137 research outputs found
The Robot and Us: An'Antidisciplinary'Perspective on the Scientific and Social Impacts of Robotics
No full textThis book offers a clear, yet comprehensive overview of the role of robots in our society. It especially focuses on the interaction between humans and robots, and on the social and political aspects of the integration of robots with humans, in their everyday life, both in the private and working sphere alike. Based on the lessons held by the author at “Scuola di Politiche”(transl. School of Political Sciences), this self-contained book mainly addresses an educated, though not-specialist, audience
Climate finance in the Asia-Pacific : trends and innovative approaches
No full textThis discussion paper 'Climate finance in the Asia-Pacific: Trends and Innovative Approaches' was prepared for ESCAP by Ilaria Carrozza, PhD Candidate, Department of International Relations, London School of Economics and Political Science, London, UK
The author would like to acknowledge ESCAP Environment and Development Division (Rae Kwon Chung, Aneta Nikolova, Riccardo Mesiano, Hala Razian, and with support from Yohan Hong). A peer review was conducted by Climate Policy Initiative (Mia Fitri and Leela Raina)
Modelli e tecnologie per le patolgie scheletriiche
No full textUna delle patologie più frequenti e più invalidanti nell’anziano sono le fratture ossee (WHO, 1994, 2007). La frattura in sé è una manifestazione a livello d’organo (l’osso, appunto) di una condizione di fragilità. Le cause della frattura tuttavia com- prendono fenomeni a diversa scala dimensionale. Da un lato vi è una compro- missione delle caratteristiche meccaniche del materiale (a livello di tessuto e a livello molecolare), spesso derivante da un alterato metabolismo del tessuto osseo. Dall’altra, le sollecitazioni meccaniche imposte all’osso, che derivano dal complesso funziona- mento dell’apparato muscolo scheletrico (a livello di organismo), spesso in concomi- tanza con fattori ambientali. Come si vedrà nei paragrafi seguenti, le fratture possono essere sia una conseguenza diretta di un trauma (che causa un carico diverso e supe- riore al fisiologico), che di condizioni di carico para-fisiologiche (corrispondenti ad un modesto sovraccarico). Anche le conseguenze della frattura comprendono fenomeni a livello di tessuto (riparazione), ma anche di organismo (a causa dell’effetto invalidan- te della frattura, e dell’immobilizzazione forzata), nonché sotto un profilo psicologico (spesso l’anziano che subisce la frattura cade in uno stato di depressione). Data la di- mensione del problema, esistono vasti studi epidemiologici per tentare di stratificare i soggetti anziani, e pianificare gli interventi per ridurre i rischi nei soggetti esposti a maggiore rischio. Ad esempio, è noto che se un anziano ha subito una frattura, ha un rischio di andare incontro ad una nuova frattura superiore del 50%-100% rispetto ad un soggetto coetaneo che non ha subito fratture (Cummings and Melton, 2002). Come si vedrà, l’incidenza del rischio di frattura e di ri-frattura cambia tra uomo e donna ed in base all’area geografica
Bio-Inspired Controller for a Dexterous Prosthetic Hand Based on Principal Components Analysis
No full textControlling a dexterous myoelectric prosthetic hand with many degrees of freedom (DoFs) could be a very demanding task, which requires the amputee for high concentration and ability in modulating many different muscular contraction signals. In this work a new approach to multi-DoF control is proposed, which makes use of Principal Component Analysis (PCA) to reduce the DoFs space dimensionality and allow to drive a 15 DoFs hand by means of a 2 DoFs signal. This approach has been tested and properly adapted to work onto the underactuated robotic hand named CyberHand, using mouse cursor coordinates as input signals and a principal components (PCs) matrix taken from the literature. First trials show the feasibility of performing grasps using this method. Further tests with real EMG signals are foreseen
Machine learning methods for motor recovery prediction and prognosis in post-stroke rehabilitation : a systematic review
No full textBackground. The rehabilitation field has always been characterised by the difficulty of conducing rigorous clinical trials and the need of an individualised care for the patient. The recent framework of Rehabilomics addresses the gap between research and clinical treatment needs. It promotes a systematic collection of data from the patient and it uses it in order to generate a treatment protocol for personalised therapy. Machine learning techniques can be considered a primary tool for embracing this new framework. The objective of this work is to develop a systematic review on machine learning algorithms trained and validated as predictive models for the clinical outcome of post-stroke patients after rehabilitation treatment.
Methods. We conducted a systematic review and included machine learning methods as predictive performance of motor recovery in all types of stroke. We conducted a comprehensive search of electronic databases such as PubMed, Web of Science, Scopus, CINHAL and Central using a Patient, Intervention, Comparison, Outcome format (PICO format), from inception to the 7th of February 2020. Data extracted included: health condition, intervention in the experimental and control group, dose, frequency and number of sessions, outcome assessed and how it has been measured, method for features extraction and selection, algorithm used for the model and validation approach. Methodological quality of included reviews has been assessed using the Prediction model risk of bias assessment tool (PROBAST), which assesses risk of bias over four domains, as well as applicability. A narrative description of the characteristics of the primary studies has been provided and a narrative data synthesis reporting the performance of individual prognostic models has been also performed. The opportunity of performing a meta-analysis has been evaluated on the level of heterogeneity of primary studies included.
Results. 846 studies met the inclusion criteria and were included in systematic review. All participants were adults with stroke. The data analysis is still ongoing and the final results will be presented during the Cochrane Colloquium.
Conclusions. Our results will highlight the better performing models and next steps for their comparison, extension or implementation.
Patient or healthcare consumer involvement. Not applicable
Wearable devices for biofeedback rehabilitation : A systematic review and meta-analysis to design application rules and estimate the effectiveness on balance and gait outcomes in neurological diseases
No full textWearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson’s n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions
L'elaborazione dei segnali biomedici per ottenere parametri vitali significativi per il benessere e l'invecchiamento attivo
No full textLa valutazione e l’intervento riabilitativo del cammino mediante l’uso di sensori a basso costo
No full textRobotCub implementation of real-time least-square fitting of ellipses
No full textThis paper presents the implementation of a new algorithm for pattern recognition in machine vision developed in our laboratory applied to the RobotCub humanoid robotics platform simulator. The algorithm is a robust and direct method for the leastsquare fitting of ellipses to scattered data. RobotCub is an open source platform, born to study the development of neuro-scientific and cognitive skills in human beings, especially in children. By the estimation of the surrounding objects properties (such as dimensions, distances, etc...) a subject can create a topographic map of the environment, in order to navigate through it without colliding with obstacles. In this work we implemented the method of the least-square fitting of ellipses of Maini (EDFE), previously developed in our laboratory, in a robotics context. Moreover, we compared its performance with the Hough Transform, and others least-square ellipse fittings techniques. We used our system to detect spherical objects, and we applied it to the simulated RobotCub platform. We performed several tests to prove the robustness of the algorithm within the overall system, and finally we present our results.</p
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