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Background: Balance impairment is a common disability in post-stroke survivors, leading to reduced mobility and increased fall risk. Robotic gait training (RAGT) is largely used, along with traditional training. There is, however, no strong evidence about RAGT superiority, especially on balance. This study aims to determine RAGT efficacy on balance of post-stroke survivors. Methods: PubMed, Cochrane Library, and PeDRO databases were investigated. Randomized clinical trials evaluating RAGT efficacy on post-stroke survivor balance with Berg Balance Scale (BBS) or Timed Up and Go test (TUG) were searched. Meta-regression analyses were performed, considering weekly sessions, single-session duration, and robotic device used. Results: A total of 18 trials have been included. BBS pre-post treatment mean difference is higher in RAGT-treated patients, with a pMD of 2.17 (95% CI 0.79; 3.55). TUG pre-post mean difference is in favor of RAGT, but not statistically, with a pMD of −0.62 (95%CI − 3.66; 2.43). Meta-regression analyses showed no relevant association, except for TUG and treatment duration (β = −1.019, 95% CI − 1.827; −0.210, p-value = 0.0135). Conclusions: RAGT efficacy is equal to traditional therapy, while the combination of the two seems to lead to better outcomes than each individually performed. Robot-assisted balance training should be the focus of experimentation in the following years, given the great results in the first available trials. Given the massive heterogeneity of included patients, trials with more strict inclusion criteria (especially time from stroke) must be performed to finally define if and when RAGT is superior to traditional therapy.

The genetic basis of autosomal-recessive dystonia remains poorly understood. Our objective was to report identification of additional individuals with variants in AOPEP, a recently described gene for recessively inherited dystonic disorders (OMIM:619565).Ongoing analysis on a high-throughput genetic platform and international case-recruitment efforts were undertaken.Novel biallelic, likely pathogenic loss-of-function alleles were identified in two pedigrees of different ethnic background. Two members of a consanguineous Iranian family shared a homozygous c.1917-1GA essential splice-site variant and featured presentations of adolescence-onset generalized dystonia. An individual of Chinese descent, homozygous for the nonsense variant c.1909GT (p.Glu637*), displayed childhood-onset generalized dystonia combined with later-manifesting parkinsonism. One additional Iranian patient with adolescence-onset generalized dystonia carried an ultrarare, likely protein-damaging homozygous missense variant (c.1201CT [p.Arg401Trp]).These findings support the implication of AOPEP in recessive forms of generalized dystonia and dystonia-parkinsonism. Biallelic AOPEP variants represent a worldwide cause of dystonic movement-disorder phenotypes and should be considered in dystonia molecular testing approaches.

The COVID-19 pandemic has been a stress test for the population, especially for people with chronic disorders such as Parkinson’s disease (PD). In addition to public health restrictions that contrast with PD management recommendations, there were deep changes in health care delivery. This retrospective study evaluates the impact of COVID-19 on the clinical and functional evolution of a cohort of 221 PD patients consecutively referred to the Movement Disorders Center between 2018 and 2021. We analyzed the trend in motor and non-motor symptoms and functional status across years based on the Unified Parkinson’s Disease Rating Scale (UPDRS) and Non-Motor Symptom Scale (NMSS). We also compared the number of emerging complications, neurologic visits, and rehabilitation sessions per subject per year. In 2020, all primary endpoint measures worsened compared to 2019, without age, disease duration, or greater neurologic impairment explaining this outcome. Concurrently, the percentage of patients receiving neurologic visits or rehabilitation sessions reduced by 53% and 58%, respectively. The subgroup analysis of 167 subjects revealed that those who received at least one cycle of rehabilitation sessions in 2020 maintained their independence level. These findings lead to emphasizing the importance of regular monitoring and rehabilitation delivery in people with chronic neurological disorders.

Background Healthy lifestyle, including caloric restriction, balanced diet and physical activity, is important in primary and secondary prevention of breast cancer (BC). It is known that Mediterranean diet reduces metabolic syndrome and insulin resistance that are associated with increased risk of BC onset and recurrence. Physical activity decreases BMI, blood concentrations of testosterone, estrogens, insulin, its resistance and strengthens anti-inflammatory pathways against tumor cells. Since January 2014, at our Institution we promoted a project named “Lifestyle Program” for high risk BC patients underwent to primary surgery. Here we presented the results of 12 months of “Lifestyle Program”. Patients and methods Since January 2014 we have prospectively enrolled all high risk patients between 18 and 70 years treated to our department for invasive early-stage breast cancer (stage I-III). High risk has been defined by one or more of the following inclusion criteria: body mass index (BMI) > 25, diagnosis of metabolic syndrome, increased level of blood testosterone and/or insulin. All high risk patients receive a periodical personalized educational intervention by a physiatrist for physical activity and by a nutritionist for a mediterranean diet low in animals fat and enriched of fibers, fruits and vegetables. All patients underwent to screening for anxiety and depression through HADS questionnaire scores. All data were analyzed by Chi-square test assuming statistical significance at p25 in 94.9% of pts at baseline vs 63.2% after 12 months of lifestyle; p=110 mg/dl in 23.5% of pts at baseline vs 10.2% at 12 months; p=27 uU/ml in 20.6% of pts at baseline vs 2.9% after 12 months; p1,2 ng/ml in 17.6% of pts at baseline vs 4.1% at 12 months; p200 mg/dl in 46.9% of pts at baseline vs 35.7% at 12 months; p170 mg/dl in 13.3% of pts at baseline vs 10.2% at 12 months; p Citation Format: Mirco Pistelli, Valentina Natalucci, Lucia Bastianelli, Laura Scortichini, Veronica Agostinelli, Filippo Merloni, Agnese Savini, Marianna Capecci, Maria Gabriella Ceravolo, Roberta Serrani, Maurizio Ricci, Marina Taus, Albano Nicolai, Elena Barbieri, Rossana Berardi. Assessing the impact of 12 months lifestyle interventions on breast cancer secondary prevention: A modeling approach [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD11-03.

Given the benefits of physical activity for breast cancer survivals, this pilot study aims to assess the feasibility of the MOTIVE program at achieving and maintaining the recommended physical activity level in women diagnosed and treated breast cancer, over 16 weeks. We conduct a pilot-controlled study of 20 women diagnosed with breast cancer stage I, II or IIIa. In this study, women of Intervention Arm (n = 10) received the MOTIVE program. This group was compared to women of Control Arm (n = 10) who received only counselling. Health-related fitness measures, and quality of life were assessed at baseline (t0) and after 4 (t1), 8 (t2) and 16 (t3) weeks. Intervention Arm women reached the recommended physical activity guidelines at t1 and t2 (eff.size = 1.9 [1.0–3.1]), and 90% continued to be active, autonomously, at t3 (eff.size = 1.12 [0.21–2.12]). Intervention Arm participants’ arm strength, fitness levels and quality of life also improved over time. No significant improvements in outcome measures were observed in Control Arm participants. These results are encouraging and suggest that the MOTIVE program may be a viable, well tolerated and effective option to help breast cancer women reaching a stable physical activity level over time, which meets prevention-related goals., Physical activity; Adherence; Breast cancer; Aerobic exercise; Strength training; Secondary/tertiary prevention; Lifestyle; Public health.

Background Although gait disorders strongly contribute to perceived disability in people with Parkinson's disease, clinical trials have failed to identify which task-oriented gait training method can provide the best benefit. Freezing of gait remains one of the least investigated and most troublesome symptoms. Objective We aimed to compare the effects of robot-assisted gait training and treadmill training on endurance and gait capacity in people with Parkinson disease; the secondary aim was to compare the effect of the treatments in people with freezing and/or severe gait disability and assess changes in overall disease-related disability and quality of life. Methods Outpatients with Parkinson disease (Hoehn and Yahr stage ≥ 2) were randomly assigned to receive 20 sessions of 45-min gait training assisted by an end-effector robotic device (G-EO System) or treadmill training. Outcome assessments were the 6-min walk test, Timed Up and Go test, Freezing of Gait Questionnaire, Unified Parkinson's Disease Rating Scales and Parkinson's Disease Quality of Life Questionnaire-39 administered before (T0) and after treatment (T1). Results We included 96 individuals with Parkinson disease: 48 with robot-assisted gait training and 48 treadmill training. Both groups showed significant improvement in all outcomes. As compared with baseline, with robot-assisted gait training and treadmill training, endurance and gait capacity were enhanced by 18% and 12%, respectively, and motor symptoms and quality of life were improved by 17% and 15%. The maximum advantage was observed with the Freezing of Gait Questionnaire score, which decreased by 20% after either treatment. On post-hoc analysis, dependent walkers benefited more than independent walkers from any gait training, whereas freezers gained more from robot-assisted than treadmill training in terms of freezing reduction. Conclusions Repetitive intensive gait training is an effective treatment for people with Parkinson disease and can increase endurance and gait velocity, especially for those with severe walking disability. Advantages are greater with robot-assisted gait training than treadmill training for individuals with freezing of gait – related disability.

Introduction The overall frequency of postural abnormalities (PA) in Parkinson's disease (PD) is unknown. We evaluated the overall prevalence of PA and assessed the association with demographic and clinical variables. Methods For this multicenter, cross-sectional study, consecutive PD outpatients attending 7 tertiary Italian centers were enrolled. Patients were evaluated and compared for the presence of isolated PA such as camptocormia, Pisa syndrome, and anterocollis and for combined forms (ie, camptocormia + Pisa syndrome) together with demographic and clinical variables. Results Of the total 811 PD patients enrolled, 174 (21.5%; 95% confidence interval [CI], 18.6%-24.3%) presented PA, 144 of which had isolated PA and 30 had combined PA. The prevalence of camptocormia was 11.2% (95% CI, 9%-13.3%), Pisa syndrome 8% (95% CI, 6.2%-9.9%), and anterocollis 6.5% (95% CI, 4.9%-8.3%). Patients with PA were more often male and older with longer disease duration, more advanced disease stage, more severe PD symptoms, a bradykinetic/rigid phenotype, and poorer quality of life. They were initially treated with levodopa, and more likely to be treated with a combination of levodopa and dopamine agonist, took a higher daily levodopa equivalent daily dose, and had more comorbidities. Falls and back pain were more frequent in PD patients with PA than in those without PA. Multiple logistic regression models confirmed an association between PA and male gender, older age, Hoehn and Yahr stage, and total Unified Parkinson's Disease Rating Scale score. Conclusions PA are frequent and disabling complications in PD, especially in the advanced disease stages.

Background Deep brain stimulation (DBS) is a widely used surgical procedure for the treatment of patients with drug resistant epilepsy (DRE) and several anatomical target have been described. Indications for DBS includes patients with focal, partial seizure and those for which resective or disconnective surgery are contraindicated, such as involvement of eloquent cortex or significant comorbidities. Despite the SANTE trial has clearly indicated the efficacy of DBS of anterior nucleus of the thalamus (ANT), specific indications regarding the best anatomical target and outcome in patients with severe disability are lacking. Here we described our case series of patients underwent DBS of three different target including ANT, centromedian thalamic nucleus (CMN) and subthalamic nucleus (STN). Method Six patients with DRE have been treated with DBS of ANT (n = 3), STN (n = 2) and CMN (n = 1). Outcome has been expressed as seizures frequency reduction and patients functional status after surgery with a follow-up of 5–11 years. Results Four out of six patients show no reduction of seizures frequency after DBS implant with one case of increasing atypical absence. Two cases, one ANT and one CMN, show a significant reduction of seizures frequency of 50–60%. No patients improve relative to functional outcome and one showed psychiatric symptoms worsening. Conclusions For patients with DRE and severe functional disability, DBS may reduce seizure frequency in some cases, but it does not improve functional outcome.

Introduction Postural instability is a cardinal feature of Parkinson's disease, together with rest tremor, rigidity and bradykinesia. It is a highly disabling symptom that becomes increasingly common with disease progression and represents a major source of reduced quality of life in patients with Parkinson's disease. Rehabilitation aims to enable patients with Parkinson's disease to maintain their maximum level of mobility, activity and independence. To date, a wide range of rehabilitation approaches has been employed to treat postural instability in Parkinson's disease, including robotic training. Our main aim was to conduct a systematic review of current literature about the effects of robot-assisted gait training on postural instability in patients with Parkinson's disease. Evidence acquisition A systematic search using the following MeSH terms "Parkinson disease," "postural balance," "robotics," "rehabilitation" AND string "robotics [mh]" OR "robot-assisted" OR "electromechanical" AND "rehabilitation [mh]" OR "training" AND "postural balance [mh]" was conducted on PubMed, Cochrane Library and Pedro electronic databases. Full text articles in English published up to December 2020 were included. Data about patient characteristics, robotic devices, treatment procedures and outcome measures were considered. Every included article got checked for quality. Level of evidence was defined for all studies. Evidence synthesis Three authors independently extracted and verified data. In total, 18 articles (2 systematic reviews, 9 randomized controlled trials, 4 uncontrolled studies and 3 case series/case reports) were included. Both end-effector and exoskeleton devices were investigated as to robot-assisted gait training modalities. No clear relationship between treatment parameters and clinical conditions was observed. We found a high level of evidence about the effects of robot-assisted gait training on balance and freezing of gait in patients with Parkinson's disease. Conclusions This systematic review provides to the reader a complete overview of current literature and levels of evidence about the effects of robot-assisted gait training on postural instability issues (static and dynamic balance, freezing of gait, falls, confidence in activities of daily living and gait parameters related to balance skills) in patients with Parkinson's disease.

Objectives To explore the impact of sex and age on relationship between prodromal constipation and disease phenotype in Parkinson’s disease at early stages. Methods A total of 385 Parkinson’s disease patients from the PRIAMO study were classified according to the presence of prodromal constipation and followed for 24 months. Multivariable mixed-effect models were applied. All analyses were performed separately for sex (64.1% men) and median age (different by sex: 67 years-old in men and 68 years-old in women). Results As for sex, prodromal constipation was associated with greater odds of attention/memory complaints and apathy symptoms in women only. As for age, prodromal constipation was associated with lower cognitive and higher apathy scores in older patients only. Conclusions Prodromal constipation anticipates lower cognitive performances and more severe apathy since the earliest stages in women and older patients. Sex- and age-related heterogeneity of prodromal markers of Parkinson’s disease may impact disease phenotype.

COVID-19 pandemic is creating collateral damage to outpatients, whose rehabilitation services have been disrupted in most of the European countries. Telemedicine has been advocated as a possible solution. This paper reports the contents of the third Italian Society of Physical and Rehabilitation Medicine (SIMFER) webinar on "experiences from the field" COVID-19 impact on rehabilitation ("Covinars"). It provides readily available, first-hand information about the application of telemedicine in rehabilitation. The experiences reported were very different for population (number and health conditions), interventions, professionals, service payment, and technologies used. Commonalities included the pushing need due to the emergency, previous experiences, and a dynamic research and innovation environment. Lights included feasibility, results, reduction of isolation, cost decrease, stimulation to innovation, satisfaction of patients, families, and professionals beyond the starting diffidence. Shadows included that telemedicine can integrate but will never substitute face-to-face rehabilitation base on the encounter among human beings; age, and technology barriers (devices absence, bad connection and human diffidence) have also been reported. Possible issues included privacy and informed consent, payments, cultural difficulties in understanding that telemedicine is a real rehabilitation intervention. There was a final agreement that this experience will be incorporated by participants in their future services: technology is ready, but the real challenge is to change PRM physicians' and patients' habits, while better specific regulation is warranted.

No neurological disorder other than Parkinson’s disease (PD) can boast so many revolutionary events, in its history. The treatment of choice has primarily been the dopamine precursor levodopa. After its introduction, the impact of disease on mortality significantly decreased, yet affected people had to face severe drug-related side effects. Around 40 years ago, other symptomatic drugs, including monoamine oxidase type B (MAO-B) inhibitors and dopamine agonists (DA), used as first-line therapy in de novo patients, were reportedly effective at preventing the levo-Dopa long-term treatment syndrome. Afterwards, catechol-O-methyltransferase inhibitors and long-release DA were added to the toolkit, as a means to obtain a steady control on motor symptoms, but their effectiveness was overtaken, 30 years ago, by functional neurosurgery that ensured brilliant results with the bilateral subthalamic deep brain stimulation (DBS) technique. Notwithstanding such care improvement, people with PD still experienced long-lasting ever-growing disability. Over the last 10 years, evidence on the efficacy of motor training has accumulated, supporting the role of exercise for promoting activity-dependent neuroplasticity, thus opening a new perspective on non-pharmacological disease modifying interventions.

Background: Postural abnormalities in Parkinson's disease (PD) form a spectrum of functional trunk misalignment, ranging from a “typical” parkinsonian stooped posture to progressively greater degrees of spine deviation. Objective: To analyze the association between degree of postural abnormalities and disability and to determine cut-off values of trunk bending associated with limitations in activities of daily living (ADLs), motor impairment, falls, and back pain. Methods: The study population was 283 PD patients with ≥5° of forward trunk bending (FTB), lateral trunk bending (LTB) or forward neck bending (FNB). The degrees were calculated using a wall goniometer (WG) and software-based measurements (SBM). Logistic regression models were used to identify the degree of bending associated with moderate/severe limitation in ADLs (Movement Disorders Society Unified PD Rating Scale [MDS-UPDRS] part II ≥17), moderate/severe motor impairment (MDS-UPDRS part III ≥33), history of falls (≥1), and moderate/severe back pain intensity (numeric rating scale ≥4). The optimal cut-off was identified using receiver operating characteristic (ROC) curves. Results: We found significant associations between modified Hoehn & Yahr stage, disease duration, sex, and limitation in ADLs, motor impairment, back pain intensity, and history of falls. Degree of trunk bending was associated only with motor impairment in LTB (odds ratio [OR] 1.12; 95% confidence interval [CI], 1.03–1.22). ROC curves showed that patients with LTB of 10.5° (SBM, AUC 0.626) may have moderate/severe motor impairment. Conclusions: The severity of trunk misalignment does not fully explain limitation in ADLs, motor impairment, falls, and back pain. Multiple factors possibly related to an aggressive PD phenotype may account for disability in PD patients with FTB, LTB, and FNB.

This work proposes a real-time monitoring tool aimed to support clinicians for remote assessing exercise performances during home-based rehabilitation. The study relies on clinician indications to define kinematic features, that describe five motor tasks (i.e., the lateral tilt of the trunk, lifting of the arms, trunk rotation, pelvis rotation, squatting) usually adopted in the rehabilitation program for axial disorders. These features are extracted by the Kinect v2 skeleton tracking system and elaborated to return disaggregated scores, representing a measure of subjects performance. A bell-shaped function is used to rank the patient performances and to provide the scores. The proposed rehabilitation tool has been tested on 28 healthy subjects and on 29 patients suffering from different neurological and orthopedic diseases. The reliability of the study has been performed through a cross-sectional controlled design methodology, comparing algorithm scores with respect to blinded judgment provided by clinicians through filling a specific questionnaire. The use of task-specific features and the comparison between the clinical evaluation and the score provided by the instrumental approach constitute the novelty of the study. The proposed methodology is reliable for measuring subject’s performance and able to discriminate between the pathological and healthy condition.

Here, the authors present a low cost telerehabilitation system made up of a commercial red-green-blue depth (RGB-D) camera and a web-based platform. The authors goal is to monitor and assess subject movement providing acceptable and usable at-home remote rehabilitation services without the presence of a clinician. Clinical goals, defined by physiotherapists, are firstly translated into motion analysis features. A Takagi Sugeno fuzzy inference system (FIS) is then proposed to evaluate and combine these features into scores. In this stage, the `collaborative design' paradigm is used in depth and complete manner: the contribution of the clinician is not limited only to the rules definition but enters in the core of the evaluation algorithm through the definition of the fuzzy rules. A case study on low back pain rehabilitation involving 40 subjects, 5 exercises, and 4 physiotherapists is then presented to the effectiveness of the proposed system. Results of the validation of the system aimed at the assessment of the reliability of the proposed approach show high correlations between clinician evaluation and FIS scores. In this scenario, due to the high correlation, each FIS could represent a virtual alter-ego of the physiotherapist which enable a real time and free second opinion.

In this paper, a Hidden Semi-Markov Model (HSMM) based approach is proposed to evaluate and monitor body motion during a rehabilitation training program. The approach extracts clinically relevant motion features from skeleton joint trajectories, acquired by the RGB-D camera, and provides a score for the subject's performance. The approach combines different aspects of rule and template based methods. The features have been defined by clinicians as exercise descriptors and are then assessed by a HSMM, trained upon an exemplar motion sequence. The reliability of the proposed approach is studied by evaluating its correlation with both a clinical assessment and a Dynamic Time Warping (DTW) algorithm, while healthy and neurological disabled people performed physical exercises. With respect to the discrimination between healthy and pathological conditions, the HSMM based method correlates better with the physician's score than DTW. The study supports the use of HSMMs to assess motor performance providing a quantitative feedback to physiotherapist and patients. This result is particularly appropriate and useful for a remote assessment in the home.

Simple Summary High body mass index (BMI) is correlated with an increased production of hormones (estrogens, insulin, testosterone, leptin), and pro-inflammatory cytokines, which have been associated with breast cancer (BC) risk and recurrence. Regular physical activity (PA) decreases BMI and blood concentrations of testosterone, estrogens, insulin and pro-inflammatory cytokines. Moreover, the Mediterranean diet (MD) reduces obesity, metabolic syndrome (MS) and insulin resistance, which are all associated with increased risk of BC onset and recurrence. Despite the accumulating evidence of the detrimental effect of physical inactivity and overweight on BC recurrence, weight control and PA counseling are not yet current practice. The principal aim of the lifestyle intervention is to promote weight loss through diet and physical activity in overweight and/or high-risk breast cancer survivors. Moreover, implementing a “lifestyle interventions program” in clinical practice can lead to improvements in psychophysical well-being and favor the correction of cardiovascular risk factors and compliance with endocrine therapy, potentially translating into a prognostic advantage. Abstract A healthy lifestyle plays a strategic role in the prevention of BC. The aim of our prospective study is to evaluate the effects of a lifestyle interventions program based on special exercise and nutrition education on weight, psycho-physical well-being, blood lipid and hormonal profile among BC patients who underwent primary surgery. From January 2014 to March 2017, a multidisciplinary group of oncologists, dieticians, physiatrists and an exercise specialist evaluated 98 adult BC female patients at baseline and at different time points. The patients had at least one of the following risk factors: BMI ≥ 25 kg/m2, high testosterone levels, high serum insulin levels or diagnosis of MS. Statistically significant differences are shown in terms of BMI variation with the lifestyle interventions program, as well as in waist circumference and blood glucose, insulin and testosterone levels. Moreover, a statistically significant difference was reported in variations of total Hospital Anxiety and Depression Scale (HADS) score, in the anxiety HADS score and improvement in joint pain. Our results suggested that promoting a healthy lifestyle in clinical practice reduces risk factors involved in BC recurrence and ensures psycho-physical well-being.

Background Heel reconstruction represents a challenge for all plastic surgeons due to the anatomical and functional features of this weight-bearing area. In the last decade a combined use of acellular dermal matrices and skin grafts has been proposed as a reliable and less invasive alternative for complex wound management; nevertheless only a few cases have been reported in the literature. Methods We describe the long-term outcome of 2 cases of severe degloving trauma of the plantar region with massive soft tissue defects of the foot, that underwent surgical reconstruction with artificial dermis and skin grafts. At the fifth year of follow-up, both patients underwent a clinical and a computerized gait analysis to study their functional outcomes and the kinematics of their gait. Results Both patients recovered functional ambulation and returned to their own work and vocational activities, showing a symmetric gait and parameters of upright posture fully comparable to normality. Conclusions Despite the initial concerns about the use of acellular dermal matrices and skin grafts for this kind of injury, they seem to be a simple and safe alternative for weight-bearing reconstruction of the degloved foot. The authors believe that the current study yields useful information and reassurance about their long-term reliability.

In this paper, a Machine-Learning Based Emotion Recognition System in patients with Parkinson’s disease is presented. The development of this system is composed of three steps. Firstly, each user is required to execute an experimental protocol while a simple device (i.e., smartwatch), worn on the wrist, collects data. During the experimental protocol, a nine-point clinical scale and a commercial emotion recognition software have been used to identify emotions. Secondly, from smartwatch data, features extraction is implemented. Lastly, a Machine Learning Algorithm (MLA) is trained with extracted features as input and emotion classes as output.

This paper proposes a free dataset, available at the following link, 1 named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, depth videos, and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject’s performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise. 1 https://univpm-my.sharepoint.com/:f:/g/personal/p008099_staff_univpm_it/EiwbKIzk6N9NoJQx4J8aubIBx0o7tIa1XwclWp1NmRkA-w?e=F3jtBk

Parkinson's disease (PD) is a neurodegenerative disorder characterized by several motor and non-motor symptoms. These last, widely, concern affective disorders, among which, alexithymia has great impact on the amount and quality of social relationship and on caregiver well-being. Ubiquitous and quantitative monitoring of patients' emotional status may help to better understand affective disorders. This study proposes a preliminary approach to investigate the feasibility of emotion recognition in PD using physiological data collected by a smartwatch.

In this paper a Real-Time L-dopa-Induced Dyskinesia (LID) Detection System based on Machine Learning Algorithms (MLAs) and simple devices such as smartphone and smartwatch is presented. The implementation of this system was performed in three steps. Firstly, the data collection is carried out, where each patient wears the smartwatch and completes some tasks, while a smartphone application captures data. Secondly, features in time and frequency domain were extracted from smartwatch data and used as input for the training of different off-line MLAs. Lastly, the best algorithm found has been integrated into a mobile App in order to real-time monitor the smartwatch data and detect LID.

Introduction Software-based measurements of postural abnormalities in Parkinson's disease (PD) are the gold standard but may be time-consuming and not always feasible in clinical practice. Wall goniometer (WG) is an easier, quicker, and inexpensive instrument for screening patients with postural abnormalities, but no studies have investigated its validity so far. The aim of this study was to investigate the validity of the WG to measure postural abnormalities. Methods A total of 283 consecutive PD outpatients with ≥5° forward trunk, lateral trunk or forward neck bending (FTB, LTB, FNB, respectively) were recruited from seven centers for movement disorders. Postural abnormalities were measured in lateral and posterior view using a freeware program (gold standard) and the WG. Both angles were expressed in degrees (°). Sensitivity and specificity for the diagnosis of camptocormia, Pisa syndrome, and anterocollis were assessed. Results WG showed good to excellent agreement (intraclass correlation coefficient from 0.80 to 0.98) compared to the gold standard. Bland-Altman plots showed a mean difference between the methods from −7.4° to 0.4° with limits of agreements from −17.7° to 9.5°. Sensitivity was 100% for the diagnosis of Pisa syndrome, 95.74% for anterocollis, 76.67% for upper camptocormia, and 63.64% for lower camptocormia. Specificity was 59.57% for Pisa syndrome, 71.43% for anterocollis, 89.80% for upper camptocormia, and 100% for lower camptocormia. Overall, the WG underestimated measurements, especially in lower camptocormia with an average of −8.7° (90% of cases). Conclusion WG is a valid tool for screening Pisa syndrome and anterocollis, but approximately 10° more should be added for camptocormia.

(1) Background: Musculoskeletal conditions show increasing prevalence and high economic/human burden. Recovery for hip or knee surgery may require more than 26 weeks, while universally accepted rehabilitation guidelines are missing. Provided that multisensory-based training enhances motor learning, the study aims to verify if visuomotor training accelerates the recovery of lower limb motor function after orthopedic surgery. (2) Methods: Post-surgery subjects were randomly assigned to receive visuomotor training as an add-on to the conventional physical therapy (VTG), or receive the conventional therapy alone (CG). Subjects performed 40 one-hour training sessions in 8 weeks. The primary endpoint was the improvement in the Lower Extremity Functional Scale (LEFS) over the minimally clinical important difference (MCID) at 4 weeks post-randomization. The secondary endpoint included pain reduction. (3) Results: Eighteen patients were equally distributed into the VTG and CG groups. While LEFS and pain scores significantly improved in both groups, the VTG exceeded the LEFS MCID by 12 points and halved the pain value after the first 4 weeks of treatment, while the CG reached the endpoints only after treatment end (p = 0.0001). (4) Conclusions: Visuomotor training offers an innovative rehabilitation approach that accelerates the recovery of lower limb motor function in patients undergoing orthopedic surgery.

Automatic emotion recognition has powerful opportunities in the clinical field, but several critical aspects are still open, such as heterogeneity of methodologies or technologies tested mainly on healthy people. This systematic review aims to survey automatic emotion recognition systems applied in real clinical contexts, to deeply analyse clinical and technical aspects, how they were addressed, and relationships among them. The literature review was conducted on: IEEEXplore, ScienceDirect, Scopus, PubMed, ACM. Inclusion criteria were the presence of an automatic emotion recognition algorithm and the enrollment of at least 2 patients in the experimental protocol. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Moreover, the works were analysed according to a reference model to deeply examine both clinical and technical topics. 52 scientific papers passed inclusion criteria. Most clinical scenarios involved neurodevelopmental, neurological and psychiatric disorders with the aims of diagnosing, monitoring, or treating emotional symptoms. The most adopted signals are video and audio, while supervised shallow learning is mostly used for emotion recognition. A poor study design, tiny samples, and the absence of a control group emerged as methodological weaknesses. Heterogeneity of performance metrics, datasets and algorithms challenges results comparability, robustness, reliability and reproducibility.

Gait dysfunctions are pathognomonic, progressive and, generally, continuous in Parkinson’s Disease (PD). The Freezing of Gait (FoG) is an episodic gait disorder involving up to 70% of people with PD, within 10 years of clinical onset, and associated with an increased risk for falls and immobility, which in turn, contributes to greater disability. Automatic and objective monitoring of FoG may help clinicians to understand and treat this phenomenon. In this work, a smartphone app for real-time FoG detection is presented and tested both in a laboratory setting and at patients’ home. The app implements a novel fuzzy logic algorithm that uses important spatio-temporal parameters of gait and is built according to clinical knowledge about FoG. The app includes a gait detection function and the evaluation of two important clinical statistics, i.e. FoG time and FoG number. The app FoG detection performance was assessed against clinicians evaluation and compared with the Moore–Bachlin FoG detection algorithm through ROC analysis, the calculation of confusion matrix, and FoG hit rate. The proposed algorithm achieved better results with respect to the Moore–Bachlin algorithm. Home reports were compared with respect to the FoG Questionnaire and laboratory reports; results indicated significant correlations for both FoG time and FoG number. The results confirm the reliability and accuracy of this app for FoG detection, supporting its wide use for diagnostic and therapeutic purposes.

In this paper a L-dopa-Induced Dyskinesia Detection System based on Machine Learning Algorithms (MLAs), smartwatch data and a smartphone is presented. The development of this system was performed in three steps. In the first step each patient wears the smartwatch and fulfills some tasks while the smartphone Application captures data. The second phase is the features extraction from acceleration and angular velocity signals and the application of a Z-score normalization. In the last step two MLAs, trained with these features as input, are implemented in order to detect dyskinesias.

This paper presents a L-dopa-Induced Dyskinesia Detection and Classification System based on Machine Learning Algorithms, wearable device (smartwatch) data and a smart-phone, connected via Bluetooth. This system was developed in three steps. The first step is the data collection, where each patient wears the smartwatch and performs some tasks while the smart-phone App captures data. These performed tasks are of different nature (i.e., writing, walking, sitting and cognitive task). In the second phase, some features were extracted from acceleration and angular velocity signals and a Z-score normalization is applied. In the last step two Machine Learning Algorithms, trained with these features as input, are used in order to detect and classify dyskinesias.

BACKGROUND Dysphagia is a critical symptom of Neuromuscular Diseases and is often associated with considerable morbidity and mortality. OBJECTIVE This study is designed to investigate the prevalence of dysphagia and to identify different clinical profiles of swallowing disorders in Myotonic Dystrophy type 1 (DM1) and Amyotrophic Lateral Sclerosis (ALS), the most common Neuromuscular Diseases in the adult age. METHODS Consecutive DM1 and ALS patients from 2013 to 2015 referred to a Centre for Neuromuscular Disease were enrolled. A comprehensive assessment of swallowing function with a Clinical Swallowing Examination and Fluid and Food Trials was performed. RESULTS 157 patients were included: 86 ALS, 71 DM1. The dysphagic patients affected by ALS and DM1 (79% and 86% of the respective samples) showed two different profiles. ALS patients with dysphagia were older and underweight. They experienced a global dysfunction of the oral and pharyngeal phases with more difficulty in swallowing thin liquids. Conversely, DM1 patients with dysphagia were younger and tended to obesity. Most of them showed impairment of oral phase and had more frequently difficulty in swallowing solid bolus. CONCLUSION The recognition of specific clinical profiles supports and guides the detection of swallowing disorders in patients with neuromuscular diseases.

Freezing of gait is considered a clinical problem in Parkinson's disease, due to its episodic nature, heterogeneous manifestation, and drug resistance. Automatic and objective assessment of freezing of gait may help to increase clinical knowledge and to manage its effects on daily living. In this paper, a fuzzy logic algorithm is presented and tested in real-time on a smartphone.

In this paper, the accuracy evaluation of the Kinect v2 sensor is investigated in a rehabilitation scenario. The accuracy analysis is provided in terms of joint positions and angles during dynamic postures used in low-back pain rehabilitation. Although other studies have focused on the validation of the accuracy in terms of joint angles and positions, they present results only considering static postures whereas the rehabilitation exercise monitoring involves to consider dynamic movements with a wide range of motion and issues related to the joints tracking. In this work, joint positions and angles represent clinical features, chosen by medical staff, used to evaluate the subject's movements. The spatial and temporal accuracy is investigated with respect to the gold standard, represented by a stereophotogrammetric system, characterized by 6 infrared cameras. The results provide salient information for evaluating the reliability of Kinect v2 sensor for dynamic postures.

Introduction/Background Freezing of Gait (FOG) is a distressing gait disorder frequently related to Parkinson's disease (PD) progression and severe disability. Detecting and quantifying FOG, in a clinical setting, is difficult given its episodic nature; hence, reliable tools are warranted for FOG monitoring in the daily life. A number of wearable sensors to detect FOG have been studied, but the majority of the available technology lacks “ecological” validation. The study is aimed at assessing the usability, at home, of the smartphone-based system for FOG detection, validated in the outpatient setting (Capecci et al., 2016). Moreover, the correlation between data collected in the daily living scenario and those recorded in the laboratory has been sought. Material and method Twenty-four patients with PD-related resistant-FOG were studied. At baseline, the following measures were taken: UPDRS, New-FOG-Q, 6MWT, PDQ-39, GFQ and video-recorded TUG-test with and without dual-tasks while wearing the smartphone, in both OFF and ON medication conditions. Patients were instructed on how to use the FOG monitoring system at home, and were requested to wear it for three consecutive days. The system was customized to record the number of FOG events and FOG duration per minute walking. Results Twenty-three out of 24 patients (95.83%) complied with the recommendations about system wearing, and used the system 263 [185; 461] minutes/day. Median [IQR] values recorded during the 3 days were: 3 [1.05; 5.15] FOG/min and 5.1 [1.52; 9.47] s/min of FOG duration. Both parameters were significantly related with the number and duration of FOG events recorded during the simple-TUG performed either in OFF or in ON medication conditions (P = .001), and with dual-task in ON condition (P = .003). They were also related with GFQ (P = .006) and NFOG scores (P = .03). Conclusion A smartphone-based FOG monitoring system is usable and reliable, even in daily living situations. It could be of great help in assessing the efficacy of rehabilitation approaches to relieve FOG-related disability.

Introduction/Background The role of not-invasive brain stimulation (NIBS) as an add-on treatment to motor training in children suffering from focal dystonia has described in the literature with contrasting results. The study is aimed at describing the clinical and functional outcome observed in a 13-years-old girl suffering from hand dystonia and undergone NIBS combined with mirror therapy. Material and method We report the case of a 13-year-old girl suffering from hand dystonia following right hemisphere lesion in the basal ganglia area, due to cerebrovascular accident occurred in the infancy. At the basal assessment she showed a complete muscle strength recovery, a quite normal gait pattern, but a complete impairment in left hand dexterity due to hand muscle dystonia. She had already undergone several training protocols, including splint wearing and repeated botulinum toxin injections. These last induced a partial resolution of muscle contraction at rest, without any improvement in hand dexterity. We proposed the following treatment protocol: daily sessions (20 minute each) of cathodal tDCS on the right sensorimotor cortex (P4), 1 mA, followed, in the same morning, by 20 minutes mirror therapy, for five consecutive days. Functional status was assessed using the Fugl-Meyer upper limb score at baseline (T0), after treatment end (T1) and one month later (T2). Moreover, a fMRI was performed at T0 and T1, in order to look what brain networks were activated during the left and right limb movements. Results The NIBS was well tolerated. No adverse events were complained for. The Fugl-Meyer score increased from 21/66 (T0) to 29/66 (T1) and up to 30/66 (T2). The fMRI showed a significant reduction of brain activation under active left limb movement after treatment. Conclusion Parietal cortex inhibition via cathodal tDCS at the lesioned hemisphere was effective at reducing dystonia, improving voluntary movement and inducing the reorganization of brain networks.

Introduction/Background Medication resistant-FOG (FOG-R) is a disabling symptom of advanced Parkinson's Disease (PD) and it is largely untreatable. The study purpose was to examine the efficacy of robotic gait training (G-EO system) on FOG-R while analysing neurophysiological changes before and after treatment. Material and method Nine patients with advanced PD and history of FOG-R underwent G-EO training (12 sessions, 3 times/week, 4 weeks). Exclusion criteria were: dementia; change of PD medication in the last month; dyskinesias; other neurological disorders; severe orthopaedic and cardiovascular diseases; rehabilitation treatments within 3 months of enrolment. Outcome measures: number and duration of FOG episodes detected both in outpatient clinic during videotaped Timed Up and Go tests (TUG) and at home, by means of a smartphone app; Gait and Falls Questionnaire; New Freezing of Gait Questionnaire; PD Questionnaire-39; Home diary of falls, Mini-BESTest for balance evaluation, UPDRS III, Six-Minutes Walking Test. All patients were tested by Transcranial Magnetic Stimulation (TMS) before and after treatment both in OFF and ON medication states. Sensorimotor plasticity (SMP) was induced by means of the rapid paired associative stimulation (rPAS). Motor evoked potentials (MEPs) were recorded at baseline and for 15 minutes after rPAS. Results After robotic treatment the gait performance improved in terms of speed, endurance, step rhythm and balance. At home, FOG occurrence and duration decreased by about 50%. Before rehabilitation, SMP was deficient in all subjects both in OFF and ON states. The robotic treatment restored the potentiation of MEPs amplitudes by rPAS both in OFF and ON states. An inverse correlation was found between PD duration and the improvement in MEPs amplitude induced by rPAS, after rehabilitation. Conclusion Robotic gait training with G-EO System is an effective rehabilitation approach able to improve gait performance and reduce FOG-R in PD patients by shaping SMP.

INTRODUCTION: The freezing of gait (FOG) is a common and highly distressing motor symptom in patients with Parkinson's Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment. METHODS: In order to verify the acceptance of a smartphone-based architecture and its reliability at detecting FOG in real-time, we studied 20 patients suffering from PD-related FOG. They were asked to perform video-recorded Timed Up and Go (TUG) test with and without dual-tasks while wearing the smartphone. Video and accelerometer recordings were synchronized in order to assess the reliability of the FOG detection system as compared to the judgement of the clinicians assessing the videos. The architecture uses two different algorithms, one applying the Freezing and Energy Index (Moore-Bachlin Algorithm), and the other adding information about step cadence, to algorithm 1. RESULTS: A total 98 FOG events were recognized by clinicians based on video recordings, while only 7 FOG events were missed by the application. Sensitivity and specificity were 70.1% and 84.1%, respectively, for the Moore-Bachlin Algorithm, rising to 87.57% and 94.97%, respectively, for algorithm 2 (McNemar value=28.42; p=0.0073). CONCLUSION: Results confirm previous data on the reliability of Moore-Bachlin Algorithm, while indicating that the evolution of this architecture can identify FOG episodes with higher sensitivity and specificity. An acceptable, reliable and easy-to-implement FOG detection system can support a better quantification of the phenomenon and hence provide data useful to ascertain the efficacy of therapeutic approaches.Copyright © 2016 Elsevier B.V. All rights reserved. Language: en

This work investigates how Hidden Semi-Markov Model (HSMM) can be used to monitor and evaluate physical rehabilitation exercises by Kinect v2 to support medical personnel and patients during rehabilitation at home. Authors developed an exercises assessment method based on the extraction of motion features determined by clinicians. Five different rehabilitation exercises are modeled using a HSMM to provide an assessment score. The scores are compared with those obtained using the Dynamic Time Warping to discriminate which, between these two methods, best correlates doctors and physiotherapists' evaluation. Results show that HSMM can be used to evaluate exercise performances and give a feedback to physiotherapists and patients about exercise execution.

Summary We explored the efficacy and safety of bilateral SubThalamic Nucleus (STN) stimulation in two subjects suffering from drug-resistant epilepsy even after anterior callosotomy. Case 1 had about 65% decrease of partial motor seizures and the complete disappearance of tonic–clonic generalized attacks. Case 2, with sudden drop (atonic) attacks, partial complex seizures, atypical absences and rare tonic–clonic seizures, showed no meaningful reduction of fits and a stimulation associated atypical absence rate increase.

Telemedicine systems for remote monitoring are gaining importance given the increase in aging population. Possible limitations to their diffusion in daily living situations can lay on usability and social acceptability barriers. In this work we propose an architecture for remote monitoring and management of motor disorders applied on Parkinsonian patients suffering of Freezing of Gait (FoG). The architecture is composed of a smartphone app, a database (DB) and a web app, hence it uses only technologies that are well known and diffuse among society. The smartphone app monitors gait parameters and provide acoustic feedback (cues) to the patient when it detects a FoG episode. Furthermore the app stores all the informations collected in a local DB and periodically sends them to the central DB when Internet connection is available. The web app provides graphic user interfaces to query the DB to observe FoG monitoring data and to remotely adjust patient's rehabilitation and pharmacological therapies. The architecture was tested on 6 Parkinsonian patients, results about FoG detection accuracy and cues effectiveness are reported.

Background: Parkinson's disease (PD) is associated with gastrointestinal motility abnormalities that could favor the occurrence of small intestinal bacterial overgrowth. The aim of the study was to assess the prevalence of small intestinal bacterial overgrowth in PD patients. Methods: Consecutive PD patients were enrolled. The controls were subjects without PD. All patients and controls underwent the glucose breath test to assess small intestinal bacterial overgrowth. Results: Forty-eight PD patients and 36 controls were enrolled. The prevalence of small intestinal bacterial overgrowth was significantly higher in PD patients than in controls (54.17% vs 8.33%; P < .0001; OR, 2.24; 95% CI, 3.50–48.24). Multivariate analysis showed Hoehn and Yahr stage (OR, 3.07; 95% CI, 1.14–8.27) and Unified PD Rating score (OR, 1.12; 95% CI, 1.02–1.23) were significantly associated with small intestinal bacterial overgrowth in PD patients. Conclusions: Small intestinal bacterial overgrowth is highly prevalent in PD. Gastrointestinal motility abnormalities might explain this association. © 2011 Movement Disorder Society

This work deals with the design of an interactive monitoring tool for home-based physical rehabilitation. The software platform includes a video processing stage and the exercise performance evaluation. Image features are extracted by a Kinect v2 sensor and elaborated to return the exercises score. Furthermore the tool provides to physiotherapists a quantitative exercise evaluation of subject's performances. The proposed tool for home rehabilitation has been tested on 5 subjects and 5 different exercises and results are presented. In particular both exercises and relative evaluation indexes were selected by specialists in neurorehabilitation.

Physical rehabilitation is an important medical activity sector for the recovery of physical functions and clinical treatment of people affected by different pathologies, as neurodegenerative diseases (i.e. multiple sclerosis, Parkinson and Alzheimer diseases, amyotrophic lateral sclerosis), neuromuscular disorders (i.e. dystrophies, myopathies, amyotrophies and neuropathies), neurovascular disorders/trauma (i.e. stroke and traumatic brain injuries), and mobility for the elderly. During the rehabilitation, the patient has to perform different exercises specific for the own disease: while some exercises have to be performed with specific equipment and under the supervision of professional staff, others can be performed by patients without the supervision of physiotherapists. In this last case, it is possible to reduce the costs of health and care national system and to accomplish the treatment at home. In this work, a computer vision system for physical rehabilitation at home is proposed. The vision system exploits a low cost RGB-D camera and open source libraries for the image processing, in order to monitor the exercises performed by the patients, and returns a video feedback to improve the treatment effectiveness and to increase the user’s motivation, interest, and perseverance. Moreover, the vision system evaluates an exercise score in order to monitor the rehabilitation progress, an helpful information both for the clinician staff and patients, and allow physiotherapists to monitor the patients at home and correct their posture if the exercises are not well performed. This approach has been implemented and experimentally tested using the Microsoft Kinect camera, demonstrating good and reliable performances.

We built a smartphone-based architecture to detect on line Freezing of Gait (FOG) occurrences and send acoustic signals to restore gait. Parameters used for FOG detection and FOG events are stored in a local database and periodically sent to a clinical server. We tested this solution on 18 patients.

Chronic diseases are an international concern, for their increasing incidence and the strain on individuals and on healthcare systems. In order to enable the healthcare system to cope with increasing demands and to avoid strong decrements in subject’s functionality and well-being, a variety of changes for the management of chronic disease care have been advocated by the World Health Organization. The objective of the research is to verify the feasibility and acceptability of a technological solution that allows to transfer a tailored rehabilitation program for patients with disabilities in the home environment. The first step of the study is to identify the correct technological solution on the basis of the International Standard of Organization (ISO) definition of usability and acceptability. The second step is to verify the capacity of the system to perform correctly what is requested. The third step will be to verify the system efficacy in the patients’ training.

The freezing of gait (FOG) is a common and highly distressing motor symptom of patients with Parkinson’s Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment. Clinicians found alternative approaches, such as rhythmic cueing. We have built a smartphone-based architecture in agreement with acceptability and usability requirements which is able to detect FOG and provide acoustic feedback to the patient. The aim of this work is to compare the reliability of a real-time FOG detection using two different algorithms implemented on the smartphone.

The freezing of gait (FOG) is a common and highly distressing motor symptom of patients with Parkinson's Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment. Clinicians found alternative approaches, such as rhythmic cueing. We built a smartphone-based architecture in agreement with acceptability and usability requirements which is able to gather data and information useful to detect FOG. In this work fusing together the information of freeze index, energy, cadency variation and the ratio of the derivative of the energy a novel Fuzzy Logic based algorithm is developed. Performances of the Fuzzy algorithm are compared with two other algorithms showing its capability to reduce false negative detection thus improving sensitivity and specificity.