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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.

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.

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

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.