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Additional file 1.

Abstract Background Recent studies have shown that neural stimulation can be used to provide artificial sensory feedback to amputees eliciting sensations referred on the amputated hand. The temporal properties of the neural stimulation modulate aspects of evoked sensations that can be exploited in a bidirectional hand prosthesis. Methods We previously collected evidence that the derivative of the amplitude of the stimulation (intra-digit temporal dynamics) allows subjects to recognize object compliance and that the time delay among stimuli injected through electrodes implanted in different nerves (inter-digit temporal distance) allows to recognize object shapes. Nevertheless, a detailed characterization of the subjects’ sensitivity to variations of intra-digit temporal dynamic and inter-digit temporal distance of the intraneural tactile feedback has not been executed. An exhaustive understanding of the overall potentials and limits of intraneural stimulation to deliver sensory feedback is of paramount importance to bring this approach closer and closer to the natural situation. To this aim, here we asked two trans-radial amputees to identify stimuli with different temporal characteristics delivered to the same active site (intra-digit temporal Dynamic Recognition (DR)) or between two active sites (inter-digit Temporal distance Recognition (TR)). Finally, we compared the results achieved for (simulated) TR with conceptually similar experiments with real objects with one subject. Results We found that the subjects were able to identify stimuli with temporal differences (perceptual thresholds) larger than 0.25 s for DR and larger than 0.125 s for TR, respectively. Moreover, we also found no statistically significant differences when the subjects were asked to identify three objects during simulated ‘open-loop’ TR experiments or real ‘closed-loop’ tests while controlling robotic hand. Conclusions This study is a new step towards a more detailed analysis of the overall potentials and limits of intraneural sensory feedback. A full characterization is necessary to develop more advanced prostheses capable of restoring all lost functions and of being perceived more as a natural limb by users.

Abstract Background In the last decades, several powered ankle-foot orthoses have been developed to assist the ankle joint of their users during walking. Recent studies have shown that the effects of the assistance provided by powered ankle-foot orthoses depend on the assistive profile. In compliant actuators, the stiffness level influences the actuator’s performance. However, the effects of this parameter on the users has not been yet evaluated. The goal of this study is to assess the effects of the assistance provided by a variable stiffness ankle actuator on healthy young users. More specifically, the effect of different onset times of the push-off torque and different actuator’s stiffness levels has been investigated. Methods Eight healthy subjects walked with a unilateral powered ankle-foot orthosis in several assisted walking trials. The powered orthosis was actuated in the sagittal plane by a variable stiffness actuator. During the assisted walking trials, three different onset times of the push-off assistance and three different actuator’s stiffness levels were used. The metabolic cost of walking, lower limb muscles activation, joint kinematics, and gait parameters measured during different assisted walking trials were compared to the ones measured during normal walking and walking with the powered orthosis not providing assistance. Results This study found trends for more compliant settings of the ankle actuator resulting in bigger reductions of the metabolic cost of walking and soleus muscle activation in the stance phase during assisted walking as compared to the unassisted walking trial. In addition to this, the study found that, among the tested onset times, the earlier ones showed a trend for bigger reductions of the activation of the soleus muscle during stance, while the later ones led to a bigger reduction in the metabolic cost of walking in the assisted walking trials as compared to the unassisted condition. Conclusions This study presents a first attempt to show that, together with the assistive torque profile, also the stiffness level of a compliant ankle actuator can influence the assistive performance of a powered ankle-foot orthosis.

The Ethics and Society Subproject has developed this Opinion in order to clarify lessons the Human Brain Project (HBP) can draw from the current discussion of artificial intelligence, in particular the social and ethical aspects of AI, and outline areas where it could usefully contribute. The EU and numerous other bodies are promoting and implementing a wide range of policies aimed to ensure that AI is beneficial - that it serves society. The HBP as a leading project bringing together neuroscience and ICT is in an excellent position to contribute to and to benefit from these discussions. This Opinion therefore highlights some key aspects of the discussion, shows its relevance to the HBP and develops a list of six recommendations. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme Under Grant Agreement no. 785907

Background. An ischemic stroke is followed by the remapping of motor representation and extensive changes in cortical excitability involving both hemispheres. Although stimulation of the ipsilesional motor cortex, especially when paired with motor training, facilitates plasticity and functional restoration, the remapping of motor representation of the single and combined treatments is largely unexplored. Objective. We investigated if spatio-temporal features of motor-related cortical activity and the new motor representations are related to the rehabilitative treatment or if they can be specifically associated to functional recovery. Methods. We designed a novel rehabilitative treatment that combines neuro-plasticizing intervention with motor training. In detail, optogenetic stimulation of peri-infarct excitatory neurons expressing Channelrhodopsin 2 was associated with daily motor training on a robotic device. The effectiveness of the combined therapy was compared with spontaneous recovery and with the single treatments (ie optogenetic stimulation or motor training). Results. We found that the extension and localization of the new motor representations are specific to the treatment, where most treatments promote segregation of the motor representation to the peri-infarct region. Interestingly, only the combined therapy promotes both the recovery of forelimb functionality and the rescue of spatio-temporal features of motor-related activity. Functional recovery results from a new excitatory/inhibitory balance between hemispheres as revealed by the augmented motor response flanked by the increased expression of parvalbumin positive neurons in the peri-infarct area. Conclusions. Our findings highlight that functional recovery and restoration of motor-related neuronal activity are not necessarily coupled during post-stroke recovery. Indeed the reestablishment of cortical activation features of calcium transient is distinctive of the most effective therapeutic approach, the combined therapy.

Αυτή η βάση δεδομένων περιλαμβάνει δεδομένα συμπεριφοράς από 61 παιδιά που διαγνώστηκαν με κατάσταση φάσματος αυτισμού (AST). Τα δεδομένα προέρχονται από μια μεγάλης κλίμακας μελέτη της θεραπείας του αυτισμού που υποστηρίζεται από ρομπότ. Η βάση δεδομένων καλύπτει πάνω από 3.000 συνεδρίες από περισσότερες από 300 ώρες θεραπείας. Τα μισά από τα παιδιά αλληλεπιδρούν με το κοινωνικό ρομπότ NAO, υπό την επίβλεψη ενός θεραπευτή. Το άλλο μισό, που σχημάτισε την ομάδα ελέγχου, αλληλεπιδρούσε άμεσα με έναν θεραπευτή. Και οι δύο ομάδες ακολούθησαν το ίδιο πρότυπο πρωτόκολλο για τη γνωστική συμπεριφορική θεραπεία, την Εφαρμοσμένη Ανάλυση Συμπεριφοράς (ABA). Κάθε συνεδρία καταγράφηκε χρησιμοποιώντας τρεις κάμερες RGB και δύο κάμερες RGBD (Kinect) που αναλύθηκαν χρησιμοποιώντας τεχνικές απεικόνισης για τον εντοπισμό της συμπεριφοράς του παιδιού κατά τη διάρκεια της θεραπείας. Αυτή η δημόσια έκδοση της βάσης δεδομένων δεν περιέχει κανένα καταγεγραμμένο υλικό βίντεο ή άλλα προσωπικά δεδομένα, αλλά περιλαμβάνει ανώνυμα δεδομένα που περιγράφουν τις κινήσεις του παιδιού, τη θέση της κεφαλής και τον προσανατολισμό του, καθώς και τις κινήσεις των ματιών, όλες που απαριθμούνται σε ένα κοινό σύστημα συντεταγμένων. Επιπλέον, περιλαμβάνονται μεταδεδομένα με τη μορφή της διάγνωσης της ηλικίας, του φύλου και του αυτισμού (ADOS). Όλα τα δεδομένα σε αυτή τη βάση δεδομένων αποθηκεύονται ως JavaScript Object Notation (JSON) μπορούν να μεταφορτωθούν με τη μορφή DREAMdataset.zip. Ένα πολύ μικρότερο αρχείο δειγμάτων δεδομένων από μια μόνο συνεδρία μπορεί να τηλεφορτωθεί ξεχωριστά με τη μορφή DREAMdata-παράδειγμα.zip. Η μορφή JSON καθορίζεται με τη μορφή σχήματος JSON που επισυνάπτεται επίσης σε αυτή τη βάση δεδομένων. Το JSON μπορεί να διαβαστεί με τη χρήση τυποποιημένων βιβλιοθηκών στις περισσότερες γλώσσες προγραμματισμού. Οδηγίες για την ανάγνωση και την απεικόνιση των δεδομένων με τη χρήση Python και Jupyter επισυνάπτονται στο DREAMdata-documentation.zip. Παρακαλούμε επισκεφθείτε τη διεύθυνση https://github.com/dream2020/data για λεπτομέρειες. Η βάση δεδομένων μπορεί επίσης να απεικονιστεί χρησιμοποιώντας το DREAM Data Visualizer, ένα απλό λογισμικό ανοιχτού κώδικα που διατίθεται μέσω https://github.com/dream2020/DREAM-data-visualizer. Το πλήρες σύστημα που χρησιμοποιείται για την καταγραφή αυτής της βάσης δεδομένων είναι επίσης διαθέσιμο στη διεύθυνση https://github.com/dream2020/DREAM. Cette base de données comprend des données comportementales de 61 enfants diagnostiqués avec l’état du spectre de l’autisme (AST).Les données recueillies proviennent d’une étude à grande échelle de la thérapie autistique soutenue par des robots. La base de données couvre plus de 3000 séances de plus de 300 heures de thérapie. La moitié des enfants ont interagi avec le robot social NAO, supervisé par un thérapeute. L’autre moitié, qui a formé le groupe témoin, a interagi directement avec un thérapeute. Les deux groupes ont suivi le même protocole standard pour la thérapie cognitivo-comportementale, Applied Behavior Analysis (ABA). Chaque séance a été enregistrée à l’aide de trois caméras RVB et de deux caméras RGBD (Kinect) analysées à l’aide de techniques d’imagerie pour identifier le comportement de l’enfant pendant la thérapie. Cette version publique de la base de données ne contient pas de matériel vidéo enregistré ou d’autres données personnelles, mais comprend plutôt des données anonymisées décrivant les mouvements de l’enfant, la position de la tête et l’orientation, ainsi que les mouvements oculaires, tous énumérés dans un système de coordonnées commun. En outre, des métadonnées sous forme d’âge, de sexe et de diagnostic d’autisme (ADOS) de l’enfant sont incluses. Toutes les données de cette base de données sont stockées sous la forme de JavaScript Object Notation (JSON) sous la forme de DREAMdataset.zip. Une archive beaucoup plus petite d’échantillons de données d’une seule session peut être téléchargée séparément sous la forme de DREAMdata-example.zip. Le format JSON est spécifié dans la forme d’un schéma JSON qui est également attaché à cette base de données. JSON peut être lu à l’aide de bibliothèques standard dans la plupart des langages de programmation. Les instructions pour la lecture et la visualisation des données à l’aide de Python et Jupyter sont jointes dans DREAMdata-documentation.zip. Veuillez consulter le site https://github.com/dream2020/data pour plus de détails. La base de données peut également être visualisée à l’aide de DREAM Data Visualizer, un logiciel open source simple disponible via https://github.com/dream2020/DREAM-data-visualizer. Le système complet utilisé pour enregistrer cette base de données est également disponible via https://github.com/dream2020/DREAM. Áirítear sa bhunachar sonraí seo sonraí iompraíochta ó 61 leanbh a diagnóisíodh le Coinníoll ar Speictream an Uathachais (AST). Tagann na sonraí a bhailítear ó staidéar mórscála ar theiripe uathachais a fhaigheann tacaíocht ó róbait. Clúdaíonn an bunachar sonraí breis agus 3,000 seisiún ó níos mó ná 300 uair an chloig teiripe. D’idirghníomhaigh leath de na leanaí leis an robot sóisialta NAO, faoi mhaoirseacht teiripeora. D’idirghníomhaigh an leath eile, a bhunaigh an grúpa rialaithe, go díreach le teiripeoir. Lean an dá ghrúpa an prótacal caighdeánach céanna le haghaidh teiripe iompraíochta cognaíoch, Anailís ar Iompraíocht Fheidhmeach (ABA). Taifeadadh gach seisiún ag baint úsáide as trí cheamara RGB agus dhá cheamara RGBD (Kinect) a ndearnadh anailís orthu ag baint úsáide as teicnící íomháithe chun iompar an linbh le linn teiripe a aithint. Níl aon ábhar físe taifeadta ná sonraí pearsanta eile sa leagan poiblí seo den bhunachar sonraí, ach ina ionad sin áirítear sonraí anaithnidithe a dhéanann cur síos ar ghluaiseachtaí, ar cheannshuíomh agus ar threoshuíomh an linbh, chomh maith le gluaiseachtaí súl, atá liostaithe i gcomhchóras comhordaithe. Ina theannta sin, áirítear meiteashonraí i bhfoirm aois, inscne, agus diagnóis uathachais an linbh (ADOS). Stóráiltear na sonraí go léir sa bhunachar sonraí seo mar is féidir Nodtation Réada JavaScript (JSON) a íoslódáil i bhfoirm DREAMdataset.zip. Is féidir cartlann i bhfad níos lú de shonraí samplacha ó sheisiún amháin a íoslódáil ar leithligh i bhfoirm DREAMdata-example.zip. Sonraítear an fhormáid JSON i bhfoirm scéimre JSON atá ceangailte leis an mbunachar sonraí seo freisin. Is féidir JSON a léamh ag baint úsáide as leabharlanna caighdeánacha i bhformhór na dteangacha cláir. Tá treoracha maidir leis na sonraí a léamh agus a amharcléiriú ag baint úsáide as Python agus Jupyter i gceangal le DREAMdata-documentation.zip. Tabhair cuairt ar https://github.com/dream2020/data chun sonraí a fháil. Is féidir an bunachar sonraí a amharcléiriú freisin trí úsáid a bhaint as DREAM Data Visualizer, bogearraí foinse oscailte simplí atá ar fáil trí https://github.com/dream2020/DREAM-data-visualizer. Tá an córas iomlán a úsáidtear chun an bunachar sonraí seo a thaifeadadh ar fáil freisin ag https://github.com/dream2020/DREAM. Deze database bevat gedragsgegevens van 61 kinderen gediagnosticeerd met Autism Spectrum Condition (AST). De verzamelde gegevens zijn afkomstig van een grootschalige studie van autismetherapie ondersteund door robots. De database omvat meer dan 3.000 sessies van meer dan 300 uur therapie. De helft van de kinderen communiceerde met de sociale robot NAO, onder toezicht van een therapeut. De andere helft, die de controlegroep vormde, communiceerde rechtstreeks met een therapeut. Beide groepen volgden hetzelfde standaardprotocol voor cognitieve gedragstherapie, Applied Behavior Analysis (ABA). Elke sessie werd opgenomen met behulp van drie RGB-camera’s en twee RGBD-camera’s (Kinect) die werden geanalyseerd met behulp van beeldvormingstechnieken om het gedrag van het kind tijdens de therapie te identificeren. Deze openbare versie van de database bevat geen opgenomen videomateriaal of andere persoonlijke gegevens, maar bevat in plaats daarvan geanonimiseerde gegevens die de bewegingen van het kind, de positie van het hoofd en de oriëntatie van het kind beschrijven, evenals oogbewegingen, allemaal vermeld in een gemeenschappelijk coördinatensysteem. Daarnaast zijn metadata opgenomen in de vorm van de leeftijd, geslacht en autismediagnose (ADOS) van het kind. Alle gegevens in deze database worden opgeslagen als JavaScript Object Notation (JSON) kan worden gedownload in de vorm van DREAMdataset.zip. Een veel kleiner archief van voorbeeldgegevens uit een enkele sessie kan afzonderlijk worden gedownload in de vorm van DREAMdata-voorbeeld.zip. Het JSON-formaat wordt gespecificeerd in de vorm van een JSON-schema dat ook aan deze database is gekoppeld. JSON kan worden gelezen met behulp van standaardbibliotheken in de meeste programmeertalen. Instructies voor het lezen en visualiseren van de gegevens met behulp van Python en Jupyter zijn bijgevoegd in DREAMdata-documentation.zip. Ga naar https://github.com/dream2020/data voor meer informatie. De database kan ook worden gevisualiseerd met behulp van DREAM Data Visualizer, een eenvoudige open source software die beschikbaar is via https://github.com/dream2020/DREAM-data-visualizer. Het volledige systeem dat voor de registratie van deze database wordt gebruikt, is ook beschikbaar via https://github.com/dream2020/DREAM. Questo database include i dati comportamentali di 61 bambini con diagnosi di condizione dello spettro autistico (AST). I dati raccolti provengono da uno studio su larga scala sulla terapia autistica supportato da robot. Il database copre oltre 3.000 sessioni da più di 300 ore di terapia. La metà dei bambini ha interagito con il robot sociale NAO, supervisionato da un terapeuta. L'altra metà, che ha formato il gruppo di controllo, ha interagito direttamente con un terapeuta. Entrambi i gruppi hanno seguito lo stesso protocollo standard per la terapia comportamentale cognitiva, Applied Behavior Analysis (ABA). Ogni sessione è stata registrata utilizzando tre telecamere RGB e due telecamere RGBD (Kinect) analizzate utilizzando tecniche di imaging per identificare il comportamento del bambino durante la terapia. Questa versione pubblica del database non contiene alcun materiale video registrato o altri dati personali, ma include invece dati anonimizzati che descrivono i movimenti del bambino, la posizione della testa e l'orientamento, nonché i movimenti oculari, tutti elencati in un sistema di coordinate comune. Inoltre, sono inclusi i metadati sotto forma di età, sesso e diagnosi di autismo (ADOS). Tutti i dati in questo database sono memorizzati come JavaScript Object Notation (JSON) può essere scaricato sotto forma di DREAMdataset.zip. Un archivio molto più piccolo di dati di esempio da una singola sessione può essere scaricato separatamente sotto forma di DREAMdata-example.zip. Il formato JSON è specificato nella forma di uno schema JSON che è anche allegato a questo database. JSON può essere letto usando librerie standard nella maggior parte dei linguaggi di programmazione. Le istruzioni per la lettura e la visualizzazione dei dati utilizzando Python e Jupyter sono allegate in DREAMdata-documentation.zip. Si prega di visitare https://github.com/dream2020/data per i dettagli. Il database può anche essere visualizzato utilizzando DREAM Data Visualizer, un semplice software open source disponibile sul sito https://github.com/dream2020/DREAM-data-visualizer. Il sistema completo utilizzato per la registrazione di questa banca dati è disponibile anche sul sito https://github.com/dream2020/DREAM. Esta base de datos incluye datos de comportamiento de 61 niños diagnosticados con enfermedad del espectro autista (AST). Los datos recopilados provienen de un estudio a gran escala de la terapia del autismo apoyada por robots. La base de datos cubre más de 3.000 sesiones de más de 300 horas de terapia. La mitad de los niños interactuaron con el robot social NAO, supervisado por un terapeuta. La otra mitad, que formó el grupo de control, interactuó directamente con un terapeuta. Ambos grupos siguieron el mismo protocolo estándar para la terapia cognitiva conductual, Análisis de Comportamiento Aplicado (ABA). Cada sesión se grabó utilizando tres cámaras RGB y dos cámaras RGBD (Kinect) analizadas utilizando técnicas de imagen para identificar el comportamiento del niño durante la terapia. Esta versión pública de la base de datos no contiene ningún material de vídeo grabado ni otros datos personales, sino que incluye datos anónimos que describen los movimientos del niño, la posición de la cabeza y la orientación, así como los movimientos oculares, todos enumerados en un sistema de coordenadas común. Además, se incluyen metadatos en forma de diagnóstico de edad, sexo y autismo (ADOS, por sus siglas en inglés) del niño. Todos los datos de esta base de datos se almacenan como JavaScript Object Notation (JSON) se puede descargar en forma de DREAMdataset.zip. Un archivo mucho más pequeño de datos de muestra de una sola sesión se puede descargar por separado en forma de DREAMdata-example.zip. El formato JSON se especifica en forma de un esquema JSON que también se adjunta a esta base de datos. JSON se puede leer usando bibliotecas estándar en la mayoría de los lenguajes de programación. Las instrucciones para leer y visualizar los datos usando Python y Jupyter se adjuntan en DREAMdata-documentation.zip. Por favor visite https://github.com/dream2020/data para más detalles. La base de datos también se puede visualizar utilizando DREAM Data Visualizer, un simple software de código abierto disponible a través de https://github.com/dream2020/DREAM-data-visualizer. El sistema completo utilizado para registrar esta base de datos también está disponible a través de https://github.com/dream2020/DREAM. Diese Datenbank enthält Verhaltensdaten von 61 Kindern, bei denen Autism Spectrum Condition (AST) diagnostiziert wurde. Die gesammelten Daten stammen aus einer groß angelegten Studie zur Autismustherapie, die von Robotern unterstützt wird. Die Datenbank umfasst mehr als 3.000 Sitzungen aus mehr als 300 Stunden Therapie. Die Hälfte der Kinder interagierte mit dem sozialen Roboter NAO, der von einem Therapeuten überwacht wurde. Die andere Hälfte, die die Kontrollgruppe bildete, interagierte direkt mit einem Therapeuten. Beide Gruppen folgten dem gleichen Standardprotokoll für kognitive Verhaltenstherapie, Applied Behavior Analysis (ABA). Jede Sitzung wurde mit drei RGB-Kameras und zwei RGBD-Kameras (Kinect) mit bildgebenden Techniken analysiert, um das Verhalten des Kindes während der Therapie zu identifizieren. Diese öffentliche Version der Datenbank enthält kein aufgezeichnetes Videomaterial oder andere personenbezogene Daten, sondern enthält anonymisierte Daten, die die Bewegungen, Kopfposition und Orientierung des Kindes beschreiben, sowie Augenbewegungen, die alle in einem gemeinsamen Koordinatensystem aufgeführt sind. Darüber hinaus sind Metadaten in Form von Alter, Geschlecht und Autismusdiagnose (ADOS) des Kindes enthalten. Alle Daten in dieser Datenbank werden als JavaScript Object Notation (JSON) in Form von DREAMdataset.zip gespeichert. Ein viel kleineres Archiv von Beispieldaten aus einer einzelnen Sitzung kann separat in Form von DREAMdata-example.zip heruntergeladen werden. Das JSON-Format wird in Form eines JSON-Schemas angegeben, das ebenfalls an diese Datenbank angehängt ist. JSON kann mit Standardbibliotheken in den meisten Programmiersprachen gelesen werden. Anweisungen zum Lesen und Visualisieren der Daten mit Python und Jupyter sind in DREAMdata-documentation.zip beigefügt. Weitere Informationen finden Sie auf https://github.com/dream2020/data. Die Datenbank kann auch mit DREAM Data Visualizer visualisiert werden, einer einfachen Open-Source-Software, die über https://github.com/dream2020/DREAM-data-visualizer verfügbar ist. Das komplette System, das für die Erfassung dieser Datenbank verwendet wird, ist auch über https://github.com/dream2020/DREAM verfügbar. Din id-database tinkludi data dwar l-imġiba minn 61 tifel u tifla dijanjostikati b’Kundizzjoni tal-Ispettru tal-Awtiżmu (AST). Id-data miġbura ġejja minn studju fuq skala kbira ta’ terapija tal-awtiżmu appoġġata mir-robots. Id-database tkopri aktar minn 3,000 sessjoni minn aktar minn 300 siegħa ta’ terapija. Nofs it-tfal interaġixxew mar-robot soċjali NAO, taħt is-superviżjoni ta’ terapista. In-nofs l-ieħor, li fforma l-grupp ta’ kontroll, interaġixxa direttament ma’ terapista. Iż-żewġ gruppi segwew l-istess protokoll standard għat-terapija tal-imġiba konjittiva, Analiżi Applikata tal-Imġiba (ABA). Kull sessjoni ġiet irreġistrata bl-użu ta’ tliet kameras RGB u żewġ kameras RGBD (Kinect) analizzati bl-użu ta’ tekniki ta’ immaġni biex tiġi identifikata l-imġiba tat-tifel/tifla waqt it-terapija. Din il-verżjoni pubblika tal-bażi tad-data ma fiha l-ebda materjal vidjo rreġistrat jew data personali oħra, iżda minflok tinkludi data anonimizzata li tiddeskrivi l-movimenti tat-tfal, il-pożizzjoni tar-ras u l-orjentazzjoni, kif ukoll il-movimenti tal-għajnejn, kollha elenkati f’sistema ta’ koordinati komuni. Barra minn hekk, metadata fil-forma tal-età tat-tfal, is-sess, u d-dijanjożi tal-awtiżmu (ADOS) huma inklużi. Id-data kollha f’din il-bażi tad-data tinħażen bħala JavaScript Object Notation (JSON) tista’ titniżżel fil-forma ta’ DREAMdataset.zip. Arkivju ħafna iżgħar tad-data tal-kampjun minn sessjoni waħda jista’ jitniżżel separatament fil-forma ta’ DREAMdata-example.zip. Il-format JSON huwa speċifikat fil-forma ta’ skema JSON li hija mehmuża wkoll ma’ din il-bażi tad-data. JSON tista’ tinqara bl-użu ta’ libreriji standard fil-biċċa l-kbira tal-lingwi tal-ipprogrammar. l-istruzzjonijiet għall-qari u l-viżwalizzazzjoni tad-data bl-użu ta’ Python u Jupyter huma mehmuża f’DREAMdata-documentation.zip. Jekk jogħġbok żur https://github.com/dream2020/data għad-dettalji. Il-bażi tad-data tista’ tiġi viżwalizzata wkoll bl-użu ta’ DREAM Data Visualiser, softwer b’sors miftuħ sempliċi disponibbli fuq https://github.com/dream2020/DREAM-data-visualizer. Is-sistema kompluta użata għar-reġistrazzjoni ta’ din il-bażi tad-data hija disponibbli wkoll fuq https://github.com/dream2020/DREAM. Această bază de date include date comportamentale de la 61 de copii diagnosticați cu stare de spectru autism (AST).Datele colectate provin dintr-un studiu la scară largă al terapiei autismului susținut de roboți. Baza de date acoperă peste 3.000 de sesiuni de la mai mult de 300 de ore de terapie. Jumătate dintre copii au interacționat cu robotul social NAO, supravegheat de un terapeut. Cealaltă jumătate, care a format grupul de control, a interacționat direct cu un terapeut. Ambele grupuri au urmat același protocol standard pentru terapia cognitiv-comportamentală, Analiza comportamentală aplicată (ABA). Fiecare sesiune a fost înregistrată folosind trei camere RGB și două camere RGBD (Kinect) analizate folosind tehnici de imagistică pentru a identifica comportamentul copilului în timpul terapiei. Această versiune publică a bazei de date nu conține niciun material video înregistrat sau alte date cu caracter personal, ci include date anonimizate care descriu mișcările copilului, poziția capului și orientarea, precum și mișcările ochilor, toate enumerate într-un sistem comun de coordonate. În plus, sunt incluse metadatele sub forma vârstei, sexului și diagnosticului de autism al copilului (ADOS). Toate datele din această bază de date sunt stocate ca JavaScript Object Notation (JSON) pot fi descărcate sub forma DREAMdataset.zip. O arhivă mult mai mică de date eșantion dintr-o singură sesiune poate fi descărcată separat sub forma DREAMdata-example.zip. Formatul JSON este specificat sub forma unei scheme JSON care este atașată, de asemenea, la această bază de date. JSON poate fi citit folosind biblioteci standard în majoritatea limbajelor de programare. Instrucțiunile pentru citirea și vizualizarea datelor utilizând Python și Jupyter sunt atașate în DREAMdata-documentation.zip. Vă rugăm să vizitați https://github.com/dream2020/data pentru detalii. Baza de date poate fi vizualizată și prin intermediul DREAM Data Visualizer, un software simplu open source disponibil pe https://github.com/dream2020/DREAM-data-visualizer. Sistemul complet utilizat pentru înregistrarea acestei baze de date este, de asemenea, disponibil la adresa https://github.com/dream2020/DREAM. Ez az adatbázis 61 autizmus spektrum állapottal (AST) diagnosztizált gyermek viselkedési adatait tartalmazza. Az összegyűjtött adatok a robotok által támogatott autizmusterápia nagy léptékű vizsgálatából származnak. Az adatbázis több mint 3000 alkalomra terjed ki több mint 300 órányi kezelésből. A gyerekek fele kölcsönhatásba lépett a NAO társas robottal, akit egy terapeuta felügyel. A másik fele, aki létrehozta a kontrollcsoportot, közvetlenül kapcsolatba lépett egy terapeutával. Mindkét csoport ugyanazt a standard protokollt követte a kognitív viselkedésterápia esetében, az Alkalmazott viselkedésanalízist (ABA). Minden munkamenetet három RGB kamerával és két RGBD kamerával (Kinect) elemeztek képalkotó technikák segítségével, hogy azonosítsák a gyermek viselkedését a terápia során. Az adatbázis ezen nyilvános változata nem tartalmaz rögzített videoanyagokat vagy egyéb személyes adatokat, hanem anonimizált adatokat tartalmaz, amelyek leírják a gyermek mozgását, a fej helyzetét és orientációját, valamint a szemmozgásokat, amelyek mindegyike egy közös koordinátarendszerben szerepel. Ezenkívül a metaadatok a gyermek életkora, neme és autizmus diagnózisa (ADOS) formájában szerepelnek. Ebben az adatbázisban minden adat tárolódik, mivel a JavaScript Object Notation (JSON) letölthető DREAMdataset.zip formátumban. Az egy munkamenetből származó mintaadatok sokkal kisebb archívuma külön letölthető DREAMdata-example.zip formájában. A JSON formátum egy JSON séma formájában van megadva, amely szintén ehhez az adatbázishoz van csatolva. A JSON a legtöbb programozási nyelven szabványos könyvtárakban olvasható. Az adatok Python és Jupyter használatával történő olvasására és megjelenítésére vonatkozó utasításokat a DREAMdata-documentation.zip tartalmazza. A részletekért látogasson el a https://github.com/dream2020/data oldalra. Az adatbázis megjeleníthető a DREAM Data Visualizer segítségével is, amely egy egyszerű nyílt forráskódú szoftver, amely a https://github.com/dream2020/DREAM-data-visualizer weboldalon érhető el. Az adatbázis rögzítéséhez használt teljes rendszer a https://github.com/dream2020/DREAM weboldalon is elérhető.

Abstract Background After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant therapeutic strategies. Although the ArmeoSpring exoskeleton has demonstrated its effectiveness in stroke motor rehabilitation, its interest as an assessment tool has not been sufficiently documented. The aim of this study was to investigate the psychometric properties of selected kinematic parameters obtained with the ArmeoSpring in post-stroke patients. Methods This study involved 30 post-stroke patients (mean age = 54.5 ± 16.4 years; time post-stroke = 14.7 ± 26.7 weeks; Upper-Extremity Fugl-Meyer Score (UE-FMS) = 40.7 ± 14.5/66) who participated in 3 assessment sessions, each consisting of 10 repetitions of the ‘horizontal catch’ exercise. Five kinematic parameters (task and movement time, hand path ratio, peak velocity, number of peak velocity) and a global Score were computed from raw ArmeoSpring’ data. Learning effect and retention were analyzed using a 2-way repeated-measures ANOVA, and reliability was investigated using the intra-class correlation coefficient (ICC) and minimal detectable change (MDC). Results We observed significant inter- and intra-session learning effects for most parameters except peak velocity. The measures performed in sessions 2 and 3 were significantly different from those of session 1. No additional significant difference was observed after the first 6 trials of each session and successful retention was also highlighted for all the parameters. Relative reliability was moderate to excellent for all the parameters, and MDC values expressed in percentage ranged from 42.6 to 102.8%. Conclusions After a familiarization session, the ArmeoSpring can be used to reliably and sensitively assess motor impairment and intervention effects on motor learning processes after a stroke. Trial registration The study was approved by the local hospital ethics committee in September 2016 and was registered under number 05-0916.

BackgroundRobotic systems combined with Functional Electrical Stimulation (FES) showed promising results on upper-limb motor recovery after stroke, but adequately-sized randomized controlled trials (RCTs) are still missing.ObjectiveTo evaluate whether arm training supported by RETRAINER, a passive exoskeleton integrated with electromyograph-triggered functional electrical stimulation, is superior to advanced conventional therapy (ACT) of equal intensity in the recovery of arm functions, dexterity, strength, activities of daily living, and quality of life after stroke.MethodsA single-blind RCT recruiting 72 patients was conducted. Patients, randomly allocated to 2 groups, were trained for 9 weeks, 3 times per week: the experimental group performed task-oriented exercises assisted by RETRAINER for 30 minutes plus ACT (60 minutes), whereas the control group performed only ACT (90 minutes). Patients were assessed before, soon after, and 1 month after the end of the intervention. Outcome measures were as follows: Action Research Arm Test (ARAT), Motricity Index, Motor Activity Log, Box and Blocks Test (BBT), Stroke Specific Quality of Life Scale (SSQoL), and Muscle Research Council.ResultsAll outcomes but SSQoL significantly improved over time in both groups (P < .001); a significant interaction effect in favor of the experimental group was found for ARAT and BBT. ARAT showed a between-group change of 11.5 points (P = .010) at the end of the intervention, which increased to 13.6 points 1 month after. Patients considered RETRAINER moderately usable (System Usability Score of 61.5 ± 22.8).ConclusionsHybrid robotic systems, allowing to perform personalized, intensive, and task-oriented training, with an enriched sensory feedback, was superior to ACT in improving arm functions and dexterity after stroke.