Go to the content. | Move to the navigation | Go to the site search | Go to the menu | Contacts | Accessibility

| Create Account

Tagliapietra, Luca (2018) A multilevel framework to measure, model, promote, and enhance the symbiotic cooperation between humans and robotic devices. [Ph.D. thesis]

Full text disponibile come:

[img]
Preview
PDF Document - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

28Mb

Abstract (english)

In the latest decades, the common perception about the role of robotic devices in the modern society dramatically changed. In the early stages of robotics, temporally located in the years of the economic boom, the development of new devices was driven by the industrial need of producing more while reducing production time and costs. The demand was, therefore, for robotic devices capable of substituting the humans in performing simple and repetitive activities. The execution of predefined basic activities in the shortest amount of time, inside carefully engineered and confined environments, was the mission of robotic devices.
Beside the results obtained in the industrial sector, a progressive widening of the fields interested in robotics – such as rehabilitation, elderly care, and medicine – led to the current vision of the device role. Indeed, these challenging fields require the robot to be a partner, which works side-by-side with the human. Therefore, the device needs to be capable of actively and efficiently interacting with humans, to provide support and overcome their limits in the execution of shared activities, even in highly unpredictable everyday environments.
Highly complex and advanced robots, such as surgical robots, rehabilitation devices, flexible manipulators, and service and companion robots, have been recently introduced into the market; despite their complexity, however, they are still tools to be used to perform, better or faster, very specific tasks.
The current open challenge is, therefore, to develop a new generation of symbiotically cooperative robotic partners, adding to the devices the capability to detect, understand, and adapt to the real intentions, capabilities, and needs of the humans. To achieve this goal, a bidirectional information channel shall be built to connect the human and the device. In one direction, the device requires to be informed about the state of its user; in the other direction, the human needs to be informed about the state of the whole interacting system.
This work reports the research activities that I conducted during my PhD studies in this research direction. Those activities led to the design, development, and assessment on a real application of an innovative multilevel framework to close the cooperation loop between a human and a robotic device, thus promoting and enhancing their symbiotic interaction.
Three main levels have been identified as core elements to close this loop: the measure level, the model level, and the extract/synthesize level. The former aims at collecting experimental measures from the whole interacting system; the second aims at estimating and predicting its dynamic behavior; the last aims at providing quantitative information to both the human and the device about their performances and about how to modify their behavior to improve their interaction symbiosis.
Within the measure level, the focus has been concentrated on investigating, critically comparing, and selecting the most suitable and advanced technologies to measure kinematics and dynamics quantities in a portable and minimally intrusive way. Particular attention has been paid to new emerging technologies; moreover, useful protocols and pipelines already recognized as de-facto in other fields have been successfully adapted to fit the needs of the man-machine interaction context. Finally, the design of a new sensor has been started to overcome the lack of tools capable of effectively measuring human-device interaction forces.
To implement the model level, a common platform to perform integrated multilevel simulations – i.e. simulations where the device and the human are considered together as interacting entities – has been selected and extensively validated. Furthermore, critical aspects characterizing the modeling of the device, the human, and their interactions have been studied and possible solutions have been proposed. For example, modeling the mechanics and the control within the selected software platform allowed accurate estimations of their behavior. To estimate human behavior, new methodologies and approaches based on anatomical neuromusculoskeletal models have been developed, validated, and released as open-source tools for the community, to allow accurate estimates of both kinematics and dynamics at run-time – i.e. at the same time that the movements are performed. An inverse kinematics approach has been developed and validated to estimate human joint angles from the orientation measurements provided by wearable inertial systems. Additionally, a state of the art neuromusculoskeletal modeling toolbox has been improved and interfaced with the other tools of the multilevel framework, to accurately predict human muscle forces, joint moments, and muscle and joint stiffness from electromyographic and kinematic measures. To estimate and predict the interactions, contact models, parameters optimization procedures, and high-level cooperation strategies have been investigated, developed, and applied.
Within the extract/synthesize level, the information provided by the other levels has been combined together to develop informative feedbacks for both the device and the human. In one direction, the device has been provided with control signals defining how to adjust the provided support to comply with the task goals and with the human current capabilities and needs. In the other direction, quantitative feedbacks have been developed to inform the human about task execution performances, task targets, and support provided by the device. This information has been provided to the user as visual feedbacks designed to be both exhaustively informative and minimally distractive, to prevent possible loss of focus. Moreover, additional feedbacks have been devised to help external observers – therapists in the rehabilitation contexts or task planners and ergonomists in the industrial field
– in the design and refinement of effective personalized tasks and long-term goals.
The integration of all the hardware and software tools of each level in a modular, flexible, and reliable software framework, based on a well known robotic middleware, has been fundamental to handle the communication and information exchange processes.
The developed general framework has been finally specialized to face the specific needs of robotic-aided gait rehabilitation. In this context, indeed, the final aim of promoting the symbiotic cooperation is translatable in maximizing treatment effectiveness for the patients by actively supporting their changing needs and capabilities while keeping them engaged during the whole rehabilitation process.
The proposed multilevel framework specialization has been successfully used, as valuable answer to those needs, within the context of the Biomot European project. It has been, indeed, fundamental to face the challenges of closing the informative loop between the user and the device, and providing valuable quantitative information to the external observers.
Within this research project, we developed an innovative compliant wearable exoskeleton prototype for gait rehabilitation capable of adjusting, at run-time, the provided support according to different cooperation strategies and to user needs and capabilities. At the same time, the wearer is also engaged in the rehabilitation process by intuitive visual feedbacks about his performances in the achievement of the rehabilitation targets and about the exoskeleton support.
Both researchers and clinical experts evaluating the final rehabilitation application of the multilevel framework provided enthusiastic feedbacks about the proposed solutions and the obtained results.
To conclude, the modular and generic multilevel framework developed in this thesis has the potential to push forward the current state of the art in the applications where a symbiotic cooperation between robotic devices and humans is required. Indeed, it effectively endorses the development of a new generation of robotic devices capable to perform challenging cooperative tasks in highly unpredictable environments while complying with the current needs, intentions, and capabilities of the human.

Abstract (italian)

Negli ultimi anni si è assistito a un radicale cambiamento negli obiettivi della ricerca robotica.
Agli albori della robotica moderna, storicamente collocati nel contesto del boom economico, lo sviluppo dei dispositivi robotici era guidato dall’esigenza industriale di ridurre tempi e costi di produzione per ottenere quantitativi sempre maggiori. Spesso questo coincideva con l’esigenza di sviluppare dispositivi robotici per sostituire gli uomini nello svolgimento di mansioni semplici e ripetitive. Questa esigenza portava poi alla progettazione di ambienti dedicati intorno ai sistemi robotici.
Più recentemente vi è stato un progressivo interesse verso la robotica di nuovi settori quali la riabilitazione, l’assistenza agli anziani, la chirurgia. In questi ambiti il ruolo del dispositivo cambia radicalmente: non è più solo uno strumento da utilizzare, ma diventa un partner con cui lavorare fianco a fianco. Pertanto, il dispositivo deve essere capace di cooperare attivamente ed efficacemente con le persone, comprendendone le esigenze ed aiutandole al fine di ottenere obiettivi condivisi in ambienti non strutturati come quelli in cui quotidianamente ci muoviamo.
Lo stato attuale del mercato vede robot utilizzati in diversi campi di applicazione, come robot chirurgici, dispositivi riabilitativi, manipolatori flessibili e robot di servizio e assistenziali ma essi sono ancora spesso semplici strumenti per svolgere specifici compiti. L’attuale sfida aperta è pertanto quella di sviluppare una nuova generazione di robot che sappiano invece essere partner, cooperando in simbiosi con l’uomo. In altre parole, l’obiettivo di ricerca è quello di fornire ai dispositivi robotici la capacità di rilevare, comprendere ed adattarsi alle reali intenzioni, capacità ed esigenze degli esseri umani.
Questa cooperazione simbiotica richiede uno scambio bidirezionale di informazioni tra l’uomo e il dispositivo. Da un lato, il dispositivo necessita di essere informato circa le necessità, le capacità e le intenzioni dell’essere umano. Dall’altro lato, l’uomo deve essere informato circa il proprio stato e le intenzioni del dispositivo con cui sta cooperando. Da tali considerazioni, tuttavia, emerge chiaramente la necessità di attingere ed integrare i contributi forniti dalla ricerca della comunità biomeccanica.
Questi obiettivi sono quelli che hanno guidato le attività condotte durante il periodo di studio del mio dottorato e che sono riportate, insieme ai risultati ottenuti, in questo elaborato. Tali attività hanno portato a progettare, sviluppare e realizzare un nuovo framework multilivello volto a chiudere l’anello di cooperazione tra essere umano e dispositivo robotico, di fatto promuovendo la loro interazione simbiotica.
Al fine di raggiungere tale obbiettivo, sono stati identificati tre livelli principali all’interno del framework multilivello: il livello di misura, il livello di modellazione ed il livello di estrazione/sintesi delle informazioni. Il primo mira a raccogliere misure sperimentali dall’intero sistema cooperante; il secondo a stimare e prevedere il suo comportamento dinamico; l’ultimo a fornire informazioni quantitative sia all’uomo che al dispositivo in merito alle loro prestazioni e a come modificare il loro comportamento per migliorare la loro simbiosi.
Nell’ambito del livello di misura, l’attenzione si è concentrata sull’analisi, sul confronto critico e sulla scelta di tecnologie indossabili e minimamente invasive per misurare al meglio la cinematica e la dinamica. Inoltre, protocolli e procedure già sviluppati e riconosciuti come standard de-facto in altri campi sono stati adattati con successo alle esigenze del contesto dell’interazione uomo-macchina. Infine, è stata avviata la progettazione di un nuovo sensore per colmare la mancanza di strumenti in grado di misurare efficacemente le forze emergenti dall’interazione dinamica tra uomo e dispositivo robotico indossabile. In tale contesto, infatti, gli attuali dispositivi di misura non risultano essere utilizzabili senza interferire con l’interazione stessa.
Al fine di realizzare il livello di modellazione, è stata innanzitutto selezionata ed ampiamente validata una piattaforma software che fosse in grado di eseguire simulazioni integrate multilivello, cioè simulazioni in cui il dispositivo e l’uomo sono considerati contemporaneamente come entità interagenti. Inoltre, sono stati studiati gli aspetti critici che caratterizzano la modellazione del dispositivo, dell’umano e delle loro interazioni e sono state proposte possibili soluzioni per affrontarli. Ad esempio, la modellazione della meccanica e dei sistemi di controllo dei dispositivi, realizzata attraverso gli strumenti messi a disposizione dalla piattaforma software selezionata, ha permesso di ottenere stime accurate del loro comportamento dinamico. Per stimare il comportamento umano, invece, sono state sviluppate, validate e rilasciate come strumenti open-source alla comunità scientifica nuove metodologie e nuovi approcci basati su modelli anatomici neuromuscoloscheletrici. Tale lavoro ha consentito di ottenere stime accurate sia della cinematica che della dinamica in tempo reale, cioè nello stesso istante in cui i movimenti vengono eseguiti. Al fine di stimare la cinematica articolare dell’uomo, nel corso del mio dottorato ho sviluppato e convalidato un approccio di cinematica inversa basato su un modello muscoloscheletrico anatomicamente attendibile, che utilizza come input le misure di orientazione fornite dai sistemi inerziali indossabili. Inoltre, lo strumento di modellazione neuromuscoloscheletrica che rappresenta l’attuale stato dell’arte in ambito biomeccanico è stato migliorato ed interfacciato con gli altri strumenti del framework multilivello. Il lavoro svolto ha consentito di prevedere con precisione ed in tempo reale le forze muscolari, le coppie articolari, e la rigidità muscolare ed articolare dell’essere umano a partire da misure elettromiografiche e cinematiche. Per stimare e prevedere le interazioni, infine, sono stati studiati, sviluppati ed applicati modelli di contatto, procedure di ottimizzazione dei parametri e strategie di cooperazione ad alto livello volte ad incrementare la simbiosi tra essere umano e dispositivo robotico.
Nell’ambito del livello di estrazione/sintesi delle informazioni, le misure e le stime ottenute attraverso gli strumenti realizzati negli altri livelli sono state combinate per ottenere accurati feedback quantitativi sia per il dispositivo che per le persone. Da un lato, al dispositivo sono stati forniti segnali di controllo volti a modulare il supporto al fine di soddisfare al meglio gli obiettivi dell’attività in corso di svolgimento, nel rispetto delle reali capacità ed esigenze umane. Dall’altro lato, sono stati sviluppati feedback quantitativi per informare l’utente sulle proprie prestazioni nell’esecuzione dei compiti, sugli obiettivi delle attività e sul supporto fornito dal dispositivo. Tali informazioni sono state fornite all’utente sotto forma di feedback visivi, concepiti per essere esaustivi senza però distrarre l’attenzione, al fine di evitare eventuali perdite di concentrazione e coinvolgimento. Inoltre, sono stati definiti ulteriori feedback volti ad aiutare gli osservatori esterni, quali terapisti in contesti riabilitativi o gestionali ed ergonomisti in campo industriale, nella progettazione e nel perfezionamento di attività personalizzate ed obiettivi a lungo termine.
Tutti gli strumenti hardware e software appartenenti ai diversi livelli sono stati poi integrati sviluppando un framework software modulare, flessibile ed affidabile, basato su un noto middleware robotico, al fine di gestire i processi di comunicazione e scambio di informazioni.
Infine, il framework sviluppato nel corso del mio dottorato è stato specializzato per realizzare un’applicazione di riabilitazione della camminata assistita da un dispositivo esoscheletrico. Questo contesto è stato scelto perché la cooperazione simbiotica è fondamentale per raggiungere l’obiettivo finale: massimizzare l’efficacia del percorso riabilitativo che deve essere dinamicamente adattato per seguire al meglio le mutevoli esigenze e capacità del paziente mantenendolo allo stesso tempo coinvolto e concentrato.
La specializzazione del framework multilivello proposto è stata utilizzata con successo per realizzare gli obiettivi del progetto Europeo Biomot. All’interno di tale progetto, infatti, abbiamo sviluppato un innovativo prototipo di esoscheletro indossabile per la riabilitazione della camminata in grado di modulare in tempo reale il supporto fornito, seguendo diverse strategie di cooperazione ed in funzione delle esigenze e capacità dell’utente. Allo stesso tempo, l’utente risulta essere coinvolto attivamente nel proprio processo di riabilitazione attraverso accattivanti feedback visivi sulle sue prestazioni nel raggiungimento degli obiettivi di riabilitazione e sul sostegno fornitogli dell’esoscheletro. Il framework si è dimostrato fondamentale per chiudere l’anello di informazioni che collega utente e dispositivo e per fornire preziosi feedback quantitativi agli osservatori esterni.
Sia i ricercatori che gli esperti clinici che hanno valutato l’applicazione riabilitativa del framework multilivello hanno fornito feedback entusiasti in merito alle soluzioni proposte e ai risultati ottenuti. Pertanto, si può affermare che il framework multilivello sviluppato in questa tesi ha le potenzialità di avanzare l’attuale stato dell’arte nell’ambito dell’interazione simbiotica uomo–macchina. Infatti, tale framework potrà supportare lo sviluppo di una nuova generazione di dispositivi robotici capaci di cooperare con l’uomo nell’esecuzione di compiti impegnativi in ambienti non strutturati, nel rispetto delle reali esigenze, intenzioni e capacità di quest’ultimo.

Statistiche Download
EPrint type:Ph.D. thesis
Tutor:Reggiani, Monica
Ph.D. course:Ciclo 29 > Corsi 29 > INGEGNERIA MECCATRONICA E DELL'INNOVAZIONE MECCANICA DEL PRODOTTO
Data di deposito della tesi:26 January 2018
Anno di Pubblicazione:26 January 2018
Key Words:human-robot interaction, interazione uomo-robot, exoskeleton, esoscheletro, riabilitazione robotica, robotic rehabilitation, multilevel framework, neuromusculoskeletal models, human kinematics, human dynamics
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Struttura di riferimento:Dipartimenti > Dipartimento di Tecnica e Gestione dei Sistemi Industriali
Codice ID:11064
Depositato il:25 Oct 2018 16:50
Simple Metadata
Full Metadata
EndNote Format

Bibliografia

I riferimenti della bibliografia possono essere cercati con Cerca la citazione di AIRE, copiando il titolo dell'articolo (o del libro) e la rivista (se presente) nei campi appositi di "Cerca la Citazione di AIRE".
Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

[1] Aghili, F., Buehler, M., and Hollerbach, J. “A joint torque sensor for robots.” In: ASME International Mechanical Engineering Congress & Exposition. 1997. Cerca con Google

[2] Aguirre-Ollinger, G., Colgate, J. E., Peshkin, M. A., and Goswami, A. “Design of an active one-degree-of-freedom lower-limb exoskeleton with inertia compensation.” In: The International Journal of Robotics Research 30.4 (2011), pp. 486–499. Cerca con Google

[3] Akhras, M. A., Bortoletto, R., Madehkhaksar, F., and Tagliapietra, Luca. “Neural and musculoskeletal modeling: its role in neurorehabilitation.” In: Emerging Therapies in Neurorehabilitation II. Springer, 2016. Chap. 5, pp. 109–143. Cerca con Google

[4] Aldebaran Robotics. Nao Robotic Platform. (Last access: Nov. 2017. url: https://www.ald.softbankrobotics.com/en/ robots/nao. Vai! Cerca con Google

[5] Altman, R., Natis, Y., Hill, J., Klein, J., Lheureux, B., Pezzini, M., Schulte, R., and Varma, S. “Middleware: The Glue for Modern Applications.” In: Gartner Group, Strategic Analysis Report 26 (1999). Cerca con Google

[6] Ama, A. J. del, Bravo-Esteban, E., Moreno, J. C., GómezSoriano, J., Piazza, S., Koutsou, A. D., Gil-Agudo, Á., and Pons, J. L. “Knee muscle fatigue estimation during isometric artificially elicited contractions in incomplete spinal cord injured subjects.” In: Converging Clinical and Engineering Research on Neurorehabilitation. Springer, 2013, pp. 327–332. Cerca con Google

[7] Amarantini, D. and Martin, L. “A method to combine numerical optimization and EMG data for the estimation of joint moments under dynamic conditions.” In: J. of Biomech. 37.9 (2004), pp. 1393–1404. Cerca con Google

[8] Armstrong-Helouvry, B. Control of machines with friction. Vol. 128. Springer Science & Business Media, 2012. Cerca con Google

[9] Arnold, E. M., Ward, S. R., Lieber, R. L., and Delp, S. L. “A model of the lower limb for analysis of human movement.” In: Ann Biomed Eng 38.2 (2010), pp. 269–79. Cerca con Google

[10] Asada, H. and Lim, S. “Design of joint torque sensors and torque feedback control for direct-drive arms.” In: ASME Winter Annual Meeting: Robotics and Manufacturing Automation. 1985, pp. 277–284. Cerca con Google

[11] Åström, K. J. and Furuta, K. “Swinging up a pendulum by energy control.” In: Automatica 36.2 (2000), pp. 287–295. Cerca con Google

[12] Bacek, T., Moltedo, M., Langlois, K., Prieto, G. A., SanchezVillamañan, M. C., Gonzalez-Vargas, J., Vanderborght, B., Lefeber, D., and Moreno, J. C. “BioMot exoskeleton—Towards a smart wearable robot for symbiotic human-robot interaction.” In: Rehabilitation Robotics (ICORR), 2017 International Conference on. IEEE. 2017, pp. 1666–1671. Cerca con Google

[13] Bajd, T., Lenarcˇicˇ, J., Munih, M., et al. Robotics. Vol. 43. Springer Science & Business Media, 2010. Cerca con Google

[14] Barrett, S. and Kridner, J. “Bad to the Bone: Crafting Electronic Systems with BeagleBone Black.” In: Synthesis Lectures on Digital Circuits and Systems 10.3 (2015), pp. 1–417. Cerca con Google

[15] Bayo, E. and Avello, A. “Singularity-free augmented Lagrangian algorithms for constrained multibody dynamics.” In: Nonlinear Dyn. 5.2 (1994), pp. 209–231. Cerca con Google

[16] Blankevoort, L., Kuiper, J., Huiskes, R., and Grootenboer, H. “Articular contact in a three-dimensional model of the knee.” In: Journal of biomechanics 24.11 (1991), pp. 1019–1031. Cerca con Google

[17] Blanton, S. and Wolf, S. L. “An application of upper-extremity constraint-induced movement therapy in a patient with subacute stroke.” In: Physical therapy 79.9 (1999), pp. 847–853. Cerca con Google

[18] Blaya, J. and Herr, H. “Adaptive control of a variableimpedance ankle-foot orthosis to assist drop-foot gait.” In: Neural Sys. and Rehab. Eng., IEEE Trans. on 12.1 (2004), pp. 24– 31. Cerca con Google

[19] Borbély, B. J. and Szolgay, P. “Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations.” In: Biomedical engineering online 16.1 (2017), p. 21. Cerca con Google

[20] Bortole, M., Del Ama, A., Rocon, E., Moreno, J. C., Brunetti, F., and Pons, J. L. “A robotic exoskeleton for overground gait rehabilitation.” In: Robotics and Automation (ICRA), 2013 IEEE International Conference on. IEEE. 2013, pp. 3356–3361. Cerca con Google

[21] Bortole, M., Venkatakrishnan, A., Zhu, F., Moreno, J. C., Francisco, G. E., Pons, J. L., and Contreras-Vidal, J. L. “The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study.” In: Journal of neuroengineering and rehabilitation 12.1 (2015), p. 54. Cerca con Google

[22] Bricard, R. “Mémoire sur la théorie de l’octaèdre articulé.” In: J. de Mathématiques pures et appliquées (1897), pp. 113–148. Cerca con Google

[23] Bright, F. A., Kayes, N. M., Worrall, L., and McPherson, K. M. “A conceptual review of engagement in healthcare and rehabilitation.” In: Disability and rehabilitation 37.8 (2015), pp. 643– 654. Cerca con Google

[24] Bruyninckx, H. “Open robot control software: the OROCOS project.” In: Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on. Vol. 3. IEEE. 2001, pp. 2523–2528. Cerca con Google

[25] BTS. BTS Bioengineering Homepage. (Last access: Nov. 2017. url: https://www.btsbioengineering.com/. Vai! Cerca con Google

[26] Buchanan, T. S., Lloyd, D. G., Manal, K., and Besier, T. F. “Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command.” In: Journal of applied biomechanics 20.4 (2004), pp. 367–395. Cerca con Google

[27] Burden, A. “Surface electromyography.” In: Biomechanical evaluation of movement in sport and exercise (2007), p. 77. Cerca con Google

[28] Burden, A. “How should we normalize electromyograms obtained from healthy participants? What we have learned from over 25years of research.” In: Journal of electromyography and kinesiology 20.6 (2010), pp. 1023–1035. Cerca con Google

[29] Cain, S. M., Gordon, K. E., and Ferris, D. P. “Locomotor adaptation to a powered ankle-foot orthosis depends on control method.” In: Journal of NeuroEngineering and Rehabilitation 4.1 (2007), p. 48. Cerca con Google

[30] Cannata, G., Maggiali, M., Metta, G., and Sandini, G. “An embedded artificial skin for humanoid robots.” In: Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 2008. IEEE International Conference on. IEEE. 2008, pp. 434–438. Cerca con Google

[31] Cappozzo, A., Catani, F., Della Croce, U., and Leardini, A. “Position and orientation in space of bones during movement: anatomical frame definition and determination.” In: Clinical Biomech 10.4 (1995), pp. 171–178. Cerca con Google

[32] Cappozzo, A., Della Croce, U., Leardini, A., and Chiari, L. “Human movement analysis using stereophotogrammetry: Part 1: theoretical background.” In: Gait & posture 21.2 (2005), pp. 186–196. Cerca con Google

[33] Cavallo, A., Cirillo, A., Cirillo, P., De Maria, G., Falco, P., Natale, C., and Pirozzi, S. “Experimental comparison of sensor fusion algorithms for attitude estimation.” In: IFAC Proceedings Volumes 47.3 (2014), pp. 7585–7591. Cerca con Google

[34] CEINMS Developers. CEINMS – Calibrated EMG-Informed NMS Modeling Toolbox Github repository. (Last access: Nov. 2017. 2015. url: https://github.com/CEINMS/CEINMS. Vai! Cerca con Google

[35] Ceseracciu, E., Mantoan, A., Bassa, M., Moreno, J. C., Pons, J. L., Prieto, G. A., Ama, A. J. del, Marquez-Sanchez, E., GilAgudo, Á., Pizzolato, C., et al. “A flexible architecture to enhance wearable robots: integration of EMG-informed models.” In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. IEEE. 2015, pp. 4368–4374. Cerca con Google

[36] Ceseracciu, E., Tagliapietra, Luca, Moreno, J. C., Asin, G., delAma, A. J., Pérez, S., Piñuela, E., Gil, Á., and Reggiani, M. “An EMG-informed Model to Evaluate Assistance of the Biomot Compliant Ankle Actuator.” In: Wearable Robotics: Challenges and Trends. Springer, 2017, pp. 261–265. Cerca con Google

[37] Cholewicki, J., McGill, S., and Norman, R. “Comparison of muscle forces and joint load from an optimization and EMG assisted lumbar spine model: towards development of a hybrid approach.” In: J. of Biomech. 28.3 (1995), pp. 321–331. Cerca con Google

[38] Chow, G. C. et al. Analysis and control of dynamic economic systems. Wiley, 1975. Cerca con Google

[39] Clarys, J. P. “Electromyography in sports and occupational settings: an update of its limits and possibilities.” In: Ergonomics 43.10 (2000), pp. 1750–1762. Cerca con Google

[40] Clarys, J. P. and Cabri, J. “Electromyography and the study of sports movements: a review.” In: Journal of sports sciences 11.5 (1993), pp. 379–448. Cerca con Google

[41] Clement, J., Dumas, R., Hagemeister, N., and De Guise, J. A. “Soft tissue artifact compensation in knee kinematics by multibody optimization: Performance of subject-specific knee joint models.” In: Biomech J of 48.14 (2015), pp. 3796–3802. Cerca con Google

[42] Close, J. R. Motor function in the lower extremity: analysis by electronic instrumentation. Thomas, 1964. Cerca con Google

[43] Close, J. and Todd, F. “The phasic activity of the muscles of the lower extremity and the effect of tendon transfer.” In: JBJS 41.2 (1959), pp. 189–235. Cerca con Google

[44] Colombo, G., Joerg, M., Schreier, R., and Dietz, V. “Treadmill training of paraplegic patients using a robotic orthosis.” In: Journal of rehabilitation research and development 37.6 (2000), p. 693. Cerca con Google

[45] Corcos, D. M., Gottlieb, G. L., Latash, M. L., Almeida, G. L., and Agarwal, G. C. “Electromechanical delay: an experimental artifact.” In: Journal of Electromyography and Kinesiology 2.2 (1992), pp. 59–68. Cerca con Google

[46] Craig, J. J. Introduction to robotics: mechanics and control. Vol. 3. Pearson Prentice Hall Upper Saddle River, 2005. Cerca con Google

[47] Cromwell, R., Schultz, A., Beck, R., and Warwick, D. “Loads on the lumbar trunk during level walking.” In: J. of Orthopaedic Research 7.3 (1989), pp. 371–377. Cerca con Google

[48] Davis, R. B., Ounpuu, S., Tyburski, D., and Gage, J. R. “A gait analysis data collection and reduction technique.” In: Human Movement Science 10.5 (1991), pp. 575–587. Cerca con Google

[49] De Luca, C. J. “The use of surface electromyography in biomechanics.” In: Journal of applied biomechanics 13.2 (1997), pp. 135– 163. Cerca con Google

[50] Defence Advanced Research Projet Agency – DARPA. DARPA Robotics Challenge. (Last access: Nov. 2017. 2017. url: https:// www.darpa.mil/program/darpa-robotics-challenge. Vai! Cerca con Google

[51] Del Din, S., Carraro, E., Sawacha, Z., Guiotto, A., Bonaldo, L., Masiero, S., and Cobelli, C. “Impaired gait in ankylosing spondylitis.” In: Med. & Biological Eng. & Computing 49.7 (2011), pp. 801–809. Cerca con Google

[52] Delp, S., Anderson, F., Arnold, A., Loan, P., Habib, A., John, C., Guendelman, E., and Thelen, D. “OpenSim: open-source software to create and analyze dynamic simulations of movement.” In: Biomed. Eng., IEEE Trans. on 54.11 (2007), pp. 1940– 1950. Cerca con Google

[53] Di Marco, R., Rossi, S., Castelli, E., Patané, F., Mazzá, C., and Cappa, P. “Effects of the calibration procedure on the metrological performances of stereophotogrammetric systems for human movement analysis.” In: Measurement 101 (2017), pp. 265–271. Cerca con Google

[54] Diebel, J. “Representing attitude: Euler angles, unit quaternions, and rotation vectors.” In: Matrix 58.15-16 (2006), pp. 1– 35. Cerca con Google

[55] Dollar, A. M. and Herr, H. “Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art.” In: IEEE Transactions on robotics 24.1 (2008), pp. 144–158. Cerca con Google

[56] Donati, M., Vitiello, N., De Rossi, S. M. M., Lenzi, T., Crea, S., Persichetti, A., Giovacchini, F., Koopman, B., Podobnik, J., Munih, M., et al. “A flexible sensor technology for the distributed measurement of interaction pressure.” In: Sensors 13.1 (2013), pp. 1021–1045. Cerca con Google

[57] Duprey, S., Cheze, L., and Dumas, R. “Influence of joint constraints on lower limb kinematics estimation from skin markers using global optimization.” In: Biomech J of 43.14 (2010), pp. 2858–2862. Cerca con Google

[58] Einhorn, E., Langner, T., Stricker, R., Martin, C., and Gross, H.-M. “Mira-middleware for robotic applications.” In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. IEEE. 2012, pp. 2591–2598. Cerca con Google

[59] Elkady, A. and Sobh, T. “Robotics middleware: A comprehensive literature survey and attribute-based bibliography.” In: Journal of Robotics 2012 (2012). Cerca con Google

[60] Emken, J. L., Benitez, R., and Reinkensmeyer, D. J. “Humanrobot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistanceas-needed.” In: Journal of NeuroEngineering and Rehabilitation 4.1 (2007), p. 8. Cerca con Google

[61] Enoka, R. M. Neuromechanics of human movement. Human kinetics, 2008. Cerca con Google

[62] Esquenazi, A., Talaty, M., Packel, A., and Saulino, M. “The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury.” In: American journal of physical medicine & rehabilitation 91.11 (2012), pp. 911–921. Cerca con Google

[63] Fernandez, J., Zhang, J., Heidlauf, T., Sartori, M., Besier, T., Röhrle, O., and Lloyd, D. “Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling.” In: Interface focus 6.2 (2016), p. 20150084. Cerca con Google

[64] Ferrari, A., Benedetti, M. G., Pavan, E., Frigo, C., Bettinelli, D., Rabuffetti, M., Crenna, P., and Leardini, A. “Quantitative comparison of five current protocols in gait analysis.” In: Gait & Posture 28.2 (2008), pp. 207–216. Cerca con Google

[65] Ferris, D., Gordon, K., Sawicki, G., and Peethambaran, A. “An improved powered ankle-foot orthosis using proportional myoelectric control.” In: Gait & posture 23.4 (2006), pp. 425–428. Cerca con Google

[66] Fitzpatrick, P., Ceseracciu, E., Domenichelli, D., Paikan, A., Metta, G., and Natale, L. “A middle way for robotics middleware.” In: Journal of Software Engineering for Robotics 5.2 (2014), pp. 42–49. Cerca con Google

[67] Fitzpatrick, P., Metta, G., and Natale, L. “Towards long-lived robot genes.” In: Robotics and Autonomous systems 56.1 (2008), pp. 29–45. Cerca con Google

[68] Fleischer, C. and Hommel, G. “A human–exoskeleton interface utilizing electromyography.” In: IEEE Transactions on Robotics 24.4 (2008), pp. 872–882. Cerca con Google

[69] Furuta, K., Yamakita, M., and Kobayashi, S. “Swing-up control of inverted pendulum using pseudo-state feedback.” In: Proceedings of the Institution of Mechanical Engineers, Part I: J. of Systems and Control Engineering 206.4 (1992), pp. 263–269. Cerca con Google

[70] Garcia, E., Jimenez, M. A., De Santos, P. G., and Armada, M. “The evolution of robotics research.” In: IEEE Robotics & Automation Magazine 14.1 (2007), pp. 90–103. Cerca con Google

[71] Gazebo Opensource Project. Gazebo Homepage. (Last access: Nov. 2017. url: http://www.gazebosim.org. Vai! Cerca con Google

[72] Genovese, K., Lamberti, L., and Pappalettere, C. “Improved global–local simulated annealing formulation for solving nonsmooth engineering optimization problems.” In: International Journal of solids and Structures 42.1 (2005), pp. 203–237. Cerca con Google

[73] Gerkey, B., Vaughan, R. T., and Howard, A. “The player/stage project: Tools for multi-robot and distributed sensor systems.” In: Proceedings of the 11th international conference on advanced robotics. Vol. 1. 2003, pp. 317–323. Cerca con Google

[74] Gerus, P., Sartori, M., Besier, T. F., Fregly, B. J., Delp, S. L., Banks, S. A., Pandy, M. G., D’Lima, D. D., and Lloyd, D. G. “Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces.” In: Journal of biomechanics 46.16 (2013), pp. 2778–2786. Cerca con Google

[75] Gonzalez-Vargas, J., Shimoda, S., Asín-Prieto, G., Pons, J. L., and Moreno, J. C. “Joint stiffness modulation of compliant actuators for lower limb exoskeletons.” In: Rehabilitation Robotics (ICORR), 2017 International Conference on. IEEE. 2017, pp. 1287– 1292. Cerca con Google

[76] González, M., González, F., Luaces, A., and Cuadrado, J. “A collaborative benchmarking framework for multibody system dynamics.” In: Eng. with Computers 26.1 (2010), pp. 1–9. Cerca con Google

[77] González, M., Dopico, D., Lugrís, U., and Cuadrado, J. “A benchmarking system for MBS simulation software: Problem standardization and performance measurement.” In: Multibody System Dynamics 16.2 (2006), pp. 179–190. Cerca con Google

[78] Grood, E. S. and Suntay, W. J. “A Joint Coordinate System for the Clinical Description of Three-Dimensional Motions: Application to the Knee.” In: Biomech Eng J of 105.2 (1983), pp. 136– 144. Cerca con Google

[79] Grübler, M. Allgemeine Eigenschaften der zwangläufigen ebenen kinematischen Ketten. L. Simion, 1884. Cerca con Google

[80] Hadim, S. and Mohamed, N. “Middleware: Middleware challenges and approaches for wireless sensor networks.” In: IEEE distributed systems online 7.3 (2006), pp. 1–1. Cerca con Google

[81] Harada, T., Mori, T., and Sato, T. “Development of a Tiny Orientation Estimation Device to Operate under Motion and Magnetic Disturbance.” In: Robotics Research Int. J. of 26.6 (2007), pp. 547–559. Cerca con Google

[82] Hashimoto, M., Kiyosawa, Y., and Paul, R. P. “A torque sensing technique for robots with harmonic drives.” In: IEEE Transactions on Robotics and Automation 9.1 (1993), pp. 108–116. Cerca con Google

[83] Heckathorne, C. W. and Childress, D. S. “Relationships of the surface electromyogram to the force, length, velocity, and contraction rate of the cineplastic human biceps.” In: American Journal of Physical Medicine & Rehabilitation 60.1 (1981), 1– hyhen. Cerca con Google

[84] Hermens, H. J., Freriks, B., Merletti, R., Stegeman, D., Blok, J., Rau, G., Disselhorst-Klug, C., and Hägg, G. “European recommendations for surface electromyography.” In: Roessingh research and development 8.2 (1999), pp. 13–54. Cerca con Google

[85] Hertz, H. “On the contact of elastic solids.” In: J. Reine Angew Math. 92 (1881), pp. 156–171. Cerca con Google

[86] Hill, A. “The heat of shortening and the dynamic constants of muscle.” In: Proceedings of the Royal Society of London. Series B, Biological Sciences (1938), pp. 136–195. Cerca con Google

[87] Hirose, S. and Yoneda, K. “Development of optical six-axial force sensor and its signal calibration considering nonlinear interference.” In: Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on. IEEE. 1990, pp. 46–53. Cerca con Google

[88] Hitt, J., Oymagil, A., Sugar, T., Hollander, K., Boehler, A., and Fleeger, J. “Dynamically controlled ankle-foot orthosis (DCO) with regenerative kinetics: Incrementally attaining user portability.” In: Robotics and Automation, 2007. IEEE Int. Conf. on. Apr. 2007. Cerca con Google

[89] Hof, A. and Van den Berg, J. “EMG to force processing I: an electrical analogue of the Hill muscle model.” In: J. of Biomech. 14.11 (1981), pp. 747–758. Cerca con Google

[90] Hu, X., Murray, W. M., and Perreault, E. J. “Muscle shortrange stiffness can be used to estimate the endpoint stiffness of the human arm.” In: Journal of neurophysiology 105.4 (2011), pp. 1633–1641. Cerca con Google

[91] Huang, A. S., Olson, E., and Moore, D. C. “LCM: Lightweight communications and marshalling.” In: Intelligent robots and systems (IROS), 2010 IEEE/RSJ international conference on. IEEE. 2010, pp. 4057–4062. Cerca con Google

[92] Hunt, K. and Crossley, F. “Coefficient of restitution interpreted as damping in vibroimpact.” In: Journal of applied mechanics 42.2 (1975), pp. 440–445. Cerca con Google

[93] iMatix Corporation. 0MQ, The Intelligent Transport Layer. (Last access: Nov. 2017. url: http://zeromq.org. Vai! Cerca con Google

[94] Inoue, H. In: Siciliano, B. and Khatib, O. Springer handbook of robotics. Springer, 2016. Chap. Forword, pp. XIII–XIV. Cerca con Google

[95] Ivanenko, Y. P., Poppele, R. E., and Lacquaniti, F. “Motor control programs and walking.” In: The Neuroscientist 12.4 (2006), pp. 339–348. Cerca con Google

[96] Jacobsen, S., Smith, F., Backman, D., and Iversen, E. “High performance, high dexterity, force reflective teleoperator II.” In: ANS topical meeting on robotics and remote systems. 1991, pp. 24– 27. Cerca con Google

[97] Jezernik, S., Colombo, G., Keller, T., Frueh, H., and Morari, M. “Robotic orthosis lokomat: A rehabilitation and research tool.” In: Neuromodulation: Tech. at the Neural Interface 6.2 (2003), pp. 108–115. Cerca con Google

[98] Johnson, K. L. and Johnson, K. L. Contact mechanics. Cambridge university press, 1987. Cerca con Google

[99] Kadaba, M. P., Ramakrishnan, H., and Wootten, M. “Measurement of lower extremity kinematics during level walking.” In: Orthopaedic research J of 8.3 (1990), pp. 383–392. Cerca con Google

[100] Kamen, G. and Gabriel, D. Essentials of electromyography. Human kinetics, 2010. Cerca con Google

[101] Kawakami, Y., Ichinose, Y., and Fukunaga, T. “Architectural and functional features of human triceps surae muscles during contraction.” In: Journal of Applied Physiology 85.2 (1998), pp. 398–404. Cerca con Google

[102] Khaleghi, B., Khamis, A., Karray, F. O., and Razavi, S. N. “Multisensor data fusion: A review of the state-of-the-art.” In: Information Fusion 14.1 (2013), pp. 28–44. Cerca con Google

[103] Klette, R. and Tee, G. “Understanding human motion: A historic review.” In: Computational Imaging and Vision 36 (2008), p. 1. Cerca con Google

[104] Koning, B. H., Krogt, M. M. van der, Baten, C. T., and Koopman, B. F. “Driving a musculoskeletal model with inertial and magnetic measurement units.” In: Comput Methods Biomech Biomed Engin 18.9 (2015), pp. 1003–1013. Cerca con Google

[105] Koo, T. and Mak, A. “Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow.” In: J. of Electromyography and Kinesiology 15.1 (2005), pp. 12–26. Cerca con Google

[106] Kramer, J. and Scheutz, M. “Development environments for autonomous mobile robots: A survey.” In: Autonomous Robots 22.2 (2007), pp. 101–132. Cerca con Google

[107] Kwakernaak, H. and Sivan, R. Linear optimal control systems. Vol. 1. Wiley-Interscience New York, 1972. Cerca con Google

[108] Kwakkel, G., Peppen, R. van, Wagenaar, R., Dauphinee, S., Richards, C., Ashburn, A., Miller, K., Lincoln, N., Partridge, C., Wellwood, I., et al. “Effects of augmented exercise therapy time after stroke a meta-analysis.” In: Stroke 35.11 (2004), pp. 2529–2539. Cerca con Google

[109] Lamberto, G., Martelli, S., Cappozzo, A., and Mazzá, C. “To what extent is joint and muscle mechanics predicted by musculoskeletal models sensitive to soft tissue artefacts?” In: Journal of biomechanics (2016). Cerca con Google

[110] Latash, M. L. “Motor synergies and the equilibrium-point hypothesis.” In: Motor control 14.3 (2010), pp. 294–322. Cerca con Google

[111] Lee, M. H. and Nicholls, H. R. “Tactile Sensing for Mechatronics – A State of the Art Survey.” In: Mechatronics 9 (1999), pp. 1– 31. Cerca con Google

[112] Lehman, G. J. and McGill, S. M. “The importance of normalization in the interpretation of surface electromyography: a proof of principle.” In: Journal of manipulative and physiological therapeutics 22.7 (1999), pp. 444–446. Cerca con Google

[113] Lewis, C. L. and Ferris, D. P. “Invariant hip moment pattern while walking with a robotic hip exoskeleton.” In: Journal of biomechanics 44.5 (2011), pp. 789–793. Cerca con Google

[114] Ligorio, G., Bergamini, E., Pasciuto, I., Vannozzi, G., Cappozzo, A., and Sabatini, A. M. “Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-InertialBased Human Body Tracking.” In: Sensors (Basel) 16.2 (2016), p. 153. Cerca con Google

[115] Lloyd, D., Besier, T., Winby, C., and Buchanan, T. “Neuromusculoskeletal modelling and simulation of tissue load in the lower extremities.” In: Handbook Biomech. and Human Movement Science (2008), pp. 3–17. Cerca con Google

[116] Lloyd, D. and Buchanan, T. “A model of load sharing between muscles and soft tissues at the human knee during static tasks.” In: J. of Biomech. Eng. 118.3 (1996), pp. 367–376. Cerca con Google

[117] Lloyd, D. G. and Besier, T. F. “An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.” In: Journal of biomechanics 36.6 (2003), pp. 765–776. Cerca con Google

[118] Lu, T.-W. and Chang, C.-F. “Biomechanics of human movement and its clinical applications.” In: The Kaohsiung journal of medical sciences 28.2 (2012), S13–S25. Cerca con Google

[119] Luh, J., Fisher, W., and Paul, R. “Joint torque control by a direct feedback for industrial robots.” In: IEEE Transactions on Automatic Control 28.2 (1983), pp. 153–161. Cerca con Google

[120] MacDonald, G. A., Kayes, N. M., and Bright, F. “Barriers and facilitators to engagement in rehabilitation for people with stroke: a review of the literature.” In: New Zealand Journal of Physiotherapy 41.3 (2013), pp. 112–121. Cerca con Google

[121] Madgwick, S. O., Harrison, A. J., and Vaidyanathan, R. “Estimation of IMU and MARG orientation using a gradient descent algorithm.” In: Rehabilitation Robotics (ICORR), 2011 IEEE International Conference on. IEEE. 2011, pp. 1–7. Cerca con Google

[122] Mahony, R., Hamel, T., and Pflimlin, J. M. “Nonlinear Complementary Filters on the Special Orthogonal Group.” In: Automatic Control IEEE Trans on 53.5 (2008), pp. 1203–1218. Cerca con Google

[123] Maiolino, P., Ascia, A., Maggiali, M., Natale, L., Cannata, G., and Metta, G. “Large scale capacitive skin for robots.” In: Smart Actuation and Sensing Systems-Recent Advances and Future Challenges. InTech, 2012. Cerca con Google

[124] Manal, K. and Buchanan, T. “A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms.” In: J. of Biomech. 36.8 (2003), pp. 1197–1202. Cerca con Google

[125] Manal, K. and Buchanan, T. “Modeling the non-linear relationship between EMG and muscle activation.” In: Journal of biomechanics 36 (2003), pp. 1197–1202. Cerca con Google

[126] Manal, K., Gonzalez, R. V., Lloyd, D. G., and Buchanan, T. S. “A real-time EMG-driven virtual arm.” In: Computers in biology and medicine 32.1 (2002), pp. 25–36. Cerca con Google

[127] Marchal-Crespo, L. and Reinkensmeyer, D. J. “Review of control strategies for robotic movement training after neurologic injury.” In: Journal of neuroengineering and rehabilitation 6.1 (2009), p. 20. Cerca con Google

[128] Marhefka, D. W. and Orin, D. E. “Simulation of contact using a nonlinear damping model.” In: Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on. Vol. 2. IEEE. 1996, pp. 1662–1668. Cerca con Google

[129] Mazzá, C., Donati, M., McCamley, J., Picerno, P., and Cappozzo, A. “An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking.” In: Gait & posture 35.1 (2012), pp. 138–142. Cerca con Google

[130] McIntyre, J., Mussa-Ivaldi, F., and Bizzi, E. “The control of stable postures in the multijoint arm.” In: Experimental brain research 110.2 (1996), pp. 248–264. Cerca con Google

[131] Mckerrow, P. Introduction to robotics. Addison-Wesley Longman Publishing Co., Inc., 1991. Cerca con Google

[132] McLean, S., Su, A., and Bogert, A. van den. “Development and validation of a 3-D model to predict knee joint loading during dynamic movement.” In: J. of Biomech. Eng. 125.6 (2003), pp. 864–874. Cerca con Google

[133] Merletti, R. and Di Torino, P. “Standards for reporting EMG data.” In: J Electromyogr Kinesiol 9.1 (1999), pp. 3–4. Cerca con Google

[134] Metta, G., Fitzpatrick, P., and Natale, L. “YARP: yet another robot platform.” In: International Journal of Advanced Robotic Systems 3.1 (2006), p. 8. Cerca con Google

[135] Metta, G., Sandini, G., Vernon, D., Natale, L., and Nori, F. “The iCub humanoid robot: an open platform for research in embodied cognition.” In: Proceedings of the 8th workshop on performance metrics for intelligent systems. ACM. 2008, pp. 50–56. Cerca con Google

[136] Middleware Resource Center. What is Middleware? Archived from the original on June 29, 2012. url: https://web.archive. org / web / 20120629211518 / http : / / www . middleware . org / whatis.html. Vai! Cerca con Google

[137] Milner-Brown, H., Stein, R., and Yemm, R. “The contractile properties of human motor units during voluntary isometric contractions.” In: The Journal of physiology 228.2 (1973), pp. 285– 306. Cerca con Google

[138] Milner-Brown, H., Stein, R., and Yemm, R. “The orderly recruitment of human motor units during voluntary isometric contractions.” In: The Journal of physiology 230.2 (1973), pp. 359– 370. Cerca con Google

[139] Mohamed, N., Al-Jaroodi, J., and Jawhar, I. “Middleware for robotics: A survey.” In: Robotics, Automation and Mechatronics, 2008 IEEE Conference on. Ieee. 2008, pp. 736–742. Cerca con Google

[140] Moltedo, M., Bacek, T., Junius, K., Vanderborght, B., and Lefeber, D. “Mechanical design of a lightweight compliant and adaptable active ankle foot orthosis.” In: Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE International Conference on. IEEE. 2016, pp. 1224–1229. Cerca con Google

[141] Moreno, J., Collantes, I., Asin, G., and Pons, J. “Design of better robotic tools adapted to stroke rehabilitation practice.” In: World Congress on Medical Physics and Biomedical Engineering. 2012. Cerca con Google

[142] Moreno, J. C., Asin, G., Pons, J., Cuypers, H., Vanderborght, B., Lefeber, D., Ceseracciu, E., Reggiani, M., Thorsteinsson, F., del-Ama, A., et al. “Symbiotic wearable robotic exoskeletons: the concept of the biomot project.” In: International Workshop on Symbiotic Interaction. Springer. 2014, pp. 72–83. Cerca con Google

[143] MSC Software. Admas Homepage. (Last access: Nov. 2017. url: http://www.mscsoftware.com/product/adams. Vai! Cerca con Google

[144] Nigg, B. M. and Herzog, W. Biomechanics of the musculo–skeletal system. John Wiley & Sons, 2007. Cerca con Google

[145] Nvidia Corporation. PhysX Homepage. (Last access: Nov. 2017. url: http://www.geforce.com/hardware/technology/physx. Vai! Cerca con Google

[146] Ogata, K. Discrete-time control systems. Vol. 8. Prentice-Hall Englewood Cliffs, NJ, 1995. Cerca con Google

[147] Ohmura, Y. and Kuniyoshi, Y. “Humanoid robot which can lift a 30kg box by whole body contact and tactile feedback.” In: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on. IEEE. 2007, pp. 1136–1141. Cerca con Google

[148] OMG. Common Object Request Broker Architecture (CORBA/IIOP). (Last access: Nov. 2017. url: http://www.omg.org/spec/ CORBA. Vai! Cerca con Google

[149] OpenSim. OpenSim Supports, Events, and Sources. (Last access: Nov. 2017. url: http://opensim.stanford.edu/cgi-bin/ support/. Vai! Cerca con Google

[150] Pandy, M. “Computer modeling and simulation of human movement.” In: Annual review of Biomed. Eng. 3.1 (2001), pp. 245–273. Cerca con Google

[151] Parmiggiani, A., Maggiali, M., Natale, L., Nori, F., Schmitz, A., Tsagarakis, N., Victor, J. S., Becchi, F., Sandini, G., and Metta, G. “The design of the iCub humanoid robot.” In: International journal of humanoid robotics 9.04 (2012), p. 1250027. Cerca con Google

[152] Pentland, W., McColl, M., and Rosenthal, C. “The effect of aging and duration of disability on long term health outcomes following spinal cord injury.” In: Spinal Cord 33.7 (1995), pp. 367–373. Cerca con Google

[153] Perry, J., Easterday, C. S., and Antonelli, D. J. “Surface versus intramuscular electrodes for electromyography of superficial and deep muscles.” In: Physical therapy 61.1 (1981), pp. 7–15. Cerca con Google

[154] Pfeifer, R., Lungarella, M., and Iida, F. “The challenges ahead for bio-inspired’soft’robotics.” In: Communications of the ACM 55.11 (2012), pp. 76–87. Cerca con Google

[155] Piazza, S. and Delp, S. “The influence of muscles on knee flexion during the swing phase of gait.” In: J. of Biomech. 29.6 (1996), pp. 723–733. Cerca con Google

[156] Piazza, S. and Delp, S. “Three-dimensional dynamic simulation of total knee replacement motion during a step-up task.” In: J. of Biomech. Eng. 123.6 (2001), pp. 599–606. Cerca con Google

[157] Picerno, P. “25 years of lower limb joint kinematics by using inertial and magnetic sensors: A review of methodological approaches.” In: Gait & Posture 51 (2017), pp. 239–246. Cerca con Google

[158] Pittaccio, S. and Viscuso, S. “An EMG-controlled SMA device for the rehabilitation of the ankle joint in post-acute stroke.” In: Journal of materials engineering and performance 20.4-5 (2011), pp. 666–670. Cerca con Google

[159] Pizzolato, C., Lloyd, D. G., Sartori, M., Ceseracciu, E., Besier, T. F., Fregly, B. J., and Reggiani, M. “CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks.” In: Journal of biomechanics 48.14 (2015), pp. 3929–3936. Cerca con Google

[160] Pizzolato, C., Reggiani, M., Saxby, D., Ceseracciu, E., Modenese, L., and Lloyd, D. G. “Biofeedback for gait retraining based on real-time estimation of tibiofemoral joint contact forces.” In: Neural Sys Rehab Eng IEEE Trans on (in press). Cerca con Google

[161] Pulfrich, C. “Die Stereoskopie im Dienste der isochromen und heterochromen Photometrie.” In: Naturwissenschaften 10.33 (1922), pp. 714–722. Cerca con Google

[162] Qualysis. Qualysis Motion Capture Systems Homepage. (Last access: Nov. 2017. url: https://www.qualisys.com/. Vai! Cerca con Google

[163] Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A. Y. “ROS: an open-source Robot Operating System.” In: ICRA workshop on open source software. Vol. 3. 3.2. Kobe. 2009, p. 5. Cerca con Google

[164] Rabiner, L. R. and Gold, B. “Theory and application of digital signal processing.” In: Englewood Cliffs, NJ, Prentice-Hall, Inc., 1975. 777 p. (1975). Cerca con Google

[165] Ramachandran, P. “From science fiction to reality: exoskeletons.” In: Life in Action 1.3 (2011), pp. 20–21. Cerca con Google

[166] Rios, J. A. and White, E. “Fusion filter algorithm enhancements for a MEMS GPS/IMU.” In: ION NTM. 2002, pp. 28– 30. Cerca con Google

[167] ROS Community. Introduction to ROS. (Last access: Nov. 2017. url: http://wiki.ros.org/ROS/Introduction. Vai! Cerca con Google

[168] Roth, B. In: Siciliano, B. and Khatib, O. Springer handbook of robotics. Springer, 2016. Chap. Forword, pp. V–IX. Cerca con Google

[169] Sabatelli, S., Galgani, M., Fanucci, L., and Rocchi, A. “A double stage Kalman filter for sensor fusion and orientation tracking in 9D IMU.” In: Sensors Applications Symposium (SAS), 2012 IEEE. IEEE. 2012, pp. 1–5. Cerca con Google

[170] Sabatini, A. M. “Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing.” In: Sensors (Basel) 11.2 (2011), pp. 1489–525. Cerca con Google

[171] Sartori, M., Reggiani, M., Pagello, E., and Lloyd, D. “Modeling the Human Knee for Assistive Technologies.” In: IEEE Trans. on Biomedical Eng. 59.9 (2012), pp. 2642–2649. Cerca con Google

[172] Sartori, M., Reggiani, M., Bogert, A. J. van den, and Lloyd, D. G. “Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-splines.” In: Journal of biomechanics 45.3 (2012), pp. 595–601. Cerca con Google

[173] Sartori, M., Reggiani, M., Farina, D., and Lloyd, D. G. “EMGdriven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity.” In: PloS one 7.12 (2012), e52618. Cerca con Google

[174] Saxby, D. J., Bryant, A. L., Modenese, L., Gerus, P., Killen, B., Konrath, J., Fortin, K., Wrigley, T. V., Bennell, K. L., and Cicuttini, F. M. “Tibiofemoral Contact Forces in the Anterior Cruciate Ligament-Reconstructed Knee.” In: Medicine & Science in Sports & Exercise 48.11 (2016), pp. 2195–2206. Cerca con Google

[175] Schiehlen, W. “Multibody system dynamics: roots and perspectives.” In: Multibody System Dyn. 1.2 (1997), pp. 149–188. Cerca con Google

[176] Scott, S. H. and Winter, D. A. “A comparison of three muscle pennation assumptions and their effect on isometric and isotonic force.” In: Journal of biomechanics 24.2 (1991), pp. 163– 167. Cerca con Google

[177] Seborg, D. E., Mellichamp, D. A., Edgar, T. F., and Doyle III, F. J. Process dynamics and control. John Wiley & Sons, 2010. Cerca con Google

[178] Sherman, M. A., Seth, A., and Delp, S. L. “Simbody: multibody dynamics for biomedical research.” In: Procedia Iutam 2 (2011), pp. 241–261. Cerca con Google

[179] Siciliano, B. and Khatib, O. Springer handbook of robotics. Springer, 2016. Cerca con Google

[180] Simbody. Simbody Github repository. (Last access: Nov. 2017. 2013. url: https://github.com/simbody/simbody. Vai! Cerca con Google

[181] SimTK Project. Simbody Homepage. (Last access: Nov. 2017. url: https://simtk.org/projects/simbody. Vai! Cerca con Google

[182] Sontag, E. D. Mathematical control theory: deterministic finite dimensional systems. Vol. 6. Springer Science & Business Media, 2013. Cerca con Google

[183] Sumida, M., Fujimoto, M., Tokuhiro, A., Tominaga, T., Magara, A., and Uchida, R. “Early rehabilitation effect for traumatic spinal cord injury.” In: Arch. Physical Med. and Rehab. 82.3 (2001), pp. 391–395. Cerca con Google

[184] Sutherland, D. H. “The evolution of clinical gait analysis part I: kinesiological EMG.” In: Gait & posture 14.1 (2001), pp. 61–70. Cerca con Google

[185] Svinin, M. M. and Uchiyama, M. “Optimal geometric structures of force/torque sensors.” In: The International journal of robotics research 14.6 (1995), pp. 560–573. Cerca con Google

[186] Tagliapietra, Luca, Ceseracciu, E., Modenese, L., Reggiani, M., and Mazzà, C. “Inertial sensors based inverse kinematics: accuracy assessment on a robot application.” In: Abstracts Book of the XXVI Congress of the International Society of Biomechanics. 2017, p. 234. Cerca con Google

[187] Tagliapietra, Luca, Modenese, L., Ceseracciu, E., Mazzà, C., and Reggiani, M. “Assessment of a MagnetoInertial Sensors Driven Inverse Kinematics Approach for the Estimate of Multibody.” In: 17th SIAMOC National Congress. 2016. Cerca con Google

[188] Tagliapietra, Luca, Modenese, L., Ceseracciu, E., Mazzà, C., and Reggiani, M. “Validation of a model-based inverse kinematics approach based on wearable inertal sensors.” In: Computer Methods in Biomechanics and Biomedical Engineering ([submitted under review]). Cerca con Google

[189] Tagliapietra, Luca, Pizzolato, C., Ceseracciu, E., Modenese, L., Lloyd, D. G., and Reggiani, M. “An OpenSim plugin to estimate joint angles using inverse kinematics and inertial measurement units.” In: Abstracts Book of the XXVI Congress of the International Society of Biomechanics. 2017, p. 945. Cerca con Google

[190] Tagliapietra, Luca, Vivian, M., Caracciolo, R., and Reggiani, M. “Evaluation of the Biomechanical Simulator OpenSim on a Multi-Body System Benchmark.” In: ECCOMAS Thematic Conference on Multibody Dynamics 2015. Ed. by Numerical Methods in Engineering, I. C. for. Vol. 1. 2015, pp. 1572–1573. Cerca con Google

[191] Tagliapietra, Luca, Vivian, M., Ceseracciu, E., and Reggiani, M. MBS-BOS: Multibody Sistems Benchmark in OpenSim. GitHub Repository. (Last access: Nov. 2017. url: https://github.com/ RehabEngGroup/MBSbenchmarksInOpenSim. Vai! Cerca con Google

[192] Tagliapietra, Luca, Vivian, M., Sartori, M., Farina, D., and Reggiani, M. “Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements.” In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE. 2015, pp. 3611– 3614. Cerca con Google

[193] Tamez-Duque, J., Cobian-Ugalde, R., Kilicarslan, A., Venkatakrishnan, A., Soto, R., and Contreras-Vidal, J. L. “Real-time strap pressure sensor system for powered exoskeletons.” In: Sensors 15.2 (2015), pp. 4550–4563. Cerca con Google

[194] Tesio, L., Monzani, M., Gatti, R., and Franchignoni, F. “Flexible electrogoniometers: kinesiological advantages with respect to potentiometric goniometers.” In: Clinical Biomechanics 10.5 (1995), pp. 275–277. Cerca con Google

[195] Thelen, D., Anderson, F., and Delp, S. “Generating dynamic simulations of movement using computed muscle control.” In: J. of Biomech. 36.3 (2003), pp. 321–328. Cerca con Google

[196] Todorov, E. “Direct cortical control of muscle activation in voluntary arm movements: a model.” In: Nature neuroscience 3.4 (2000), p. 391. Cerca con Google

[197] Tsetserukou, D. and Tachi, S. “Torque sensors for robot joint control.” In: Sensors: Focus on Tactile Force and Stress Sensors. InTech, 2008. Cerca con Google

[198] Urbiforge. Urbi. (Last access: Nov. 2017. url: https://github. com/urbiforge/urbi. Vai! Cerca con Google

[199] Utz, H., Sablatnog, S., Enderle, S., and Kraetzschmar, G. “Miromiddleware for mobile robot applications.” In: IEEE Transactions on Robotics and Automation 18.4 (2002), pp. 493–497. Cerca con Google

[200] Van den Bogert, A. J., Geijtenbeek, T., Even-Zohar, O., Steenbrink, F., and Hardin, E. C. “A real-time system for biomechanical analysis of human movement and muscle function.” In: Med Biol Eng Comput 51.10 (2013), pp. 1069–77. Cerca con Google

[201] Van der Lee, J. H., Wagenaar, R. C., Lankhorst, G. J., Vogelaar, T. W., Devillé, W. L., and Bouter, L. M. “Forced use of the upper extremity in chronic stroke patients.” In: Stroke 30.11 (1999), pp. 2369–2375. Cerca con Google

[202] Van Ham, R., Vanderborght, B., Van Damme, M., Verrelst, B., and Lefeber, D. “MACCEPA: the actuator with adaptable compliance for dynamic walking bipeds.” In: Climbing and Walking Robots. Springer, 2006, pp. 759–766. Cerca con Google

[203] Van Peppen, R., Kwakkel, G., Wood-Dauphinee, S., Hendriks, H., Van der Wees, P., and Dekker, J. “The impact of physical therapy on functional outcomes after stroke: what’s the evidence?” In: Clinical rehabilitation 18.8 (2004), pp. 833–862. Cerca con Google

[204] Veneman, J. F., Kruidhof, R., Hekman, E. E., Ekkelenkamp, R., Van Asseldonk, E. H., and Van Der Kooij, H. “Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation.” In: IEEE Transactions on Neural Systems and Rehabilitation Engineering 15.3 (2007), pp. 379–386. Cerca con Google

[205] Vicon. Vicon Homepage. (Last access: Nov. 2017. url: https : / / www.vicon.com. Vai! Cerca con Google

[206] Vikas, V. and Crane, C. D. “Joint angle measurement using strategically placed accelerometers and gyroscope.” In: Journal of Mechanisms and Robotics 8.2 (2016), p. 021003. Cerca con Google

[207] Vivian, M., Reggiani, M., and Sartori, M. “Experimentallybased optimization of contact parameters in dynamics simulation of humanoid robots.” In: Robotics and Automation (ICRA), 2013 IEEE International Conference on. IEEE. 2013, pp. 1643– 1648. Cerca con Google

[208] Vivian, M., Tagliapietra, Luca, Reggiani, M., Farina, D., and Sartori, M. “Design of a Subject-Specific EMG Model for Rehabilitation Movement.” In: Replace, Repair, Restore, Relieve – Bridging Clinical and Engineering Solutions in Neurorehabilitation. Springer, 2014, pp. 813–822. Cerca con Google

[209] Vivian, M., Tagliapietra, Luca, Sartori, M., and Reggiani, M. “Dynamic simulation of robotic devices using the biomechanical simulator OpenSim.” In: Intelligent Autonomous Systems 13. Springer, 2016, pp. 1639–1651. Cerca con Google

[210] Wade, D. and Hewer, R. “Functional abilities after stroke: measurement, natural history and prognosis.” In: J. Neurology, Neurosurgery & Psychiatry 50.2 (1987), pp. 177–182. Cerca con Google

[211] Wakeling, J. M., Lee, S. S., Arnold, A. S., Boef Miara, M. de, and Biewener, A. A. “A muscle’s force depends on the recruitment patterns of its fibers.” In: Annals of biomedical engineering 40.8 (2012), pp. 1708–1720. Cerca con Google

[212] Watkins, J. Structure and function of the musculoskeletal system. Human Kinetics 1, 2010. Cerca con Google

[213] Weber, W. and Weber, E. F. Mechanik der menschlichen Gehwerkzeuge: eine anatomisch-physiologische Untersuchung. Vol. 1. Dietrich, 1836. Cerca con Google

[214] Winter, D. A. Biomechanics and motor control of human movement. John Wiley & Sons, 2009. Cerca con Google

[215] Woods, J. and Bigland-Ritchie, B. “Linear and non-linear surface emg/force relationships in human muscles: an anatomical/functional argument for the existence of both.” In: American Journal of Physical Medicine & Rehabilitation 62.6 (1983), pp. 287–299. Cerca con Google

[216] Wu, G., Siegler, S., Allard, P., Kirtley, C., Leardini, A., Rosenbaum, D., Whittle, M., D D’Lima, D., Cristofolini, L., and Witte, H. “ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion—part I: ankle, hip, and spine.” In: Biomech J of 35.4 (2002), pp. 543–548. Cerca con Google

[217] Yamaguchi, G. and Zajac, F. “Restoring unassisted natural gait to paraplegics via functional neuromuscular stimulation: a computer simulation study.” In: Biomed. Eng., IEEE Trans. on 37.9 (1990), pp. 886–902. Cerca con Google

[218] YARP Community. Using YARP with ROS. (Last access: Nov. 2017. url: http://www.yarp.it/yarp_with_ros.html. Vai! Cerca con Google

[219] Z. Inc. Internet communications engine. (Last access: Nov. 2017. url: https://zeroc.com/products/ice. Vai! Cerca con Google

[220] Zajac, F. E. “Muscle and tendon Properties models scaling and application to biomechanics and motor.” In: Critical reviews in biomedical engineering 17.4 (1989), pp. 359–411. Cerca con Google

[221] Zuniga, E. N. and Simons, E. “Nonlinear relationship between averaged electromyogram potential and muscle tension in normal subjects.” In: Archives of physical medicine and rehabilitation 50.11 (1969), pp. 613–620. Cerca con Google

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record