-stroke patients was used for the prediction of the class transition, obtained from the modified Barthel Index. Four classification algorithms were cross-validated and compared. On the best performing model on the validation set, an analysis of predictors contribution was conducted., Results: The Random Forest obtained the best overall results on the accuracy (76.2%), balanced accuracy (74.3%), sensitivity (0.80), and specificity (0.68). The combination of all the classification results on the test set, by weighted voting, reached 80.2% accuracy. The predictors analysis applied on the Support Vector Machine, showed that a good trunk control and communication level, and the absence of bedsores retain the major contribution in the prediction of a good functional outcome., Conclusions: Despite a more comprehensive assessment of the patients is needed, this work paves the way for the implementation of solutions for clinical decision support in the rehabilitation of post-stroke patients. Indeed, offering good prognostic accuracies for class transition and patient-wise view of the predictors contributions, it might help in a personalised optimisation of the patients' rehabilitation path., (© 2022. The Author(s).)

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The state of the art is still lacking an extensive analysis of which clinical characteristics are leading to better outcomes after robot-assisted rehabilitation on post-stroke patients. Prognostic machine learning-based models could promote the identification of predictive factors and be exploited as Clinical Decision Support Systems (CDSS). For this reason, the aim of this work was to set the first steps toward the development of a CDSS, by the development of machine learning models for the functional outcome prediction of post-stroke patients after upper-limb robotic rehabilitation. Four different regression algorithms were trained and cross-validated using a nested 5×10-fold cross-validation. The performances of each model on the test set were provided through the Median Average Error (MAE) and interquartile range. Additionally, interpretability analyses were performed, to evaluate the contribution of the features to the prediction. The results on the two best performing models showed a MAE of 13.6 [13.4] and 13.3 [14.8] on the Modified Barthel Index score (MBI). The interpretability analyses highlighted the Fugl-Meyer Assessment, MBI, and age as the most relevant features for the prediction of the outcome. This work showed promising results in terms of outcome prognosis after robot-assisted treatment. Further research should be planned for the development, validation and translation into clinical practice of CDSS in rehabilitation. Clinical relevance- This work establishes the premises for the development of data-driven tools able to support the clinical decision for the selection and optimisation of the robotic rehabilitation treatment.

Background: Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with new trends in personalised medical therapies, is expected to change clinical practice dramatically. The emerging field of Rehabilomics is only possible if methodologies are based on biomedical data collection and analysis. In this framework, the objective of this work is to develop a systematic review of machine learning algorithms as solutions to predict motor functional recovery of post-stroke patients after treatment., Methods: We conducted a comprehensive search of five electronic databases using the Patient, Intervention, Comparison and Outcome (PICO) format. We extracted health conditions, population characteristics, outcome assessed, the method for feature extraction and selection, the algorithm used, and the validation approach. The methodological quality of included studies was assessed using the prediction model risk of bias assessment tool (PROBAST). A qualitative description of the characteristics of the included studies as well as a narrative data synthesis was performed., Results: A total of 19 primary studies were included. The predictors most frequently used belonged to the areas of demographic characteristics and stroke assessment through clinical examination. Regarding the methods, linear and logistic regressions were the most frequently used and cross-validation was the preferred validation approach., Conclusions: We identified several methodological limitations: small sample sizes, a limited number of external validation approaches, and high heterogeneity among input and output variables. Although these elements prevented a quantitative comparison across models, we defined the most frequently used models given a specific outcome, providing useful indications for the application of more complex machine learning algorithms in rehabilitation medicine., (© 2022. The Author(s).)

Background: Stroke related motor function deficits affect patients' likelihood of returning to professional activities, limit their participation in society and functionality in daily living. Hence, robot-aided gait rehabilitation needs to be fruitful and effective from a motor learning perspective. For this reason, optimal human-robot interaction strategies are necessary to foster neuroplastic shaping during therapy. Therefore, we performed a systematic search on the effects of different control algorithms on quantitative objective gait parameters of post-acute stroke patients., Methods: We conducted a systematic search on four electronic databases using the Population Intervention Comparison and Outcome format. The heterogeneity of performance assessment, study designs and patients' numerosity prevented the possibility to conduct a rigorous meta-analysis, thus, the results were presented through narrative synthesis., Results: A total of 31 studies (out of 1036) met the inclusion criteria, without applying any temporal constraints. No controller preference with respect to gait parameters improvements was found. However, preferred solutions were encountered in the implementation of force control strategies mostly on rigid devices in therapeutic scenarios. Conversely, soft devices, which were all position-controlled, were found to be more commonly used in assistive scenarios. The effect of different controllers on gait could not be evaluated since conspicuous heterogeneity was found for both performance metrics and study designs., Conclusions: Overall, due to the impossibility of performing a meta-analysis, this systematic review calls for an outcome standardisation in the evaluation of robot-aided gait rehabilitation. This could allow for the comparison of adaptive and human-dependent controllers with conventional ones, identifying the most suitable control strategies for specific pathologic gait patterns. This latter aspect could bolster individualized and personalized choices of control strategies during the therapeutic or assistive path., (© 2022. The Author(s).)

Background: Due to continuous advances in intensive care technology and neurosurgical procedures, the number of survivors from severe acquired brain injuries (sABIs) has increased considerably, raising several delicate ethical issues. The heterogeneity and complex nature of the neurological damage of sABIs make the detection of predictive factors of a better outcome very challenging. Identifying the profile of those patients with better prospects of recovery will facilitate clinical and family choices and allow to personalize rehabilitation. This paper describes a multicenter prospective study protocol, to investigate outcomes and baseline predictors or biomarkers of functional recovery, on a large Italian cohort of sABI survivors undergoing postacute rehabilitation., Methods: All patients with a diagnosis of sABI admitted to four intensive rehabilitation units (IRUs) within 4 months from the acute event, aged above 18, and providing informed consent, will be enrolled. No additional exclusion criteria will be considered. Measures will be taken at admission (T0), at three (T1) and 6 months (T2) from T0, and follow-up at 12 and 24 months from onset, including clinical and functional data, neurophysiological results, and analysis of neurogenetic biomarkers., Statistics: Advanced machine learning algorithms will be cross validated to achieve data-driven prediction models. To assess the clinical applicability of the solutions obtained, the prediction of recovery milestones will be compared to the evaluation of a multiprofessional, interdisciplinary rehabilitation team, performed within 2 weeks from admission., Discussion: Identifying the profiles of patients with a favorable prognosis would allow customization of rehabilitation strategies, to provide accurate information to the caregivers and, possibly, to optimize rehabilitation outcomes., Conclusions: The application and validation of machine learning algorithms on a comprehensive pool of clinical, genetic, and neurophysiological data can pave the way toward the implementation of tools in support of the clinical prognosis for the rehabilitation pathways of patients after sABI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hakiki, Donnini, Romoli, Draghi, Maccanti, Grippo, Scarpino, Maiorelli, Sterpu, Atzori, Mannini, Campagnini, Bagnoli, Ingannato, Nacmias, De Bellis, Estraneo, Carli, Pasqualone, Comanducci, Navarro, Carrozza, Macchi and Cecchi.)

Objective: The aim of this study was to describe an innovative methodology of a registry development, constantly updated for the scientific assessment and analysis of the health status of the population with COVID-19., Study Design and Setting: A methodological study design to develop a multi-site, Living COVID-19 Registry of COVID-19 patients admitted in Fondazione Don Gnocchi centres started in March 2020., Results: The integration of the living systematic reviews and focus group methodologies led to a development of a registry which includes 520 fields filled in for 748 COVID-19 patients recruited from 17 Fondazione Don Gnocchi centres. The result is an evidence and experience-based registry, according to the evolution of a new pathology which was not known before outbreak of March 2020 and with the aim of building knowledge to provide a better quality of care for COVID-19 patients., Conclusion: A Living COVID-19 Registry is an open, living and up to date access to large-scale patient-level data sets that could help identifying important factors and modulating variable for recognising risk profiles and predicting treatment success in COVID-19 patients hospitalized. This innovative methodology might be used for other registries, to be sure which the data collected is an appropriate means of accomplishing the scientific objectives planned., Clinical Trial Registration Number: not applicable., (Copyright © 2021. Published by Elsevier Inc.)

COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients were enrolled in our observational study. Anagraphical and anamnestic data, COVID-19 signs and symptoms, COVID-19 therapy, hematochemical test results, and prior therapies administered to patients are used as predictors. A set of 55 features, all of which can be taken in the first hours of the patient's hospitalization, was considered. Different solutions were compared achieving the best performance with a sequential convolutional neural network-based model merged in an ensemble with two different meta-learners linked in cascade. We obtained a median absolute error of 2.7 days (IQR = 3.0) in predicting the duration of the infection; the error was equally distributed in the infection duration range. This tool could preemptively give an outlook of the COVID-19 patients' expected path and the associated hospitalization effort. The proposed solution could be viable in tackling the huge burden and the logistics complexity of hospitals or rehabilitation centers during the pandemic waves. With data taken ad admission, entering a PCA-based feature selection, a k-fold cross-validated CNN-based model was implemented. After external texting, a median absolute error of 2.7 days [IQR = 3 days]., (© 2022. International Federation for Medical and Biological Engineering.)

Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical data-based and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Data-driven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decision-making process the practitioners must face.

Objectives: This study aims to evaluate the diagnostic performance of NIHSS extinction and inattention item, compared to the results of the Oxford Cognitive Screen (OCS) heart subtest. Additionally, the possible role of the NIHSS visual field subtest on the NIHSS extinction and inattention subtest performance is explored and discussed., Methods: We analysed scores on NIHSS extinction and inattention subtest, NIHSS visual field subtest, and OCS heart subtest on a sample of 118 post-stroke patients., Results: Compared to OCS heart subtest, the results on NIHSS extinction and inattention subtest showed an accuracy of 72.9% and a moderate agreement level (Cohen's kappa = 0.404). Furthermore, a decrease in NIHSS accuracy detecting neglect (61.1%) was observed in patients with pathological scores in NIHSS visual field item., Conclusions: Extreme caution is recommended for the diagnostic performance of extinction and inattention item of NIHSS. Signs of neglect may not be detected by NIHSS, and may be confused with visual field impairment., Trial Registration: This study refers to an observational study protocol submitted to ClinicalTrials.gov with identifier: NCT03968627 . The name of the registry is "Development of a National Protocol for Stroke Rehabilitation in a Multicenter Italian Institution" and the date of the registration is the 30th May 2019., (© 2021. The Author(s).)

Background and Objectives: From a rehabilitation perspective, removal of tracheostomy in patients with severe acquired brain injuries (sABI) is a crucial step. Predictive parameters for a successful decannulation are currently still a focus of the research for sABI patients, especially for those presenting a disorder of consciousness. For this reason, we adopted a data-driven approach predicting decannulation probability and timing using ensemble learning models in patients in intensive rehabilitation units., Methods: 327 patients, 186 of which were successfully decannulated during their intensive rehabilitative stay, were recruited in a non-concurrent retrospective study. Decannulation probability and timing were predicted using data available within one week from admission at the rehabilitation unit. Two predictive models were trained and cross-validated independently, with the first being an ensemble of a Support Vector Machine and Random Forests and the second an Adaptive Boosting with a Support Vector Regression as weak learner. Confusion matrix, accuracy and AUC were considered as evaluation metrics for the classifier and median absolute error was considered for the regressor. To quantify the advantages in the clinical practice of using the latter prediction, we compared timing estimation with a timing guess (median) calculated on available data. The comparison was based on a Wilcoxon signed rank test., Results: Decannulation probability was successfully predicted with an accuracy of 84.8% (AUC = 0.85) and timing with a median absolute error of 25.7 days [IQR = 25.6]. This resulted in a significant improvement with respect to the weaning time guess (p<0.05) with an effect size of 71.7%. Furthermore, dichotomizing the regression prediction with a threshold (3 months from the event), resulted in a prediction accuracy of 77.5% (AUC = 0.82) on the test set., Discussions: A model capable of providing a prediction on decannulation probability and timing was developed and cross-validated, built on data taken at admission to the intensive rehabilitation unit. Translated in clinical practice, this information can support the clinical decision process and provide a mean to improve both in-hospital and domiciliary care organization., Competing Interests: Declaration of Competing Interest No author has a competing interest to declare., (Copyright © 2021. Published by Elsevier B.V.)

Background: The aim of the study was to describe the epidemiological characteristics of Nursing Homes (NHs) residents infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and to compute the related case-fatality rate., Materials and Methods: The outcomes were mortality and case-fatality rate with related epidemiological characteristics (age, sex, comorbidity, and frailty)., Results: During the COVID-19 outbreak lasted from March 1 to May 7, 2020, 330 residents died in Fondazione Don Gnocchi NHs bringing the mortality rate to 27% with a dramatic increase compared to the same period of 2019, when it was 7.5%. Naso/oropharyngeal swabs resulted positive for COVID-19 in 315 (71%) of the 441of the symptomatic/exposed residents tested. The COVID-19 population was 75% female, with a 17% overall fatality rate and sex-specific fatality rates of 19% and 13% for females and males, respectively. Fifty-six percent of deaths presented SARS-CoV-2-associated pneumonia, 15% cardiovascular, and 29% miscellaneous pathologies., Conclusion: Patients' complexity and frailty might influence SARS-CoV-2 infection case-fatality rate estimates. A COVID-19 register is needed to study COVID-19 frail patients' epidemiology and characteristics., Competing Interests: There are no conflicts of interest., (Copyright: © 2021 Journal of Research in Medical Sciences.)

Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients' engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson's n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions.

Cognitive decline is often present in stroke survivors, with a significant impact on motor recovery. However, how specific cognitive domains could impact motor recovery after robotic rehabilitation in patients with stroke is still not well understood. In this study, we analyzed the relationship between cognitive impairment and the outcome of a robot-mediated upper limb rehabilitation intervention in a sample of 51 subacute stroke patients. Participants were enrolled and treated with a set of robotic and sensor-based devices. Before the intervention, patients underwent a cognitive assessment by means of the Oxford Cognitive Screen. To assess the effect of the 30-session rehabilitation intervention, patients were assessed twice with the following outcome measures: the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), to evaluate motor function; the Upper limb Motricity Index (MI), to evaluate upper limb muscle strength; the Modified Barthel Index (mBI), to evaluate activities of daily living and mobility. We found that deficits in spatial attention and executive functions impacted the mBI improvement, while language, number processing, and spatial attention deficits reduced the gains in the FMA-UE. These results suggest the importance to evaluate the cognitive functions using an adequate tool in patients with stroke undergoing a robotic rehabilitation intervention.

Background: The complex nature of stroke sequelae, the heterogeneity in rehabilitation pathways, and the lack of validated prediction models of rehabilitation outcomes challenge stroke rehabilitation quality assessment and clinical research. An integrated care pathway (ICP), defining a reproducible rehabilitation assessment and process, may provide a structured frame within investigated outcomes and individual predictors of response to treatment, including neurophysiological and neurogenetic biomarkers. Predictors may differ for different interventions, suggesting clues to personalize and optimize rehabilitation. To date, a large representative Italian cohort study focusing on individual variability of response to an evidence-based ICP is lacking, and predictors of individual response to rehabilitation are largely unexplored. This paper describes a multicenter study protocol to prospectively investigate outcomes and predictors of response to an evidence-based ICP in a large Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation. Methods: All patients with diagnosis of ischemic or hemorrhagic stroke confirmed both by clinical and brain imaging evaluation, admitted to four intensive rehabilitation units (adopting the same stroke rehabilitation ICP) within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled (expected sample: 270 patients). Measures will be taken at admission (T0), at discharge (T1), and at follow-up 6 months after a stroke (T2), including clinical data, nutritional, functional, neurological, and neuropsychological measures, electroencephalography and motor evoked potentials, and analysis of neurogenetic biomarkers. Statistics: In addition to classical multivariate logistic regression analysis, advanced machine learning algorithms will be cross-validated to achieve data-driven prognosis prediction models. Discussion: By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to the development of patient-oriented therapy and to optimize rehabilitation outcomes. Clinical Trial Registration: ClinicalTrials.gov, NCT03968627. https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&cond=Stroke&draw=2&rank=2., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hakiki, Paperini, Castagnoli, Hochleitner, Verdesca, Grippo, Scarpino, Maiorelli, Mosca, Gemignani, Borsotti, Gabrielli, Salvadori, Poggesi, Lucidi, Falsini, Gentilini, Martini, Luisi, Biffi, Mainardi, Barretta, Pancani, Mannini, Campagnini, Bagnoli, Ingannato, Nacmias, Macchi, Carrozza and Cecchi.)

Introduction: In recent years, robot-assisted gait training (RAGT) has been proposed as therapy for balance and gait dysfunctions in people with multiple sclerosis (PwMS). Through this systematic review, we aimed to discuss the impact of RAGT on balance and gait outcomes. Furthermore, characteristics of the training in terms of robots used, participants characteristics, protocols and combined therapeutic approaches have been described., Evidence Acquisition: As part of the Italian Consensus on robotic rehabilitation "CICERONE" a systematic search was provided in PubMed, the Cochrane Library and PEDro to identify relevant studies published before December 2019. Only randomized control trials (RCT) involving RAGT for PwMS were included. PEDro scale was used to assess the risk of bias and the Oxford Center for Evidence-Based Medicine (OCEBM) was used to assess level of evidence of included studies., Evidence Synthesis: The search on databases resulted in 336 records and, finally, 12 studies were included. RAGT was provided with Exoskeleton in ten studies (6-40 session, 2-5 per week) and with end-effector in two studies (12 sessions, 2-3 per week) with large variability in terms of participants' disability. All the exoskeletons were combined with bodyweight support treadmill and movement assistance varied from 0% to 100% depending on participants' disability, two studies combined exoskeleton with virtual reality. The end-effector speed ranged between 1.3 and 1.8 km/h, with bodyweight support starting from 50% and progressively reduced. In seven out of twelve studies RAGT was provided in a multimodal rehabilitation program or in combination with standard physical therapy. There is level 2 evidence that RAGT has positive impact in PwMS, reaching the minimally clinically importance difference in Berg Balance Scale, six-minute walking test and gait speed., Conclusions: In available RCT, RAGT is mostly provided with exoskeleton devices and improves balance and gait outcomes in a clinically meaningful way. Considering several advantages in terms of safety, motor assistance and intensity of training provided, RAGT should be promoted for PwMS with severe disability in a multimodal rehabilitation context as an opportunity to maximize recovery.

In this case series study, we aimed to evaluate the feasibility of a subacute rehabilitation program for mechanically ventilated patients with severe consequences of COVID-19 infection. Data were retrospectively collected from seven males (age 37-61 years) who were referred for inpatient rehabilitation following the stay in the ICU (14-22 days). On admission, six patients were still supported by mechanical ventilation. All patients were first placed in isolation in a special COVID unit for 6-22 days. Patients attended 11-24 treatment sessions for the duration of rehabilitation stay (13-27 days), including 6-20 sessions in the COVID unit. The treatment included pulmonary and physical rehabilitation. The initially nonventilated patient was discharged prematurely due to gallbladder problems, whereas all six mechanically ventilated patients were successfully weaned off before transfer to a COVID-free unit where they stayed for 7-19 days. At discharge, all patients increased limb muscle strength and thigh circumference, reduced activity-related dyspnea, regained functional independence and reported better quality of life. Rehabilitation plays a vital role in the recovery of seriously ill post-COVID-19 patients. Facilities should develop and implement plans for providing multidisciplinary rehabilitation treatments in various settings to recover functioning and prevent the development of long-term consequences of the COVID-19 disease., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: There is consistent evidence that robotic rehabilitation is at least as effective as conventional physiotherapy for upper extremity (UE) recovery after stroke, suggesting to focus research on which subgroups of patients may better respond to either intervention. In this study, we evaluated which baseline variables are associated with the response after the two approaches., Methods: This is a secondary analysis of a randomized-controlled trial comparing robotic and conventional treatment for the UE. After the assigned intervention, changes of the Fugl-Meyer Assessment UE score by ≥ 5 points classified patients as responders to treatment. Variables associated with the response were identified in a univariate analysis. Then, variables independently associated with recovery were investigated, in the whole group, and the two groups separately., Results: A sample of 190 patients was evaluated after the treatment; 121 were responders. Age, baseline impairment, and neglect were significantly associated with worse response to the treatment. Age was the only independently associated variable (OR 0.967, p = 0.023). Considering separately the two interventions, age remained negatively associated with recovery (OR 0.948, p = 0.013) in the conventional group, while none of the variables previously identified were significantly associated with the response to treatment in the robotic group., Conclusions: We found that, in our sample, age is significantly associated with the outcome after conventional but not robotic UE rehabilitation. Possible explanations may include an enhanced positive attitude of the older patients towards technological training and reduced age-associated fatigue provided by robotic-assisted exercise. The possibly higher challenge proposed by robotic training, unbiased by the negative stereotypes concerning very old patients' expectations and chances to recover, may also explain our findings., Trial Registration Number: NCT02879279.

Details of the Clinical Case: A 51-yr-old man underwent a respiratory rehabilitation program (RRP), after being tracheostomized and ventilated due to acute respiratory distress syndrome (ARDS) from coronavirus disease-2019 (COVID-19) infection. Respiratory care, early mobilization, and neuromuscular electrical stimulation were started in the ad hoc isolation ward of our rehabilitation center. At baseline, muscle function was consistent with intensive care unit-acquired weakness and the patient still needed mechanical ventilation (MV) and oxygen support. During the first week of RRP in isolation, the patient was successfully weaned from MV, the tracheal cannula was removed, and the walking capacity was recovered. At the end of the RRP, continued in a standard department, respiratory muscles strength increased by 7% and muscle function improved as indicated by the quadriceps size enlargement of 13% and the change of the Medical Research Council sum score from 48/60 to 58/60., Discussion: Providing RRP in patients with severe COVID-19 ARDS involves risks for operators and organizational difficulties, especially in rehabilitation centers; nevertheless, its continuity is important to prevent the development of permanent disabilities in previously healthy subjects. Limited to the experience of only one patient, we were able to carry out a safe RRP during the COVID-19 pandemic, promoting the complete functional recovery of a COVID-19 young patient., Summary: Most patients who develop serious consequences of COVID-19 infection risk a reduction in their quality of life. However, by organizing and directing specialized resources, subacute rehabilitation facilities could ensure the continuity of the RRPs even during the COVID-19 pandemic.

Background and Purpose: Rehabilitation plays a central role in stroke recovery. Besides conventional therapy, technological treatments have become available. The effectiveness and appropriateness of technological rehabilitation are not yet well defined; hence, research focused on different variables impacting recovery is needed. Results from the literature identified cognitive reserve (CR) as a variable impacting on the cognitive outcome. In this paper, the aim was to evaluate whether CR influences the motor outcome in patients after stroke treated with conventional or robotic therapy and whether it may influence one treatment rather than another., Methods: Seventy-five stroke patients were enrolled in five Italian neurological rehabilitation centres. Patients were assigned either to a robotic group, rehabilitation by means of robotic devices, or to a conventional group, where a traditional approach was used. Patients were evaluated at baseline and after rehabilitation treatment of 6 weeks through the Action Research Arm Test (ARAT), the Motricity Index (MI) and the Barthel Index (BI). CR was assessed at baseline using the Cognitive Reserve Index (CRI) questionnaire., Results: Considering all patients, a weak correlation was found between the CRI related to leisure time and MI evolution (r = 0.276; P = 0.02). Amongst the patients who performed a robotic rehabilitation, a moderate correlation emerged between the CRI related to working activities and MI evolution (r = 0.422; P = 0.02)., Conclusions: Our results suggest that CR may influence the motor outcome. For each patient, CR and its subcategories should be considered in the choice between conventional and robotic treatment., (© 2019 European Academy of Neurology.)

Background and Purpose: After stroke, only 12% of survivors obtain complete upper limb (UL) functional recovery, while in 30% to 60% UL deficits persist. Despite the complexity of the UL, prior robot-mediated therapy research has used only one robot in comparisons to conventional therapy. We evaluated the efficacy of robotic UL treatment using a set of 4 devices, compared with conventional therapy., Methods: In a multicenter, randomized controlled trial, 247 subjects with subacute stroke were assigned either to robotic (using a set of 4 devices) or to conventional treatment, each consisting of 30 sessions. Subjects were evaluated before and after treatment, with follow-up assessment after 3 months. The primary outcome measure was change from baseline in the Fugl-Meyer Assessment (FMA) score. Secondary outcome measures were selected to assess motor function, activities, and participation., Results: One hundred ninety subjects completed the posttreatment assessment, with a subset (n = 122) returning for follow-up evaluation. Mean FMA score improvement in the robotic group was 8.50 (confidence interval: 6.82 to 10.17), versus 8.57 (confidence interval: 6.97 to 10.18) in the conventional group, with no significant between-groups difference (adjusted mean difference -0.08, P = 0.948). Both groups also had similar change in secondary measures, except for the Motricity Index, with better results for the robotic group (adjusted mean difference 4.42, P = 0.037). At follow-up, subjects continued to improve with no between-groups differences., Discussion and Conclusions: Robotic treatment using a set of 4 devices significantly improved UL motor function, activities, and participation in subjects with subacute stroke to the same extent as a similar amount of conventional therapy. Video Abstract is available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A291).

Purpose: The aim of this review is to analyze haptic sensory substitution technologies for deaf, blind and deaf-blind individuals., Method: The literature search has been performed in Scopus, PubMed and Google Scholar databases using selected keywords, analyzing studies from 1960s to present. Search on databases for scientific publications has been accompanied by web search for commercial devices. Results have been classified by sensory disability and functionality, and analyzed by assistive technology. Complementary analyses have also been carried out on websites of public international agencies, such as the World Health Organization (WHO), and of associations representing sensory disabled persons., Results: The reviewed literature provides evidences that sensory substitution aids are able to mitigate in part the deficits in language learning, communication and navigation for deaf, blind and deaf-blind individuals, and that the tactile sense can be a means of communication to provide some kind of information to sensory disabled individuals., Conclusions: A lack of acceptance emerged from the discussion of capabilities and limitations of haptic assistive technologies. Future researches shall go towards miniaturized, custom-designed and low-cost haptic interfaces and integration with personal devices such as smartphones for a major diffusion of sensory aids among disabled. Implications for rehabilitation Systematic review of state of the art of haptic assistive technologies for vision and audition sensory disabilities. Sensory substitution systems for visual and hearing disabilities have a central role in the transmission of information for patients with sensory impairments, enabling users to interact with the not disabled community in daily activities. Visual and auditory inputs are converted in haptic feedback via different actuation technologies. The information is presented in the form of static or dynamic stimulation of the skin. Their effectiveness and ease of use make haptic sensory substitution systems suitable for patients with different levels of disabilities. They constitute a cheaper and less invasive alternative to implantable partial sensory restitution systems. Future researches are oriented towards the optimization of the stimulation parameters together with the development of miniaturized, custom-designed and low-cost aids operating in synergy in networks, aiming to increase patients' acceptability of these technologies.

We present a tactile telepresence system for real-time transmission of information about object stiffness to the human fingertips. Experimental tests were performed across two laboratories (Italy and Ireland). In the Italian laboratory, a mechatronic sensing platform indented different rubber samples. Information about rubber stiffness was converted into on-off events using a neuronal spiking model and sent to a vibrotactile glove in the Irish laboratory. Participants discriminated the variation of the stiffness of stimuli according to a two-alternative forced choice protocol. Stiffness discrimination was based on the variation of the temporal pattern of spikes generated during the indentation of the rubber samples. The results suggest that vibrotactile stimulation can effectively simulate surface stiffness when using neuronal spiking models to trigger vibrations in the haptic interface. Specifically, fractional variations of stiffness down to 0.67 were significantly discriminated with the developed neuromorphic haptic interface. This is a performance comparable, though slightly worse, to the threshold obtained in a benchmark experiment evaluating the same set of stimuli naturally with the own hand. Our paper presents a bioinspired method for delivering sensory feedback about object properties to human skin based on contingency-mimetic neuronal models, and can be useful for the design of high performance haptic devices., Competing Interests: Authors declare the owning of the patent of the development process of the encapsulated piezoelectric transducers (application no. 102016000002346) and of the mechatronic platform for the automatized indentation of tactile samples (application no. 102018000001108).

The integration of polymeric actuators in haptic displays is widespread nowadays, especially in virtual reality and rehabilitation applications. However, we are still far from optimizing the transducer ability in conveying sensory information. Here, we present a vibrotactile actuator characterized by a piezoelectric disk embedded in a polydimethylsiloxane (PDMS) shell. An original encapsulation technique was performed to provide the stiff active element with a compliant cover as an interface towards the soft human skin. The interface stiffness, together with the new geometry, generated an effective transmission of vibrotactile stimulation and made the encapsulated transducer a performant component for the development of wearable tactile displays. The mechanical behavior of the developed transducer was numerically modeled as a function of the driving voltage and frequency, and the exerted normal forces were experimentally measured with a load cell. The actuator was then tested for the integration in a haptic glove in single-finger and bi-finger condition, in a 2-AFC tactile stimulus recognition test. Psychophysical results across all the tested sensory conditions confirmed that the developed integrated haptic system was effective in delivering vibrotactile information when the frequency applied to the skin is within the 200⁻700 Hz range and the stimulus variation is larger than 100 Hz., Competing Interests: Authors declare the owning of the patent of the development process of the encapsulated piezoelectric transducer (application no. 102016000002346).

One of the crucial actions to be performed during a grasping task is to avoid slippage. The human hand can rapidly correct applied forces and prevent a grasped object from falling, thanks to its advanced tactile sensing. The same capability is hard to reproduce in artificial systems, such as robotic or prosthetic hands, where sensory motor coordination for force and slippage control is very limited. In this paper, a novel algorithm for slippage detection is presented. Based on fast, easy-to-perform processing, the proposed algorithm generates an ON/OFF signal relating to the presence/absence of slippage. The method can be applied either on the raw output of a force sensor or to its calibrated force signal, and yields comparable results if applied to both normal or tangential components. A biomimetic fingertip that integrates piezoresistive MEMS sensors was employed for evaluating the method performance. Each sensor had four units, thus providing 16 mono-axial signals for the analysis. A mechatronic platform was used to produce relative movement between the finger and the test surfaces (tactile stimuli ). Three surfaces with submillimetric periods were adopted for the method evaluation, and 10 experimental trials were performed per each surface. Results are illustrated in terms of slippage events detection and of latency between the slippage itself and its onset., Competing Interests: The authors declare no conflict of interest.

Introduction: Spasticity is a typical motor disorder in patients affected by stroke. Typically post-stroke rehabilitation consists of repetition of mobilization exercises on impaired limbs, aimed to reduce muscle hypertonia and mitigate spastic reflexes. It is currently strongly debated if the treatment's effectiveness improves with the timeliness of its adoption; in particular, starting intensive rehabilitation as close as possible to the stroke event may counteract the growth and postpone the onset of spasticity. In this paper we present a phase-II clinical validation of a robotic exoskeleton in treating subacute post-stroke patients. Methods: Seventeen post-stroke patients participated in 10 daily rehabilitation sessions using the NEUROExos Elbow Module exoskeleton, each one lasting 45 min: the exercises consisted of isokinetic passive mobilization of the elbow, with torque threshold to detect excessive user's resistance to the movement. We investigated the safety by reporting possible adverse events, such as mechanical, electrical or software failures of the device or injuries or pain experienced by the patient. As regards the efficacy , the Modified Ashworth Scale, was identified as primary outcome measure and the NEEM metrics describing elbow joint resistance to passive extension (i.e., maximum extension torque and zero-torque angle) as secondary outcomes. Results: During the entire duration of the treatments no failures or adverse events for the patients were reported. No statistically significant differences were found in the Modified Ashworth Scale scores, between pre-treatment and post-treatment and between post-treatment and follow-up sessions, indicating the absence of spasticity increase throughout (14 days) and after (3-4 months follow-up) the treatment. Exoskeleton metrics confirmed the absence of significant difference in between pre- and post-treatment data, whereas intra-session data highlighted significant differences in the secondary outcomes, toward a decrease of the subject's joint resistance. Conclusions: The results show that our robotic exoskeleton can be safely used for prolonged sessions in post-stroke and suggest that intensive early rehabilitation treatment may prevent the occurrence of spasticity at a later stage. Moreover, the NEEM metrics were found to be reliable compared to the Modified Ashworth Scale and sensitive to revealing intra-session changes of elbow resistance to passive extension, in agreement with clinical evidences.

The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.