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.
Highlights • A smartphone application for real-time detection of freezing of gait is presented. • The application is tested on 20 patients with Parkinson’s disease and FOG. • An innovative algorithm ...is compared to a traditional one. • The architecture is highly reliable in FOG detection, even if it occurs at turning.
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.
People with Parkinson’s Disease (PwPD) usually experience several neuropsychiatric signs such as anxiety, depression, and negative emotions that contribute to disability and worsening of quality of ...life. Notwithstanding, the assessment of these symptoms are largely underrated, subjective and difficult due to a large overlapping with other PD symptoms, like hypomimia and bradikinesia. The aim and novelty of the current work is to study and validate a method for automatic emotion recognition in PwPD during daily living through autonomic signals acquired by acceptable and low-cost consumer technology. The best shallow learning algorithm and the best minimal feature set are individuated. 11 PwPD and 8 subjects with no history of neurological injury or illness were enrolled in the study. Participants were asked to watch video clips purposely selected to arouse emotions, and annotate arousal and valence of emotions triggered by video clips, while their heart rate, skin conductance, and temperature were recorded by a smartwatch. Smartwatch data was used for features extraction, while participants’ reported arousal and valence were used as gold-standard to train machine learning algorithms for emotion classification (low/high arousal, positive/negative valence). Different feature sets and different algorithms (i.e. decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP)) were evaluated to find the best solution for each group of participant. In each group of participants, it was possible to find a combination of feature set and algorithm to reach a classification accuracy greater than 90%. The Random Forest reached the best performance in both groups and for both valence and arousal. For each classification task (valence or arousal, PwPD or controls), the best model was selected and the minimal feature set was found by performing a recursive feature elimination based on the Shapley value. A lower accuracy of appraisal emerged for arousal compared to valence. Obtained results showed the feasibility of automatic emotion recognition in PwPDs through autonomic signals. Autonomic dysfunction in PwPDs may explain the lower arousal accuracy. The findings warrant confirmation from trials on larger samples and there are open issues to be deepened in future work.
•Smartwatch based automatic emotion recognition (AER).•AER in Parkinson’s disease through autonomic nervous system signals.•Comparison of shallow learning algorithms for AER in Parkinson’s disease.•Analysis of emotional baseline and features normalization on performance.•SHAP-based recursive feature elimination on the best algorithm.
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.
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.
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.
BACKGROUND - Automatic emotion recognition has powerful and interesting 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 (i.e., on a population of people with a pathology). METHODS - The literature review was conducted on the following scientific databases: IEEE Xplore ®, 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 (PRISMA) guidelines. Moreover, the works were analysed according to a reference model in the form of a class diagram, to highlight the most important clinical and technical aspects and relationships among them. RESULTS - 52 scientific papers passed the inclusion criteria. Based on our findings, most clinical applications involved neuro-developmental, neurological and psychiatric disorders with the aims of diagnosing, monitoring, or treating emotional symptoms. The study design seems to be mostly related to the aim of the study (it is generally observational for monitoring and diagnosis, interventional for treatment), the most adopted signals are video and audio, and supervised shallow learning emerged as most used approach for emotion recognition algorithm. DISCUSSION - Tiny samples, absence of a control group and of tests in real-life conditions emerged as important clinical limitations. Under a technical point of view, a great heterogeneity of performance metrics, datasets and algorithms challenges the comparability, robustness, reliability and reproducibility of results. Suggested guidelines are identified and discussed to help scientific community to overcome limitations and provide direction for future works.
•A novel fuzzy logic algorithm for freezing of gait detection is presented.•A smartphone app was developed to enhance usability and acceptability.•High reliability in laboratory tests against ...clinicians observation.•Home monitoring correlates significantly with laboratory clinical evaluation.•A well known algorithm was applied on the same data for comparison.
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.
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•HSMM based approach is proposed to assess human movement during rehabilitation.•The method combines different aspects of both rule and template based approaches.•The reliability of ...the method is measured with respect to clinician judgment and DTW.•The proposed method shows a significant correlation with the clinical score.•The proposed method outperforms baseline approach as DTW.
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.
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