02388nas a2200793 4500008004100000022001400041245011100055210006900166260001000235300001000245490000700255653002300262653002800285653002200313653001800335653002100353653002900374653003000403653001300433653001900446653002800465653002800493653002200521653002000543653002800563653002900591653002700620653001900647653002400666653002500690653002200715653002300737653003500760653001500795653003300810653002300843653001700866653001100883653002100894653002100915653002600936653002000962653002800982653002401010653002601034653003601060653001601096653002201112653001201134653001801146653002901164653001701193653002401210653001701234653001501251653001101266653001601277653002201293653002201315100001301337700001501350700002001365700001501385700001701400700001601417700001701433700001601450856012801466 2018 eng d a1070-993200aSimplifying Telerobotics: Wearability and Teleimpedance Improves Human-Robot Interactions in Teleoperation0 aSimplifying Telerobotics Wearability and Teleimpedance Improves cMarch a77-880 v2510aapplication fields10aaugmented teleoperation10aAutonomous robots10acommunication10aeffective design10aeffective simplification10aenvironmental constraints10afeedback10aForce feedback10afundamental requirement10ahaptic feedback devices10ahaptic interfaces10ahuman workspace10ahuman-robot interaction10ahuman-robot interactions10ahuman-robot interfaces10aideal scenario10aintegrated approach10aintegrated interface10aintegrated system10ainteraction forces10aintuitive information exchange10aKinematics10aKUKA lightweight robotic arm10alightweight design10amanipulators10amaster10aposition control10areduced versions10aRobot sensing systems10arobotic devices10arobotic hand-arm system10arobotic manipulator10arobotic teleoperation10asimplified information exchange10aslave robot10astiffness control10asynergy10aTask analysis10ateleimpedance techniques10aTelemedicine10ateleoperator system10atelerobotics10aTI control10avision10awearability10awearable feedback10awearable hand/arm1 aFani, S.1 aCiotti, S.1 aCatalano, M. G.1 aGrioli, G.1 aTognetti, A.1 aValenza, G.1 aAjoudani, A.1 aBianchi, M. uhttps://www.centropiaggio.unipi.it/publications/simplifying-telerobotics-wearability-and-teleimpedance-improves-human-robot02219nas a2200169 4500008003900000245009900039210006900138520157700207100001501784700001801799700001701817700001701834700001901851700001901870700002101889856013901910 2017 d00aCharacterization of Hand Movements using a Sensing Glove in Hand Assisted Laparoscopic Surgery0 aCharacterization of Hand Movements using a Sensing Glove in Hand3 a
The past thirty years have seen increasingly rapid advances in the field of laparoscopic surgery, in part because of the use of robots. A well-known example is the da Vinci surgical system. However, far too little attention has been paid to Hand Assisted Laparoscopic Surgery (HALS), a surgery in which the surgeon introduces the non-dominant hand into the abdomen of the patient. The risk of collision between the hand of the surgeon and the tool moved by the robot is the reason why these robots for laparoscopic surgery are not appropriate for HALS. On the other hand, in recent years, there has been an increasing interest in wearables, which have been introduced in our daily life. This interest and the lack of surgery robots for HALS are the reasons to develop a sensing glove which co-works whit a collaborative robot in this kind of surgery. The aim of this paper is to study the use of a sensing glove which will provide information of the movements of the surgeon’s hand to the collaborative robot. This information determinates the actions that the robot will carry on. The first step was to define different movements of the hand which could be identified. An algorithm identifies these movements using the data given by the sensing glove. For the purpose of algorithm accuracy measurement, 4 persons wearing the sensing glove made a sequence with different movements. The evidence from this study suggests that a sensing glove can be used to send information of the movements of the surgeon’s hand to a collaborative robot during a HALS.
1 aSantos, L.1 aCarbonaro, N.1 aTognetti, A.1 aGonzales, R.1 aFraile, J., C.1 aTuriel, J., P.1 aDe La Fuente, E. uhttps://www.centropiaggio.unipi.it/publications/characterization-hand-movements-using-sensing-glove-hand-assisted-laparoscopic-surgery00644nas a2200193 4500008004100000245014300041210006900184490000600253653002200259653000800281653002000289653001600309653001300325100001400338700002200352700001800374700001700392856004100409 2017 eng d00aDevelopment of a High-Speed Current Injection and Voltage Measurement System for Electrical Impedance Tomography-Based Stretchable Sensors0 aDevelopment of a HighSpeed Current Injection and Voltage Measure0 v510aconductive fabric10aEIT10apressure sensor10astretchable10awearable1 aRusso, S.1 aNefti-meziani, S.1 aCarbonaro, N.1 aTognetti, A. uhttp://www.mdpi.com/2227-7080/5/3/4800796nas a2200217 4500008004100000245009400041210006900135490000600204653002800210653001800238653002500256653003700281653002000318100001800338700001800356700001500374700001700389700001700406700001700423856013800440 2017 eng d00aA Multimodal Perception Framework for Users Emotional State Assessment in Social Robotics0 aMultimodal Perception Framework for Users Emotional State Assess0 v910ahuman-robot interaction10amultimodality10aperception framework10aphysiological signal acquisition10asocial robotics1 aCominelli, L.1 aCarbonaro, N.1 aMazzei, D.1 aGarofalo, R.1 aTognetti, A.1 aDe Rossi, D. uhttps://www.centropiaggio.unipi.it/publications/multimodal-perception-framework-users-emotional-state-assessment-social-robotics.html01880nas a2200193 4500008004100000245010200041210006900143490000700212520125600219653002201475653002001497653002701517653002301544100001401567700002201581700001801603700001701621856004801638 2017 eng d00aA Quantitative Evaluation of Drive Pattern Selection for Optimizing EIT-Based Stretchable Sensors0 aQuantitative Evaluation of Drive Pattern Selection for Optimizin0 v173 aElectrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback with EIT-based sensors, however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal-to-Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of one drive pattern over another can improve the target size detection and position accuracy up to 0.04. and 0.18, respectively.
10aconductive fabric10ainverse problem10aperformance parameters10astretchable sensor1 aRusso, S.1 aNefti-meziani, S.1 aCarbonaro, N.1 aTognetti, A. uhttp://www.mdpi.com/1424-8220/17/9/1999/htm00589nas a2200145 4500008004100000245007900041210006900120260004900189300001200238100001600250700001800266700001700284700001700301856012500318 2017 eng d00aStrain and angular sensing fabrics for human motion analysis in daily life0 aStrain and angular sensing fabrics for human motion analysis in aDordrechtbSpringer International Publishing a49–701 aLorussi, F.1 aCarbonaro, N.1 aDe Rossi, D.1 aTognetti, A. uhttps://www.centropiaggio.unipi.it/publications/strain-and-angular-sensing-fabrics-human-motion-analysis-daily-life.html00584nas a2200181 4500008004100000245007800041210006900119653002700188653001800215653000900233653002400242653001100266100001500277700001800292700001700310700001500327856006000342 2017 eng d00aSystematic Review of fMRI Compatible Devices: Design and Testing Criteria0 aSystematic Review of fMRI Compatible Devices Design and Testing 10aBiomedical Engineering10aCompatibility10afMRI10aMechatronic devices10aSafety1 aHartwig, V1 aCarbonaro, N.1 aTognetti, A.1 aVanello, N uhttp://link.springer.com/journal/volumesAndIssues/1043900636nas a2200217 4500008004100000245008100041210006900122300001100191490000600202653001900208653001800227653001500245653001500260653001500275653001200290653002400302100001800326700001600344700001700360856004100377 2016 eng d00aAssessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking0 aAssessment of a Smart Sensing Shoe for Gait Phase Detection in L a1–150 v510aaccelerometers10aforce sensors10agait cycle10agait phase10asmart shoe10awalking10awearable technology1 aCarbonaro, N.1 aLorussi, F.1 aTognetti, A. uhttp://www.mdpi.com/2079-9292/5/4/7800713nas a2200205 4500008004100000245010300041210006900144300001100213490000700224653002800231653002200259653002900281653002800310653002100338100001600359700001800375700001700393700001700410856008000427 2016 eng d00aA bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors0 abiarticular model for scapularhumeral rhythm reconstruction thro a1–130 v1310aHand posture estimation10aReaching activity10aScapular girdle movement10aScapular-humeral rhythm10aWearable sensing1 aLorussi, F.1 aCarbonaro, N.1 aDe Rossi, D.1 aTognetti, A. uhttps://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0149-201067nas a2200337 4500008004100000020002200041245008400063210006900147260003800216300001400254653001900268100001700287700002400304700002400328700001300352700002100365700001400386700001500400700001600415700001700431700001600448700001600464700001300480700002000493700001700513700002200530700001900552700001600571700001600587856012600603 2016 eng d a978-3-319-26128-700aA Full Body Sensing System for Monitoring Stroke Patients in a Home Environment0 aFull Body Sensing System for Monitoring Stroke Patients in a Hom bSpringer International Publishing a378 - 39310aBioengineering1 aKlaassen, B.1 avan Beijnum, B.J.F.1 aWeusthof, M., H. H.1 aHofs, D.1 avan Meulen, F.B.1 aDroog, E.1 aLuinge, H.1 aLaurens, S.1 aTognetti, A.1 aLorussi, F.1 aParadiso, R1 aHeld, J.1 aLuft, A., R. L.1 aReenalda, J.1 aNikamp, C., D. M.1 aBuurke, J., H.1 aHermens, HJ1 aVeltink, P. uhttps://www.centropiaggio.unipi.it/publications/full-body-sensing-system-monitoring-stroke-patients-home-environment.html00704nas a2200193 4500008004100000245008000041210006900121300001100190490000700201653002700208653003200235653002500267100001400292700001700306700001700323700002200340700002100362856012700383 2016 eng d00aA Hands-Free Interface for Controlling Virtual Electric-Powered Wheelchairs0 aHandsFree Interface for Controlling Virtual ElectricPowered Whee a1–100 v1310aAssistive Technologies10aElectric-powered Wheelchair10aHands-free Interface1 aGulrez, T1 aTognetti, A.1 aYooN, W., J.1 aKavakli-Thorne, M1 aCabibihan, J.-J. uhttps://www.centropiaggio.unipi.it/publications/hands-free-interface-controlling-virtual-electric-powered-wheelchairs.html00482nas a2200145 4500008003900000245006100039210005900100260003700159300001400196100001400210700001700224700001800241700002200259856005500281 2016 d00aA highly stretchable artificial sensitive skin using EIT0 ahighly stretchable artificial sensitive skin using EIT aStoccolmabKarolinska Institutet a132–1321 aRusso, S.1 aTognetti, A.1 aCarbonaro, N.1 aNefti-meziani, S. uhttp://www.icebi2016.org/images/ICEBI_2016_web.pdf01722nas a2200181 4500008004100000245006900041210006500110490000700175520120200182653001201384100001601396700001601412700001701428700001501445700001801460700001701478856004501495 2016 eng d00aA Multi-Modal Sensing Glove for Human Manual-Interaction Studies0 aMultiModal Sensing Glove for Human ManualInteraction Studies0 v 53 aWe present an integrated sensing glove that combines two of the most visionary wearable sensing technologies to provide both hand posture sensing and tactile pressure sensing in a unique, lightweight, and stretchable device. Namely, hand posture reconstruction employs Knitted Piezoresistive Fabrics that allows us to measure bending. From only five of these sensors (one for each finger) the full hand pose of a 19 degrees of freedom (DOF) hand model is reconstructed leveraging optimal sensor placement and estimation techniques. To this end, we exploit a-priori information of synergistic coordination patterns in grasping tasks. Tactile sensing employs a piezoresistive fabric allowing us to measure normal forces in more than 50 taxels spread over the palmar surface of the glove. We describe both sensing technologies, report on the software integration of both modalities, and describe a preliminary evaluation experiment analyzing hand postures and force patterns during grasping. Results of the reconstruction are promising and encourage us to push further our approach with potential applications in neuroscience, virtual reality, robotics and tele-operation.
10aHaptics1 aBianchi, M.1 aHaschke, R.1 aBüscher, G.1 aCiotti, S.1 aCarbonaro, N.1 aTognetti, A. uhttp://www.mdpi.com/2079-9292/5/3/42/pdf00864nas a2200277 4500008003900000245007300039210006600112260003100178300001200209490000900221653002400230653002500254653002700279653002800306653002000334653002000354653003300374100001800407700001500425700001800440700001700458700001500475700001700490700001700507856006200524 2016 d00aA preliminary framework for a social robot ‚Äúsixth sense‚Äù0 apreliminary framework for a social robot Äúsixth sense Äù aDordrechtbSpringer Verlag a58–700 v979310aAffective computing10aBehaviour monitoring10aComputer Science (all)10ahuman-robot interaction10asocial robotics10aSynthetic tutor10aTheoretical Computer Science1 aCominelli, L.1 aMazzei, D.1 aCarbonaro, N.1 aGarofalo, R.1 aZaraki, A.1 aTognetti, A.1 aDe Rossi, D. uhttp://springerlink.com/content/0302-9743/copyright/2005/00673nas a2200181 4500008003900000245008300039210006900122300001400191653001900205653000800224653003600232653002400268100001400292700001800306700001700324700002200341856012800363 2016 d00aA Quantitative Evaluation of Drive Patterns in Electrical Impedance Tomography0 aQuantitative Evaluation of Drive Patterns in Electrical Impedanc a337–34410adrive patterns10aEIT10aElectrical impedance tomography10astretchable sensors1 aRusso, S.1 aCarbonaro, N.1 aTognetti, A.1 aNefti-meziani, S. uhttps://www.centropiaggio.unipi.it/publications/quantitative-evaluation-drive-patterns-electrical-impedance-tomography.html01917nas a2200181 4500008004100000245008600041210006900127490000700196520137800203653001201581100001501593700001801608700001801626700001501644700001701659700001601676856004301692 2016 eng d00aA Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition0 aSynergyBased Optimally Designed Sensing Glove for Functional Gra0 v163 aAchieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness.
10aHaptics1 aCiotti, S.1 aBattaglia, E.1 aCarbonaro, N.1 aBicchi, A.1 aTognetti, A.1 aBianchi, M. uhttp://www.mdpi.com/1424-8220/16/6/81100688nas a2200193 4500008003900000245012500039210006900164260003100233300001400264490000900278653002700287653003300314100001400347700002200361700001400383700001800397700001700415856006200432 2016 d00aTowards the development of an EIT-based stretchable sensor for multi-touch industrial human-computer interaction systems0 aTowards the development of an EITbased stretchable sensor for mu aDordrechtbSpringer Verlag a563–5730 v974110aComputer Science (all)10aTheoretical Computer Science1 aRusso, S.1 aNefti-meziani, S.1 aGulrez, T1 aCarbonaro, N.1 aTognetti, A. uhttp://springerlink.com/content/0302-9743/copyright/2005/00839nas a2200253 4500008004100000245009400041210006900135300001100204490000600215653002600221653001600247653000900263653001300272653001300285653002600298653002100324100001600345700001800361700001700379700001600396700001600412700001700428856014000445 2016 eng d00aWearable Textile Platform for Assessing Stroke Patient Treatment in Daily Life Conditions0 aWearable Textile Platform for Assessing Stroke Patient Treatment a1–280 v410aambulatory monitoring10adata fusion10agait10aGrasping10areaching10astroke rehabilitation10awearable sensors1 aLorussi, F.1 aCarbonaro, N.1 aDe Rossi, D.1 aParadiso, R1 aVeltink, P.1 aTognetti, A. uhttps://www.centropiaggio.unipi.it/publications/wearable-textile-platform-assessing-stroke-patient-treatment-daily-life-conditions.html00613nas a2200157 4500008003900000245010900039210006900148260009700217300000800314653001900322100001600341700001800357700001700375700001700392856004600409 2015 d00aEvaluation of wearable KPF goniometers in knee flexion-extension measurement for daily-life applications0 aEvaluation of wearable KPF goniometers in knee flexionextension bICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering a1-410aBioengineering1 aLorussi, F.1 aCarbonaro, N.1 aDe Rossi, D.1 aTognetti, A. uhttp://dl.acm.org/citation.cfm?id=289747700625nas a2200193 4500008004100000022001400041245011600055210006900171300001600240490000800256653001900264100001300283700001500296700001700311700001800328700001700346700001800363856005000381 2015 eng d a0014-481900aMusic, clicks, and their imaginations favor differently the event-based timing component for rhythmic movements0 aMusic clicks and their imaginations favor differently the eventb a1945–19610 v23310aBioengineering1 aBravi, R1 aQuarta, E.1 aDel Tongo, C1 aCarbonaro, N.1 aTognetti, A.1 aMinciacchi, D uhttp://dx.medra.org/10.1007/s00221-015-4267-z00760nas a2200265 4500008004100000022001400041245009100055210006900146300001400215490000700229653001900236100001800255700001800273700001600291700002000307700001400327700001600341700001600357700001200373700001700385700001700402700001700419700001500436856004300451 2015 eng d a0953-543800aNeuro-fuzzy physiological computing to assess stress levels in virtual reality therapy0 aNeurofuzzy physiological computing to assess stress levels in vi a521–5330 v2710aBioengineering1 aTartarisco, G1 aCarbonaro, N.1 aTonacci, A.1 aBernava, G., M.1 aArnao, A.1 aCrifaci, G.1 aCipresso, P1 aRiva, G1 aGaggioli, A.1 aDe Rossi, D.1 aTognetti, A.1 aPioggia, G uhttp://dx.medra.org/10.1093/iwc/iwv01000553nas a2200169 4500008004100000022001400041245010400055210006900159300001800228490000700246653001900253100001700272700001600289700001800305700001700323856004300340 2015 eng d a1424-822000aWearable goniometer and accelerometer sensory fusion for knee joint angle measurement in daily-life0 aWearable goniometer and accelerometer sensory fusion for knee jo a28435–284550 v1510aBioengineering1 aTognetti, A.1 aLorussi, F.1 aCarbonaro, N.1 aDe Rossi, D. uhttp://dx.medra.org/10.3390/s15112843501438nas a2200217 4500008003900000245009600039210006900135520070200204100001700906700001600923700001800939700001700957700001600974700001500990700001701005700001501022700001701037700001401054700001601068856013601084 2014 d00aDaily-Life Monitoring of Stroke Survivors Motor Performance: The INTERACTION Sensing System0 aDailyLife Monitoring of Stroke Survivors Motor Performance The I3 aThe objective of the INTERACTION Eu project is to develop and validate an unobtrusive and modular system for monitoring daily life activities, physical interactions with the environment and for training upper and lower extremity motor function in stroke subjects. This paper describes the development and preliminary testing of the project sensing platform made of sensing shirt, trousers, gloves and shoes. Modular prototypes were designed and built considering the minimal set of inertial, force and textile sensors that may enable an efficient monitoring of stroke patients. The single sensing elements are described and the results of their preliminary lab-level testing are reported.
1 aTognetti, A.1 aLorussi, F.1 aCarbonaro, N.1 aDe Rossi, D.1 aDe Toma, G.1 aMancuso, C1 aParadiso, R.1 aLuinge, H.1 aReenalda, J.1 aDroog, E.1 aVeltink, P. uhttps://www.centropiaggio.unipi.it/publications/daily-life-monitoring-stroke-survivors-motor-performance-interaction-sensing-system01671nas a2200337 4500008003900000245007200039210006900111260001700180300001400197520065700211100001800868700002100886700002400907700001700931700002000948700001400968700002400982700001601006700001701022700001701039700002201056700002101078700001901099700001301118700002001131700001501151700001601166700001501182700001701197856011901214 2014 d00aDaily-life tele-monitoring of motor performance in stroke survivors0 aDailylife telemonitoring of motor performance in stroke survivor c17 July 2014 a159–1623 aThe objective of the EU project INTERACTION is to develop an unobtrusive and modular sensing system for objective monitoring of daily-life motor performance of stroke survivors. This will enable clinical professionals to advise their patients about their continued daily-life activity profile and home training, and evaluate and optimize rehabilitation programs.A modular textile-integrated sensing system was developed and performance and capacity measures were proposed and clinically tested in stroke subject.Telemonitoring facilities were developed and tested. In the last stage of the project, the system will be tested during daily-life.
1 aVeltink, P.H.1 avan Meulen, F.B.1 avan Beijnum, B.J.F.1 aKlaassen, B.1 aHermens, H., J.1 aDroog, E.1 aWeusthof, M., H. H.1 aLorussi, F.1 aTognetti, A.1 aReenalda, J.1 aNikamp, C., D. M.1 aBaten, C., T. M.1 aBuurke, J., H.1 aHeld, J.1 aLuft, A., R. L.1 aLuinge, H.1 aDe Toma, G.1 aMancuso, C1 aParadiso, R. uhttps://www.centropiaggio.unipi.it/publications/daily-life-tele-monitoring-motor-performance-stroke-survivors.html01840nas a2200277 4500008004100000245009300041210006900134300001400203520097000217100001701187700001601204700001501220700001501235700001801250700001501268700001401283700001301297700001801310700001701328700001701345700001801362700001601380700001401396700001201410856014001422 2014 eng d00aA Decision Support System for Real-Time Stress Detection During Virtual Reality Exposure0 aDecision Support System for RealTime Stress Detection During Vir a114–1203 aVirtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface.
1 aGaggioli, A.1 aCipresso, P1 aSerino, S.1 aPioggia, G1 aTartarisco, G1 aBaldus, G.1 aCorda, D.1 aFerro, M1 aCarbonaro, N.1 aTognetti, A.1 aDe Rossi, D.1 aGiakoumis, D.1 aTzovaras, D1 aRiera, A.1 aRiva, G uhttps://www.centropiaggio.unipi.it/publications/decision-support-system-real-time-stress-detection-during-virtual-reality-exposure.html00415nas a2200121 4500008003900000245006800039210006800107300001400175100001400189700002200203700001700225856005100242 2014 d00aDecoding 2D kinematics of human arm for body machine interfaces0 aDecoding 2D kinematics of human arm for body machine interfaces a719–7221 aGulrez, T1 aKavakli-Thorne, M1 aTognetti, A. uhttp://dx.medra.org/10.1109/ICIEA.2013.656646100726nas a2200193 4500008003900000245010500039210006900144260004000213653001200253653001300265100001600278700001800294700001800312700001600330700001500346700001700361700001700378856013700395 2014 d00aExploiting hand kinematic synergies and wearable under-sensing for hand functional grasp recognition0 aExploiting hand kinematic synergies and wearable undersensing fo aNovember 3–5, 2014 Athens, Greece10aHaptics10aRobotics1 aBianchi, M.1 aCarbonaro, N.1 aBattaglia, E.1 aLorussi, F.1 aBicchi, A.1 aDe Rossi, D.1 aTognetti, A. uhttps://www.centropiaggio.unipi.it/publications/exploiting-hand-kinematic-synergies-and-wearable-under-sensing-hand-functional-grasp01930nas a2200157 4500008004100000245007200041210006900113520136600182100001801548700001801566700001601584700001701600700001701617700001701634856012101651 2014 eng d00aExploiting wearable goniometer technology for motion sensing gloves0 aExploiting wearable goniometer technology for motion sensing glo3 aThis paper presents an innovative wearable kinesthetic glove realized with knitted piezoresistive fabric (KPF) sensor technology. The glove is conceived to capture hand movement and gesture by using KPF in a double layer configuration working as angular sensors (electro-goniometers). The sensing glove prototype is endowed by three KPF goniometers, used to track flexion and extension movement of metacarpo-phalangeal joint of thumb, index and middle fingers. The glove is devoted to the continuous monitoring of patients during their daily life activities, in particular for stroke survivors during their rehabilitation. The prototype performances have been evaluated in comparison with an optical tracking system considered as a gold standard both for relieving static and dynamic posture and gesture of the hand. The introduced prototype has shown very interesting figures of merit. The angular error, evaluated through the standard Bland Altman analysis, has been estimated in ? 3? which is slightly less accurate than commercial electro-goniometers. Moreover, a new conceptual prototype design, preliminary evaluated within this work, is presented and discussed in order to solve actual limitations in terms of number and type of sensor connections, avoiding mechanical constraints given by metallic inextensible wires and improving user comfort.
1 aCarbonaro, N.1 aDalle Mura, G1 aLorussi, F.1 aParadiso, R.1 aDe Rossi, D.1 aTognetti, A. uhttps://www.centropiaggio.unipi.it/publications/exploiting-wearable-goniometer-technology-motion-sensing-gloves.html02338nas a2200229 4500008004100000245012400041210006900165490000600234520155800240100001601798700001401814700001701828700001401845700001401859700001501873700001701888700002101905700001401926700001701940700001701957856013401974 2014 eng d00aInference of Human Affective States from Psychophysiological Measurements Extracted under Ecologically Valid Conditions0 aInference of Human Affective States from Psychophysiological Mea0 v83 aCompared to standard laboratory protocols, the measurement of psychophysiological signals in real world experiments poses technical and methodological challenges due to external factors that cannot be directly controlled. To address this problem, we propose a hybrid approach based on an immersive and human accessible space called the eXperience Induction Machine (XIM), that incorporates the advantages of a laboratory within a life-like setting. The XIM integrates unobtrusive wearable sensors for the acquisition of psychophysiological signals suitable for ambulatory emotion research. In this paper, we present results from two different studies conducted to validate the XIM as a general-purpose sensing infrastructure for the study of human affective states under ecologically valid conditions. In the first investigation, we recorded and classified signals from subjects exposed to pictorial stimuli corresponding to a range of arousal levels, while they were free to walk and gesticulate. In the second study, we designed an experiment that follows the classical conditioning paradigm, a well-known procedure in the behavioral sciences, with the additional feature that participants were free to move in the physical space, as opposed to similar studies measuring physiological signals in constrained laboratory settings. Our results indicate that, by using our sensing infrastructure, it is indeed possible to infer human event-elicited affective states through measurements of psychophysiological signals under ecological conditions.
1 aBetella, A.1 aZucca, R.1 aCetnarski, A1 aGreco, A.1 aLanata, A1 aMazzei, D.1 aTognetti, A.1 aArsiwalla, X. D.1 aOmedas, P1 aDe Rossi, D.1 aVerschure, P uhttps://www.centropiaggio.unipi.it/publications/inference-human-affective-states-psychophysiological-measurements-extracted-under02009nas a2200265 4500008003900000245009800039210006900137520115700206100001601363700001601379700001401395700002101409700001401430700001401444700001501458700001701473700001401490700001801504700001501522700002101537700001501558700001701573700001801590856013501608 2014 d00aInterpreting Psychophysiological States Using Unobtrusive Wearable Sensors in Virtual Reality0 aInterpreting Psychophysiological States Using Unobtrusive Wearab3 aOne of the main challenges in the study of human be- havior is to quantitatively assess the participants? affective states by measuring their psychophysiological signals in ecologically valid conditions. The quality of the acquired data, in fact, is often poor due to artifacts generated by natural interactions such as full body movements and gestures. We created a technology to address this problem. We enhanced the eXperience Induction Machine (XIM), an immersive space we built to conduct experiments on human behavior, with unobtrusive wearable sensors that measure electrocardiogram, breathing rate and electrodermal response. We conducted an empirical validation where participants wearing these sensors were free to move in the XIM space while exposed to a series of visual stimuli taken from the International Affective Picture System (IAPS). Our main result consists in the quan- titative estimation of the arousal range of the affective stimuli through the analysis of participants? psychophysiological states. Taken together, our findings show that the XIM constitutes a novel tool to study human behavior in life-like conditions.
1 aBetella, A.1 aPacheco, D.1 aZucca, R.1 aArsiwalla, X. D.1 aOmedas, P1 aLanata, A1 aMazzei, D.1 aTognetti, A.1 aGreco, A.1 aCarbonaro, N.1 aWagner, J.1 aLingenfelser, F.1 aAndrè, E.1 aDe Rossi, D.1 aVerschure, P. uhttps://www.centropiaggio.unipi.it/publications/interpreting-psychophysiological-states-using-unobtrusive-wearable-sensors-virtual00709nas a2200193 4500008004100000245011200041210006900153260001200222300001600234490000800250653001900258100001300277700001700290700001500307700001800322700001700340700001800357856014000375 2014 eng d00aModulation of isochronous movements in a flexible environment: links between motion and auditory experience0 aModulation of isochronous movements in a flexible environment li c06/2014 a1663 - 16750 v23210aBioengineering1 aBravi, R1 aDel Tongo, C1 aCohen, E J1 aDalle Mura, G1 aTognetti, A.1 aMinciacchi, D uhttps://www.centropiaggio.unipi.it/publications/modulation-isochronous-movements-flexible-environment-links-between-motion-and-auditory02203nas a2200181 4500008004100000245007000041210006900111490000700180520159900187100001701786700001601803700001801819700001801837700001601855700001701871700001701888856011601905 2014 eng d00aNew generation of wearable goniometers for motion capture systems0 aNew generation of wearable goniometers for motion capture system0 v113 aBackground Monitoring joint angles through wearable systems enables human posture and gesture to be reconstructed as a support for physical rehabilitation both in clinics and at the patient's home. A new generation of wearable goniometers based on knitted piezoresistive fabric (KPF) technology is presented. Methods KPF single-and double-layer devices were designed and characterized under stretching and bending to work as strain sensors and goniometers. The theoretical working principle and the derived electromechanical model, previously proved for carbon elastomer sensors, were generalized to KPF. The devices were used to correlate angles and piezoresistive fabric behaviour, to highlight the differences in terms of performance between the single layer and the double layer sensors. A fast calibration procedure is also proposed. Results The proposed device was tested both in static and dynamic conditions in comparison with standard electrogoniometers and inertial measurement units respectively. KPF goniometer capabilities in angle detection were experimentally proved and a discussion of the device measurement errors of is provided. The paper concludes with an analysis of sensor accuracy and hysteresis reduction in particular configurations. Conclusions Double layer KPF goniometers showed a promising performance in terms of angle measurements both in quasi-static and dynamic working mode for velocities typical of human movement. A further approach consisting of a combination of multiple sensors to increase accuracy via sensor fusion technique has been presented.
1 aTognetti, A.1 aLorussi, F.1 aDalle Mura, G1 aCarbonaro, N.1 aPacelli, M.1 aParadiso, R.1 aDe Rossi, D. uhttps://www.centropiaggio.unipi.it/publications/new-generation-wearable-goniometers-motion-capture-systems.html00587nas a2200193 4500008003900000245005700039210005700096260003800153300001600191100001800207700001600225700001700241700001500258700001800273700001500291700001600306700001700322856005400339 2014 d00aPiezoresistive Goniometer Network for Sensing Gloves0 aPiezoresistive Goniometer Network for Sensing Gloves bSpringer International Publishing a1547–15501 aDalle Mura, G1 aLorussi, F.1 aTognetti, A.1 aAnania, G.1 aCarbonaro, N.1 aPacelli, M1 aParadiso, R1 aDe Rossi, D. uhttp://dx.medra.org/10.1007/978-3-319-00846-2_38201641nas a2200205 4500008004100000245008300041210006900124300001200193490000600205520096200211100001801173700001601191700001701207700001501224700001701239700001901256700001701275700001201292856013101304 2014 eng d00aPsychometric Assessment of Cardio-Respiratory Activity Using a Mobile Platform0 aPsychometric Assessment of CardioRespiratory Activity Using a Mo a13–290 v53 aStress is an increasingly recognized phenomenon that has negative effects on growing numbers of people. Stress assessment is a complex issue, but different studies have shown that monitoring user psychophysi- ological parameter during daily life can be greatly helpful in stress evaluation. In this context, the European Collaborative Project INTERSTRESS is aimed at designing and developing advanced simulation and sensing technologies for the assessment and treatment of psychological stress, based on mobile biosensors.In this study a wearable biosensor platform able to collect physiological and behavioral parameters is reported. The developed mobile platform, in terms of hardware and processing algorithms, is described. Moreover the use of this wearable biosensor platform in combination with advanced simulation technologies, such as virtual reality, offer interesting opportunities for innovative personal health-care solutions to stress.
1 aCarbonaro, N.1 aCipresso, P1 aTognetti, A.1 aAnania, G.1 aDe Rossi, D.1 aPallavicini, F1 aGaggioli, A.1 aRiva, G uhttps://www.centropiaggio.unipi.it/publications/psychometric-assessment-cardio-respiratory-activity-using-mobile-platform.html02102nas a2200301 4500008004100000245016500041210006900206520109600275100001501371700001601386700001401402700002101416700001601437700001401453700002001467700001501487700001401502700001701516700001801533700001501551700001801566700001301584700001701597700001701614700001401631700002301645856013201668 2014 eng d00aXIM-Engine: a software framework to support the development of interactive applications that uses conscious and unconscious reactions in immersive mixed reality0 aXIMEngine a software framework to support the development of int3 aThe development of systems that allow multimodal interpretation of human-machine interaction is crucial to advance our understanding and validation of theoretical models of user behavior. In particular, a system capable of collecting, perceiving and interpreting unconscious behavior can provide rich contextual information for an interactive system. One possible application for such a system is in the exploration of complex data through immersion, where massive amounts of data are generated every day both by humans and computer processes that digitize information at different scales and resolutions thus exceeding our processing capacity. We need tools that accelerate our understanding and generation of hypotheses over the datasets, guide our searches and prevent data overload. We describe XIM- engine, a bio-inspired software framework designed to capture and analyze multi-modal human behavior in an immersive environment. The framework allows performing studies that can advance our understanding on the use of conscious and unconscious reactions in interactive systems.
1 aOmedas, P.1 aBetella, A.1 aZucca, R.1 aArsiwalla, X. D.1 aPacheco, D.1 aWagner, J1 aLingenfelser, F1 aMazzei, D.1 aLanata, A1 aTognetti, A.1 aGoldhoorn, A.1 aGuerra, E.1 aAlquìzar, R.1 aGrau, A.1 aSanfeliu, A.1 aDe Rossi, D.1 aAndré, E1 aVerschure, P F M J uhttps://www.centropiaggio.unipi.it/publications/xim-engine-software-framework-support-development-interactive-applications-uses00562nas a2200157 4500008004100000022001400041245007700055210006900132300001100201490000700212100001400219700001500233700001700248700001500265856012400280 2013 eng d a1456-785700aAn Activity Classifier based on Heart Rate and Accelerometer Data Fusion0 aActivity Classifier based on Heart Rate and Accelerometer Data F a7–120 v151 aCurone, D1 aSecco, E L1 aTognetti, A.1 aMagenes, G uhttps://www.centropiaggio.unipi.it/publications/activity-classifier-based-heart-rate-and-accelerometer-data-fusion.html00809nas a2200277 4500008003900000245011000039210006900149260002200218653001900240100001600259700001700275700001400292700002100306700001400327700001600341700001200357700001400369700002000383700001400403700001500417700001700432700001400449700001700463700002300480856002800503 2013 d00aAdvanced Interfaces to Stem the Data Deluge in Mixed Reality: Placing Human (un)consciuosness in the Loop0 aAdvanced Interfaces to Stem the Data Deluge in Mixed Reality Pla aNew York, NY, USA10aBioengineering1 aBetella, A.1 aMartìnez, E1 aZucca, R.1 aArsiwalla, X. D.1 aOmedas, P1 aWierenga, S1 aMura, A1 aWagner, J1 aLingenfelser, F1 aAndré, E1 aMazzei, D.1 aTognetti, A.1 aLanata, A1 aDe Rossi, D.1 aVerschure, P F M J u10.1145/2503385.250346000632nas a2200217 4500008003900000245005700039210005400096260003800150300001200188100001800200700001500218700001800233700001400251700001600265700001600281700001500297700001500312700001700327700001700344856005300361 2013 d00aAn Innovative Multisensor Controlled Prosthetic Hand0 aInnovative Multisensor Controlled Prosthetic Hand bSpringer International Publishing a93–961 aCarbonaro, N.1 aAnania, G.1 aBacchereti, M1 aDonati, G1 aFerretti, G1 aPellicci, L1 aParrini, G1 aVitetta, N1 aDe Rossi, D.1 aTognetti, A. uhttp://dx.medra.org/10.1007/978-3-319-00846-2_2301652nas a2200217 4500008004100000022002300041245008000064210006900144260001800213300001500231520093300246653001901179100001801198700001701216700001501233700001701248700001601265700001701281700001201298856012401310 2013 eng d a978-1-4799-0296-5 00aA Mobile Biosensor to detect Cardiorespiratory Activity for Stress Tracking0 aMobile Biosensor to detect Cardiorespiratory Activity for Stress aVenice, Italy a440 - 445 3 aStress is an increasingly recognized phenomenon that has negative effects on growing numbers of people. Stress assessment is a complex issue, but different studies have shown that monitoring user psychophysiological parameter during daily life can be greatly helpful in stress evaluation. In this study a wearable biosensor platform able to collect physiological and behavioral parameters is reported. The developed wearable platform, in terms of hardware and processing algorithms, is described. Moreover the use of this wearable biosensor platform in combination with advanced simulation technologies, such as virtual reality offer interesting opportunities for innovative personal health-care solutions to stress. A recently founded European project, "INTERSTRESS - Interreality in the management and treatment of stress-related disorders," will take into account these relevant aspects.
10aBioengineering1 aCarbonaro, N.1 aTognetti, A.1 aAnania, G.1 aDe Rossi, D.1 aCipresso, P1 aGaggioli, A.1 aRiva, G uhttps://www.centropiaggio.unipi.it/publications/mobile-biosensor-detect-cardiorespiratory-activity-stress-tracking.html01265nas a2200217 4500008003900000245005700039210005700096260003800153300001600191520058000207653001900787100001900806700001600825700001700841700001500858700001800873700001500891700001600906700001700922856010800939 2013 d00aPiezoresistive Goniometer Network for Sensing Gloves0 aPiezoresistive Goniometer Network for Sensing Gloves bSpringer International Publishing a1547–15503 aThis paper presents a kinesthetic glove realized with knitted piezoresistive fabric (KPF) sensor technology. The glove forefinger area is sensorized by two KPF goniometers obtained on the same piezoresistive substrate. The piezoresistive textile is used for the realization of both electrogoniometers and connections, thus avoiding mechanical constraints due to metallic wires. Sensors are characterized in comparison with commercial goniometers. The glove behavior is pointed out in terms of methacarpal-phalangeal and interphalangeal joint movement reconstruction.
10aBioengineering1 aMura, Dalle, G1 aLorussi, F.1 aTognetti, A.1 aAnania, G.1 aCarbonaro, N.1 aPacelli, M1 aParadiso, R1 aDe Rossi, D. uhttps://www.centropiaggio.unipi.it/publications/piezoresistive-goniometer-network-sensing-gloves.html-000620nas a2200217 4500008003900000245005500039210005300094260003500147300000800182100001400190700002000204700001400224700001500238700001700253700001700270700001600287700001400303700001400317700002300331856004800354 2013 d00aA Sensing Architecture for Empathetic Data Systems0 aSensing Architecture for Empathetic Data Systems bACM Digital Libraryc7-8 March a–1 aWagner, J1 aLingenfelser, F1 aAndré, E1 aMazzei, D.1 aTognetti, A.1 aDe Rossi, D.1 aBetella, A.1 aZucca, R.1 aOmedas, P1 aVerschure, P F M J uhttp://dx.medra.org/10.1145/2459236.245925300390nas a2200121 4500008004100000022001400041245006300055210006100118300000800179100001400187700001700201856005000218 2013 eng d a0921-029600aA Sensorized Garment Controlled Virtual Robotic Wheelchair0 aSensorized Garment Controlled Virtual Robotic Wheelchair a–1 aGulrez, T1 aTognetti, A. uhttp://dx.medra.org/10.1007/s10846-013-9839-100549nas a2200181 4500008004100000245004500041210004500086300000800131490001800139100001800157700001800175700001600193700001600209700001600225700001700241700001500258856009400273 2013 eng d00aStruttura protesica per amputazione mano0 aStruttura protesica per amputazione mano a–0 vPI2013A0000041 aBacchereti, M1 aCarbonaro, N.1 aGABRIELE, D1 aFERRETTI, L1 aPELLICCI, G1 aTognetti, A.1 aVitetta, N uhttps://www.centropiaggio.unipi.it/publications/struttura-protesica-amputazione-mano.html00504nas a2200157 4500008004100000245004500041210004500086100001800131700001800149700001900167700001600186700001600202700001700218700001500235856009600250 2013 eng d00aStruttura protesica per amputazione mano0 aStruttura protesica per amputazione mano1 aBacchereti, M1 aCarbonaro, N.1 aMura, Dalle, G1 aFERRETTI, L1 aPELLICCI, G1 aTognetti, A.1 aVitetta, N uhttps://www.centropiaggio.unipi.it/publications/struttura-protesica-amputazione-mano.html-000605nas a2200193 4500008004100000022001400041245011000055210006900165300001400234490000700248100001400255700001500269700001500284700001400299700001600313700001700329700001500346856005000361 2012 eng d a1089-777100aAssessment of Sensing Fire Fighters Uniforms for Physiological Parameter Measurement in Harsh Environment0 aAssessment of Sensing Fire Fighters Uniforms for Physiological P a501–5110 v161 aCurone, D1 aSecco, E L1 aCaldani, L1 aLanata, A1 aParadiso, R1 aTognetti, A.1 aMagenes, G uhttp://dx.medra.org/10.1109/TITB.2011.218261500602nas a2200181 4500008004100000245006300041210006200104300001400166653001900180100001700199700001600216700001400232700001300246700001600259700001700275700001500292856011300307 2012 eng d00aGait and Anthropometric Profile Biometrics: A Step Forward0 aGait and Anthropometric Profile Biometrics A Step Forward a105–12710aBioengineering1 aIoannidis, D1 aTzovaras, D1 aMura, G D1 aFerro, M1 aValenza, G.1 aTognetti, A.1 aPioggia, G uhttps://www.centropiaggio.unipi.it/publications/gait-and-anthropometric-profile-biometrics-step-forward.html00645nas a2200229 4500008004100000020001800041245004600059210004600105260003600151300001400187100001500201700001500216700002000231700001400251700001700265700001700282700001600299700001700315700001500332700001500347856005300362 2012 eng d a978144712753600aImmersive Multimodal Interactive Presence0 aImmersive Multimodal Interactive Presence aLONDON – GBRbSPRINGER-VERLAG a215–2281 aVanello, N1 aHartwig, V1 aScilingo, E. P.1 aBonino, D1 aRicciardi, E1 aTognetti, A.1 aPietrini, P1 aDe Rossi, D.1 aLandini, L1 aBicchi, A. uhttp://dx.medra.org/10.1007/978-1-4471-2754-3_1200897nas a2200277 4500008003900000245011000039210006900149300000800218100001600226700001500242700001700257700001600274700001700290700001600307700001700323700001500340700001600355700001500371700001600386700001600402700001700418700001500435700001600450700001500466856013800481 2012 d00aINTERACTION, Training and monitoring of daily-life physical interaction with the environment after stroke0 aINTERACTION Training and monitoring of dailylife physical intera a–1 aVeltink, PH1 aMeulen, FB1 aBeijnum, BJF1 aHermens, HJ1 aDe Rossi, D.1 aLorussi, F.1 aTognetti, A.1 aBuurke, JH1 aReenalda, J1 aBaten, CTM1 aSimons, CDM1 aLuft, AR, L1 aSchepers, HM1 aLuinge, HJ1 aParadiso, R1 aOrselli, R uhttps://www.centropiaggio.unipi.it/publications/interaction-training-and-monitoring-daily-life-physical-interaction-environment-after00640nas a2200157 4500008003900000245012800039210006900167260002100236300000800257100001800265700001700283700001500300700001800315700001700333856013200350 2012 d00aPersonal Biomonitoring System: a real-time physiological and behavioural parameter monitoring system for stress correlation0 aPersonal Biomonitoring System a realtime physiological and behav cSeptember 25-28, a–1 aCarbonaro, N.1 aTognetti, A.1 aAnania, G.1 aDalle Mura, G1 aDe Rossi, D. uhttps://www.centropiaggio.unipi.it/publications/personal-biomonitoring-system-real-time-physiological-and-behavioural-parameter00734nas a2200193 4500008003900000245013200039210006900171260001800240300001200258100001800270700001400288700001500302700001900317700001700336700002000353700001700373700001400390856013600404 2012 d00aUnobtrusive Physiological and Gesture Wearable Acquisition System: A Preliminary Study on Behavioral and Emotional Correlations0 aUnobtrusive Physiological and Gesture Wearable Acquisition Syste c21-26 October a88–921 aCarbonaro, N.1 aGreco, A.1 aAnania, G.1 aMura, Dalle, G1 aTognetti, A.1 aScilingo, E. P.1 aDe Rossi, D.1 aLanata, A uhttps://www.centropiaggio.unipi.it/publications/unobtrusive-physiological-and-gesture-wearable-acquisition-system-preliminary-study00557nas a2200169 4500008004100000022001400041245011400055210006900169300001400238490000600252100001500258700001400273700001700287700001700304700001500321856005100336 2012 eng d a2163-105000aValidation of Smart Garments for Physiological and Activity-Related Monitoring of Humans in Harsh Environment0 aValidation of Smart Garments for Physiological and ActivityRelat a189–1960 v21 aSecco, E L1 aCurone, D1 aTognetti, A.1 aBonfiglio, A1 aMagenes, G uhttp://dx.medra.org/10.5923/j.ajbe.20120204.0700414nas a2200121 4500008003900000245007300039210006900112300001400181100001700195700001600212700001700228856004700245 2012 d00aWearable systems for e-health: Telemonitoring and telerehabilitation0 aWearable systems for ehealth Telemonitoring and telerehabilitati a335–3381 aDe Rossi, D.1 aLorussi, F.1 aTognetti, A. uhttp://dx.medra.org/10.2316/P.2012.763-00400467nas a2200145 4500008003900000245006500039210006500104300001600169100001700185700001300202700001800215700001700233700002000250856005100270 2011 d00aElectroactive polymer patches for wearable haptic interfaces0 aElectroactive polymer patches for wearable haptic interfaces a8369–83721 aDe Rossi, D.1 aCarpi, F1 aCarbonaro, N.1 aTognetti, A.1 aScilingo, E. 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P.1 aCutolo, F1 aGiovannetti, G1 aPietrini, P1 aDe Rossi, D.1 aLandini, L uhttps://www.centropiaggio.unipi.it/publications/mr-compatible-sensing-glove-brain-studies.html-000708nas a2200253 4500008003900000245004900039210004800088260004500136300001400181100001500195700001400210700001700224700001500241700002000256700001500276700001700291700001400308700001400322700001900336700001600355700001700371700001500388856005100403 2010 d00aNeural Correlates of Human-Robot Handshaking0 aNeural Correlates of HumanRobot Handshaking aNEW YORK – USAbIEEEc13-15, Settembre a555–5611 aVanello, N1 aBonino, D1 aRicciardi, E1 aTesconi, M1 aScilingo, E. P.1 aHartwig, V1 aTognetti, A.1 aZupone, G1 aCutolo, F1 aGiovannetti, G1 aPietrini, P1 aDe Rossi, D.1 aLandini, L uhttp://dx.medra.org/10.1109/ROMAN.2010.559862400487nas a2200145 4500008004100000020001800041245008000059210006900139260002000208300001300228100001400241700001700255700001700272856005200289 2010 eng d a978184882598700aPervasive Computing: Innovations in Intelligent Multimedia and Applications0 aPervasive Computing Innovations in Intelligent Multimedia and Ap bSpringer London a97–1151 aGulrez, T1 aTognetti, A.1 aDe Rossi, D. uhttp://dx.medra.org/10.1007/978-1-84882-599-4_500605nas a2200217 4500008004100000022001400041245006400055210006300119300001400182490000700196100001400203700001500217700001700232700001400249700001400263700001500277700001300292700001700305700001500322856005000337 2010 eng d a1089-777100aSmart Garments for Emergency Operators: The ProeTEX Project0 aSmart Garments for Emergency Operators The ProeTEX Project a694–7010 v141 aCurone, D1 aSecco, E L1 aTognetti, A.1 aLoriga, G1 aDudnik, G1 aRisatti, M1 aWhyte, R1 aBonfiglio, A1 aMagenes, G uhttp://dx.medra.org/10.1109/TITB.2010.204500300520nas a2200157 4500008003900000245009100039210006900130260001400199300001600213100001700229700001600246700001700262700001500279700001700294856005100311 2010 d00aWearable monitoring of lumbar spine curvature by inertial and e-textile sensory fusion0 aWearable monitoring of lumbar spine curvature by inertial and et aUSAbIEEE a6373–63761 aBartalesi, R1 aLorussi, F.1 aDe Rossi, D.1 aTesconi, M1 aTognetti, A. uhttp://dx.medra.org/10.1109/IEMBS.2010.562729400607nas a2200157 4500008004100000022001400041245009400055210006900149300000800218100001600226700001600242700002000258700001700278700001700295856013700312 2010 eng d a1353-802000aA WEARABLE SYSTEM FOR MONITORING GESTURE, POSTURE AND PHYSIOLOGICAL CORRELATES OF EMOTION0 aWEARABLE SYSTEM FOR MONITORING GESTURE POSTURE AND PHYSIOLOGICAL a–1 aDebarnot, U1 aLorussi, F.1 aScilingo, E. 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P.1 aTognetti, A. uhttp://dx.medra.org/10.1109/IEMBS.2009.533448100724nas a2200205 4500008004100000022001400041245010600055210006900161300000800230653001900238100001600257700001500273700001700288700001500305700001400320700001500334700001800349700001700367856013400384 2008 eng d a0928-493100aElectroactive Carbon Nanotube Actuators: Soft-Lithographic Fabrication and Electro-chemical Modelling0 aElectroactive Carbon Nanotube Actuators SoftLithographic Fabrica a–10aBioengineering1 aMazzoldi, A1 aTesconi, M1 aTognetti, A.1 aRocchia, W1 aVozzi, G.1 aPioggia, G1 aAhluwalia, A.1 aDe Rossi, D. uhttps://www.centropiaggio.unipi.it/publications/electroactive-carbon-nanotube-actuators-soft-lithographic-fabrication-and-electro02166nas a2200337 4500008004100000245010800041210006900149300001200218490000700230520114700237653001201384653001301396100001501409700001501424700001501439700001701454700001701471700001401488700001501502700001501517700001901532700002001551700001901571700001601590700002201606700001501628700001601643700001701659700001501676856013701691 2008 eng d00aSensing Glove for Brain Studies: Design and assessment of its Compatibility for fMRI with a Robust Test0 aSensing Glove for Brain Studies Design and assessment of its Com a345-3540 v133 aIn this paper, we describe a biomimetic-fabric-based sensing glove that can be used tomonitor hand posture and gesture. Our device is made of a distributed sensor network of piezoresistive conductive elastomers integrated into an elastic fabric. This solution does not affect natural movement and hand gestures, and can be worn for a long time with no discomfort. The glove could be fruitfully employed in behavioral and functional studies with functional MRI (fMRI) during specific tactile or motor tasks. To assess MR compatibility of the system, a statistical test on phantoms is introduced. This test can also be used for testing the compatibility of mechatronic devices designed to produce different stimuli inside the MR environment. We propose a statistical test to evaluate changes in SNR and time-domain standard deviations between image sequences acquired under different experimental conditions. fMRI experiments on subjects wearing the glove are reported. The reproducibility of fMRIresults obtained with andwithout the glove was estimated. A good similarity between the activated regions was found in the two conditions.
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P.1 aCarpi, F1 aTesconi, M1 aTognetti, A.1 aOrsini, P uhttps://www.centropiaggio.unipi.it/publications/upper-limb-kinesthetic-system-tele-rehabilitation.html00660nas a2200181 4500008004100000245009400041210006900135260001300204300001200217490000600229653001900235100001600254700001500270700002000285700001700305700001700322856013900339 2004 eng d00aWearable, Redundant Fabric-Based Sensor Arrays for Reconstruction of Body Segment Posture0 aWearable Redundant FabricBased Sensor Arrays for Reconstruction cDecember a807-8180 v410aBioengineering1 aLorussi, F.1 aRocchia, W1 aScilingo, E. 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