An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications.

This work presents an overview of the main strategies that have been proposed for non-invasive monitoring of heart rate (HR) in extramural and home settings. We discuss three categories of sensing according to what physiological effect is used to measure the pulsatile activity of the heart, and we focus on an illustrative sensing modality for each of them. Therefore, electrocardiography, photoplethysmography, and mechanocardiography are presented as illustrative modalities to sense electrical activity, mechanical activity, and the peripheral effect of heart activity. In this paper, we describe the physical principles underlying the three categories and the characteristics of the different types of sensors that belong to each class, and we touch upon the most used software strategies that are currently adopted to effectively and reliably extract HR. In addition, we investigate the strengths and weaknesses of each category linked to the different applications in order to provide the reader with guidelines for selecting the most suitable solution according to the requirements and constraints of the application.

Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes.

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes.

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0-20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.

Changes in leg cycling muscle synergies after training augmented by functional electrical stimulation in subacute stroke survivors: a pilot study.

BACKGROUND: Muscle synergies analysis can provide a deep understanding of motor impairment after stroke and of changes after rehabilitation. In this study, the neuro-mechanical analysis of leg cycling was used to longitudinally investigate the motor recovery process coupled with cycling training augmented by Functional Electrical Stimulation (FES) in subacute stroke survivors. METHODS: Subjects with ischemic subacute stroke participated in a 3-week training of FES-cycling with visual biofeedback plus usual care. Participants were evaluated before and after the intervention through clinical scales, gait spatio-temporal parameters derived from an instrumented mat, and a voluntary pedaling test. Biomechanical metrics (work produced by the two legs, mechanical effectiveness and symmetry indexes) and bilateral electromyography from 9 leg muscles were acquired during the voluntary pedaling test. To extract muscles synergies, the Weighted Nonnegative Matrix Factorization algorithm was applied to the normalized EMG envelopes. Synergy complexity was measured by the number of synergies required to explain more than 90% of the total variance of the normalized EMG envelopes and variance accounted for by one synergy. Regardless the inter-subject differences in the number of extracted synergies, 4 synergies were extracted from each patient and the cosine-similarity between patients and healthy weight vectors was computed. RESULTS: Nine patients (median age of 75 years and median time post-stroke of 2 weeks) were recruited. Significant improvements in terms of clinical scales, gait parameters and work produced by the affected leg were obtained after training. Synergy complexity well correlated to the level of motor impairment at baseline, but it did not change after training. We found a significant improvement in the similarity of the synergy responsible of the knee flexion during the pulling phase of the pedaling cycle, which was the mostly compromised at baseline. This improvement may indicate the re-learning of a more physiological motor strategy. CONCLUSIONS: Our findings support the use of the neuro-mechanical analysis of cycling as a method to assess motor recovery after stroke, mainly in an early phase, when gait evaluation is not yet possible. The improvement in the modular coordination of pedaling correlated with the improvement in motor functions and walking ability achieved at the end of the intervention support the role of FES-cycling in enhancing motor re-learning after stroke but need to be confirmed in a controlled study with a larger sample size. TRIAL REGISTRATION: ClinicalTrial.gov, NCT02439515. Registered on May 8, 2015, .

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography.

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

Increasing accuracy of pulse arrival time estimation in low frequency recordings.

Objective.Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz.Approach.The method applies template matching to leverage the random nature of sampling time and expected change in the PAT.Main results.The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively.Significance.This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings.

Optimizing wearable single-channel EEG sleep staging in a heterogeneous sleep-disordered population using transfer learning.

STUDY OBJECTIVES: While various wearable EEG devices have been developed, performance evaluation of the devices and their associated automated sleep stage classification models is mostly limited to healthy subjects. A major barrier for applying automated wearable EEG sleep staging in clinical populations is the need for large-scale data for model training. We therefore investigated transfer learning as strategy to overcome limited data availability and optimize automated single-channel EEG sleep staging in people with sleep disorders. METHODS: We acquired 52 single-channel frontopolar headband EEG recordings from a heterogeneous sleep-disordered population with concurrent PSG. We compared three model training strategies: 'pre-training' (i.e., training on a larger dataset of 901 conventional PSGs), 'training-from-scratch' (i.e., training on wearable headband recordings), and 'fine-tuning' (i.e., training on conventional PSGs, followed by training on headband recordings). Performance was evaluated on all headband recordings using 10-fold cross-validation. RESULTS: Highest performance for 5-stage classification was achieved with fine-tuning (κ = .778), significantly higher than with pre-training (κ = .769) and with training-from-scratch (κ = .733). No significant differences or systematic biases were observed with clinically relevant sleep parameters derived from PSG. All sleep disorder categories showed comparable performance. CONCLUSIONS: This study emphasizes the importance of leveraging larger available datasets through deep transfer learning to optimize performance with limited data availability. Findings indicate strong similarity in data characteristics between conventional PSG and headband recordings. Altogether, results suggest the combination of the headband, classification model, and training methodology can be viable for sleep monitoring in the heterogeneous clinical population.

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation.

OBJECTIVE: Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. METHODS: We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. RESULTS: Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configurat- ion is recognized in 92.2% of the subjects. CONCLUSION: The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. SIGNIFICANCE: The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.

Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches.

Introduction: Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice. However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). Methods: We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N = 94). Performance of the different training strategies was evaluated using Cohen's Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N = 60), patients with obstructive sleep apnea (OSA, N = 60), insomnia (N = 60), and REM sleep behavioral disorder (RBD, N = 22), combined with two EEG channels, F3-M2 and F3-F4. Results: No differences in performance between the training strategies was observed in the age-matched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average. Discussion: We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training.

A multimodal training with visual biofeedback in subacute stroke survivors: a randomized controlled trial.

BACKGROUND: Early interventions maximizing patient's involvement are essential to promote gait restoration and motor recovery after stroke. AIM: The aim of this study is to evaluate the effects of a multimodal biofeedback training involving cycling augmented by functional electrical stimulation (FES) and balance exercises on walking ability and motor recovery. DESIGN: Randomized controlled trial (NCT02439515). SETTING: Inpatient rehabilitation facility. POPULATION: Subacute stroke survivors (less than 6 months from the first event) aged up to 90 years old. METHODS: Sixty-eight participants were randomly allocated to an experimental group, performing 15 sessions of biofeedback FES-cycling training followed by 15 sessions of biofeedback balance training (20 minutes each) in addition to usual care (70 minutes), and a control group performing 30 sessions (90 minutes) of usual care. Participants were evaluated before training, after 15 sessions, after 30 sessions, and at 6-month follow-up through: gait speed (primary outcome), spatiotemporal gait parameters, Six-Minute Walking Test, Functional Independence Measure, Motricity Index, Trunk Control Test, Berg Balance Scale, and Fall Efficacy Scale. RESULTS: Both groups significantly improved over time, but no group and interaction effects were found for any outcomes. The 73% of the experimental group achieved a clinically meaningful change in gait speed compared to the 38% of the control group (P=0.048). These percentages were even more unbalanced for patients with a moderate to severe gait impairment at baseline (91% versus 36%; P=0.008). CONCLUSIONS: The multimodal biofeedback training was not statistically superior to usual care, showing only a positive trend in favor of the experimental group on locomotion recovery. Patients initially not able to walk might be the best candidates for such a training. CLINICAL REHABILITATION IMPACT: The multimodal biofeedback training is a task-specific, repetitive and intensive training requiring a minimal supervision, which might result in a lower staff to patient ratio if organized in group sessions. Therefore, it can represent a good alternative for early stroke rehabilitation.

Motor Improvement in Adolescents Affected by Ataxia Secondary to Acquired Brain Injury: A Pilot Study.

AIM: To assess changes in locomotion and balance in adolescents affected by ataxia secondary to acquired brain injury after a rehabilitation treatment with physiotherapy and the Gait Real-time Analysis Interactive Lab (GRAIL), an immersive virtual reality platform. METHODS: 11 ataxic adolescents (16(5) years old, 4.7(6.7) years from injury) underwent 20 45-minute sessions with GRAIL plus 20 45-minute sessions of physiotherapy in one month. Patients were assessed before and after rehabilitation with functional scales and three-dimensional multiple-step gait analysis. RESULTS: Results showed significant improvements in ataxia score assessed by the Scale for the Assessment and Rating of Ataxia, in dimension D and E of Gross Motor Function Measure, in walking endurance and in balance abilities. Moreover, the training fostered significant changes at hip, knee, and ankle joints, and the decrease of gait variability, toward healthy references. INTERPRETATION: In spite of the pilot nature of the study, data suggest that training with immersive virtual reality and physiotherapy is a promising approach for ataxic gait rehabilitation, even in chronic conditions.

Psychometric properties of the Italian version of the Cold Intolerance Symptom Severity questionnaire in upper-extremity nerve repair.

BACKGROUND: In upper-limb peripheral nerve injured patients, cold intolerance is the most bothersome, prolonged and disabling symptom, negatively affecting work and leisure activities. The Cold Intolerance Symptom Severity (CISS) questionnaire is widely used to assess this symptom, though its psychometric properties have not been examined in depth and no validated Italian version exists. AIM: The aim of this study was to examine in depth the psychometric properties of the Italian version of CISS (CISS-It) in a sample of patients with upper-limb peripheral nerve injury. DESIGN: Prospective cross-sectional. SETTING: Outpatient. POPULATION: Seventy-two subjects with upper-limb peripheral nerve injury consecutively admitted for outpatient assessment and rehabilitation. METHODS: We assessed dimensionality, reliability, validity, and responsiveness (minimum detectable change, MDC) of the CISS-It by means of Classical Test Theory methods. RESULTS: Factor analysis confirmed the scale's unidimensionality. Internal consistency (alpha=0.93) and test-retest reliability (ICC2,1 =0.96) were high. The convergent validity of the CISS-It was demonstrated by its correlations with the 4-item Douleur Neuropathique (DN4) Scale (rs=0.73), pain score (rs=0.61), and Medical Research Council (MRC) Scale for sensory function (rs=-0.44). The MDC95 was 11.30 points. CONCLUSIONS: CISS-It is a reliable, valid and easy-to-use questionnaire for measuring cold intolerance in subjects with upper-limb peripheral nerve injury. However, there is room for some refinement of the CISS structure and wording, which we suggest to perform within the framework of modern statistical methods (such as Rasch analysis), in order to optimize content coverage and technical quality of the measurement. CLINICAL REHABILITATION IMPACT: This examination in depth of the psychometric properties of the CISS further increases confidence in the scale's use for clinical assessment and monitoring of abnormal cold sensitivity in the rehabilitation of patients with upper extremity peripheral nerve injury.

Movement Velocity and Fluidity Improve after Armeo®Spring Rehabilitation in Children Affected by Acquired and Congenital Brain Diseases: An Observational Study.

BACKGROUND: Children with cerebral palsy (CP) and acquired brain injury (ABI) often exhibit upper limb impairment, with repercussions in their daily activities. Robotic rehabilitation may promote their functional recovery, but evidence of its effectiveness is often based on qualitative functional scales. The primary aim of the present work was to assess movement precision, velocity, and smoothness using numerical indices from the endpoint trajectory of Armeo®Spring. Secondly, an investigation of the effectiveness of robotic rehabilitation in CP and ABI children was performed. METHODS: Upper limb functional changes were evaluated in children with CP (N=21) or ABI (N=22) treated with Armeo®Spring (20 45-minute sessions over 4 weeks) using clinical scales and numerical indices computed from the exoskeleton trajectory. RESULTS: Functional scales (i.e., QUEST and Melbourne) were sensitive to changes produced by the treatment for the whole study group and for the two etiology-based subgroups (improvements above Minimal Clinically Importance Difference). Significant improvement was also observed in terms of velocity, fluidity, and precision of the movement through the numerical indices of kinematic performance. Differences in the temporal evolution of the motor outcome were highlighted between the ABI and CP subgroups, pointing toward adopting different rehabilitative protocols in these two populations. CONCLUSIONS: Robot-assisted upper limb rehabilitation seems to be a promising tool to promote and assess rehabilitation in children affected by acquired and congenital brain diseases.

Assessment of Malnutrition in Community-dwelling Elderly People: Cooperation among General Practitioners and Public Health.

BACKGROUND: Malnutrition, a very common condition in the elderly, is known to increase their vulnerability to adverse health events. This study aimed to estimate the prevalence of malnutrition in the over 75-yr-old community-dwelling population in the "Chiavarese" Local Health Unit district (North West Italy). METHODS: The short version of the Mini Nutritional Assessment (MNA-SF) was used by General Practitioners (GP) as a screening tool to investigate the nutritional status of elderly people (1039 subjects). The study was conducted in 2012-2013 in the Local Health Unit of Chiavari (Liguria Region), Italy. The malnutrition was subsequently confirmed by means of biochemical parameters. Subjects at risk of malnutrition or malnourished (n=22) received personalized dietary counseling by the GP. RESULTS: The MNA-SF recognized 21% of the elderly people being at risk of malnutrition and biochemical tests confirmed a malnutrition prevalence of 3.5%. The dietary counseling improved the MNA-SF score and biochemical parameters, but the difference was statistically significant only for the MNA-SF score (P=0.00613). CONCLUSION: Malnutrition can be evaluated with a simple tool such as the MNA-SF, recognized at the earliest stage and successfully treated by the GP.

Effects of dose and duration of Robot-Assisted Gait Training on walking ability of children affected by cerebral palsy.

BACKGROUND: Robot-Assisted Gait Training (RAGT) is a widespread approach for locomotion rehabilitation but information about intervention frequency and duration is still lacking. OBJECTIVE: To evaluate the effect of frequency and duration of a RAGT on motor outcome of children affected by Cerebral Palsy (CP). METHODS: Forty-four CP children (age 4-17) underwent one among four different intensive trainings with equal dose of intervention, combining Task-Oriented Physiotherapy (TOP) and RAGT: 40 sessions (4 sessions/week) over 10 weeks of sole TOP (group1) or RAGT (group2) or RAGT and TOP (2 + 2 sessions/week; group3); 40 sessions in shorter period (4 weeks) of RAGT and TOP (5 + 5 sessions/week; group4). Each child was assessed before, after the training and after 3 months with: Ashworth, gross motor function measure (GMFM)-88, GMFM-66, six minutes walking test and gait analysis. RESULTS: No differences among the 4 protocols were highlighted although both groups with exclusive physiotherapy and RAGT obtained significant improvements in GMFM-88, GMFM-E and GMFM-66 while the mixed approaches did not show significant changes. CONCLUSION: Single-treatment approaches seem to be more effective than mixed approaches, independently from the duration (4 or 10 weeks). RAGT seems to have similar effect with respect to the traditional TOP, at least over 10 weeks.

Intra and inter-session reliability of rapid Transcranial Magnetic Stimulation stimulus-response curves of tibialis anterior muscle in healthy older adults.

OBJECTIVE: The clinical use of Transcranial Magnetic Stimulation (TMS) as a technique to assess corticospinal excitability is limited by the time for data acquisition and the measurement variability. This study aimed at evaluating the reliability of Stimulus-Response (SR) curves acquired with a recently proposed rapid protocol on tibialis anterior muscle of healthy older adults. METHODS: Twenty-four neurologically-intact adults (age:55-75 years) were recruited for this test-retest study. During each session, six SR curves, 3 at rest and 3 during isometric muscle contractions at 5% of maximum voluntary contraction (MVC), were acquired. Motor Evoked Potentials (MEPs) were normalized to the maximum peripherally evoked response; the coil position and orientation were monitored with an optical tracking system. Intra- and inter-session reliability of motor threshold (MT), area under the curve (AURC), MEPmax, stimulation intensity at which the MEP is mid-way between MEPmax and MEPmin (I50), slope in I50, MEP latency, and silent period (SP) were assessed in terms of Standard Error of Measurement (SEM), relative SEM, Minimum Detectable Change (MDC), and Intraclass Correlation Coefficient (ICC). RESULTS: The relative SEM was ≤10% for MT, I50, latency and SP both at rest and 5%MVC, while it ranged between 11% and 37% for AURC, MEPmax, and slope. MDC values were overall quite large; e.g., MT required a change of 12%MSO at rest and 10%MSO at 5%MVC to be considered a real change. Inter-sessions ICC were >0.6 for all measures but slope at rest and MEPmax and latency at 5%MVC. CONCLUSIONS: Measures derived from SR curves acquired in <4 minutes are affected by similar measurement errors to those found with long-lasting protocols, suggesting that the rapid method is at least as reliable as the traditional methods. As specifically designed to include older adults, this study provides normative data for future studies involving older neurological patients (e.g. stroke survivors).

Can new technologies improve upper limb performance in grown-up diplegic children?

BACKGROUND: Few systematic studies describe rehabilitation trainings for upper limb in diplegic children with cerebral palsy (CP), who - especially once grown up - are often not considered as a target for rehabilitation interventions. AIM: In this pilot study, we describe the details and the effectiveness of an intensive, technology assisted intervention for upper limb. SETTING: The treatment combines the utilization of Armeo® Spring with a training focused on hand/finger fluency and dexterity in a pre-post treatment experimental design. POPULATION: Participants were ten school-aged children (mean age 11.2) with bilateral CP and diplegia, attending mainstream schools. METHODS: Participants underwent 40 therapy sessions in four weeks. Armeo® Spring measures, standardized motor and perceptual outcome indexes, as well as everyday life indicators were utilized to assess the effect of the intervention. RESULTS: Upper limb coordination, fluency and quality of movements mainly of hands and fingers significantly improved, with a good transferability to everyday life also in areas not specifically trained, such as self-care abilities and mobility. Probably due to the visual feedback provided by the virtual reality setting (which was all in one the context, the incentive and the product of activities), perceptual abilities significantly improved, too. CONCLUSIONS: Our study suggests the importance of intervention on upper limb even in milder CP diplegic forms and in relatively grown-up children. The possibility of modification at least partially relies on learning processes that are active all along development and benefit from stimulation. CLINICAL REHABILITATION IMPACT: Though further studies with control groups and follow-up perspective are needed to confirm, new technologies offer interesting possibilities to be integrated into new evidence-based rehabilitation models.

Can FES-Augmented Active Cycling Training Improve Locomotion in Post-Acute Elderly Stroke Patients?

Recent studies advocated the use of active cycling coupled with functional electrical stimulation to induce neuroplasticity and enhance functional improvements in stroke adult patients. The aim of this work was to evaluate whether the benefits induced by such a treatment are superior to standard physiotherapy. A single-blinded randomized controlled trial has been performed on post-acute elderly stroke patients. Patients underwent FES-augmented cycling training combined with voluntary pedaling or standard physiotherapy. The intervention consisted of fifteen 30-minutes sessions carried out within 3 weeks. Patients were evaluated before and after training, through functional scales, gait analysis and a voluntary pedaling test. Results were compared with an age-matched healthy group. Sixteen patients completed the training. After treatment, a general improvement of all clinical scales was obtained for both groups. Only the mechanical efficiency highlighted a group effect in favor of the experimental group. Although a group effect was not found for any other cycling or gait parameters, the experimental group showed a higher percentage of change with respect to the control group (e.g. the gait velocity was improved of 35.4% and 25.4% respectively, and its variation over time was higher than minimal clinical difference for the experimental group only). This trend suggests that differences in terms of motor recovery between the two groups may be achieved increasing the training dose. In conclusion, this study, although preliminary, showed that FES-augmented active cycling training seems to be effective in improving cycling and walking ability in post-acute elderly stroke patients. A higher sample size is required to confirm results.