@conference {3451, title = {Separating haptic guidance from task dynamics: A practical solution via cutaneous devices}, booktitle = {2018 IEEE Haptics Symposium (HAPTICS)}, year = {2018}, month = {March}, pages = {20-25}, keywords = {Biomechanics, cutaneous forces, cutaneous guidance, Dynamics, Exoskeletons, guidance condition, guidance cues, guidance forces, haptic feedback, haptic guidance, haptic interfaces, human movement, kinesthetic guidance, rendering (computer graphics), separate haptic cues, Skin, Task analysis, task completion strategies, task complexity, task dynamics, task forces, Training, Trajectory, Wrist}, doi = {10.1109/HAPTICS.2018.8357147}, author = {E. Pezent and S. Fani and J. Bradley and M. Bianchi and M. K. O{\textquoteright}Malley} } @article {3448, title = {Simplifying Telerobotics: Wearability and Teleimpedance Improves Human-Robot Interactions in Teleoperation}, journal = {IEEE Robotics Automation Magazine}, volume = {25}, year = {2018}, month = {March}, pages = {77-88}, keywords = {application fields, augmented teleoperation, Autonomous robots, communication, effective design, effective simplification, environmental constraints, feedback, Force feedback, fundamental requirement, haptic feedback devices, haptic interfaces, human workspace, human-robot interaction, human-robot interactions, human-robot interfaces, ideal scenario, integrated approach, integrated interface, integrated system, interaction forces, intuitive information exchange, Kinematics, KUKA lightweight robotic arm, lightweight design, manipulators, master, position control, reduced versions, Robot sensing systems, robotic devices, robotic hand-arm system, robotic manipulator, robotic teleoperation, simplified information exchange, slave robot, stiffness control, synergy, Task analysis, teleimpedance techniques, Telemedicine, teleoperator system, telerobotics, TI control, vision, wearability, wearable feedback, wearable hand/arm}, issn = {1070-9932}, doi = {10.1109/MRA.2017.2741579}, author = {S. Fani and S. Ciotti and M. G. Catalano and G. Grioli and A. Tognetti and G. Valenza and A. Ajoudani and M. Bianchi} } @article {3697, title = {The SoftHand Pro: Functional evaluation of a novel, flexible, and robust myoelectric prosthesis}, journal = {PLOS One}, year = {2018}, author = {S. B. Godfrey and K. Zhao and A. Theuer and M. G. Catalano and M. Bianchi and R. Breighner and D. Bhaskaran and R. Lennon and G. Grioli and M. Santello and A. Bicchi and K. Andrews} } @conference {3747, title = {The softpro project: Synergy-based open-source technologies for prosthetics and rehabilitation}, booktitle = {International Symposium on Wearable Robotics}, year = {2018}, doi = {https://doi.org/10.1007/978-3-030-01887-0_71}, author = {C. Piazza and M. G. Catalano and M. Bianchi and E. Ricciardi and D. Pratichizzo and S. Haddadin and Luft, A. R. L. and O. Lambercy and R. Gassert and E. Jakubowitz and H. Van Der Kooij and F. Tonis and F. Bonomo and B. de Jonge and T. Ward and K. Zhao and M. Santello and A. Bicchi} } @article {3725, title = {SoftHand at the CYBATHLON: A user{\textquoteright}s experience}, journal = {JOURNAL OF NEUROENGINEERING AND REHABILITATION}, year = {2017}, doi = {10.1186/s12984-017-0334-y}, author = {S. B. Godfrey and M. Rossi and C. Piazza and M. G. Catalano and M. Bianchi and G. Grioli and K. Zhao and A. Bicchi} } @article {3258, title = {The SoftHand Pro-H: A Hybrid Body-Controlled, Electrically Powered Hand Prosthesis for Daily Living and Working }, journal = {IEEE Robotics and Automation Magazine}, year = {2017}, note = {

This work was supported by the European Commission project (Horizon 2020 research program) SOFTPRO 688857, the European Research Council under the Advanced Grant SoftHands {\textquotedblleft}A Theory of Soft Synergies for a New Generation of Artificial Hands,{\textquotedblright} ERC-291166, and the Proof of Concept Project SoftHand Pro-H, ERC-2016-PoC 727536.\ 

}, month = {11/2017}, keywords = {Haptics, Robotics}, issn = {1070-9932}, doi = {10.1109/MRA.2017.2751662}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8110634}, author = {C. Piazza and M. G. Catalano and S. B. Godfrey and M. Rossi and G. Grioli and M. Bianchi and K. Zhao and A. Bicchi} } @conference {3726, title = {Synergy-driven performance enhancement of vision-based 3D hand pose reconstruction}, booktitle = {Wireless mobile communication and healthcare : 6th International conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings}, year = {2017}, doi = {10.1007/978-3-319-58877-3_42}, author = {S. Ciotti and E. Battaglia and I. Oikonomidis and A. Makris and A. Tsoli and A. Bicchi and A. A. Agyros and M. Bianchi} } @inbook {3729, title = {Sensorymotor Synergies: Fusion of Cutaneous Touch and Proprioception in the Perceived Hand Kinematics}, booktitle = {SPRINGER SERIES ON TOUCH AND HAPTIC SYSTEMS}, volume = {Human and Robot Hands}, year = {2016}, url = {https://link.springer.com/book/10.1007/978-3-319-26706-7}, author = {A. Moscatelli and M. Bianchi and A. Serio and A. Bicchi and M. Ernst} } @conference {3086, title = {Soft Robots that Mimic the Neuromusculoskeletal System}, booktitle = {3rd International Conference on NeuroRehabilitation (ICNR2016)}, volume = {Converging Clinical and Engineering Research on Neurorehabilitation}, year = {2016}, month = {10/2016}, pages = {259-263}, publisher = {Springer}, organization = {Springer}, abstract = {

In motor control studies, the question on which
parameters human beings and animals control through their
nervous system has been extensively explored and discussed,
and several hypotheses proposed. It is widely acknowledged
that useful inputs in this problem could be provided by
developing artificial replication of the neuromusculoskeletal
system, to experiment different motor control hypothesis. In
this paper we present such device, which reproduces many of
the characteristics of an agonistic-antagonistic muscular pair
acting on a joint.

}, keywords = {Robotics}, doi = { 10.1007/978-3-319-46669-9_45}, url = {http://link.springer.com/chapter/10.1007/978-3-319-46669-9_45}, author = {M. Garabini and C. Della Santina and M. Bianchi and M. G. Catalano and G. Grioli and A. Bicchi} } @inbook {2865, title = {The SoftHand Pro: Translation from Robotic Hand to Prosthetic Prototype}, booktitle = {Converging Clinical and Engineering Research on Neurorehabilitation II}, volume = {15}, number = {Biosystems \& Biorobotics }, year = {2016}, note = {

Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), October 18-21, 2016, Segovia, Spain

}, pages = {469-473}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

This work presents the translation from a humanoid robotic hand to a prosthetic prototype and its first evaluation in a set of 9 persons with amputation. The Pisa/IIT SoftHand is an underactuated hand built on the neuroscientific principle of motor synergies enabling it to perform natural, human-like movements and mold around grasped objects with minimal control input. These features motivated the development of the SoftHand Pro, a prosthetic version of the SoftHand built to interface with a prosthetic socket. The results of the preliminary testing of the SoftHand Pro showed it to be a highly functional design with an intuitive control system. Present results warrant further testing to develop the SoftHand Pro.

}, keywords = {Haptics, Robotics}, issn = {978-3-319-46668-2}, doi = {10.1007/978-3-319-46669-9_78}, url = {http://link.springer.com/chapter/10.1007/978-3-319-46669-9_78}, author = {S. B. Godfrey and M. Bianchi and K. Zhao and M. G. Catalano and R. Breighner and A. Theuer and K. Andrews and G. Grioli and M. Santello and A. Bicchi} } @article {2660, title = {A synergy-based hand control is encoded in human motor cortical areas}, journal = {eLIFE}, year = {2016}, abstract = {

How the human brain controls hand movements to carry out different tasks is still debated. The concept of\ synergy\ has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses.

}, doi = {http://dx.doi.org/10.7554/eLife.13420}, url = {http://elifesciences.org/content/5/e13420v2}, author = {A. Leo and G. Handjaras and M. Bianchi and H. Marino and M Gabiccini and Guidi, A. and E. P. Scilingo and P. Pietrini and A. Bicchi and M. Santello and E. Ricciardi} } @inbook {2887, title = {Synergy-Based Optimal Sensing Techniques for Hand Pose Reconstruction}, booktitle = {Human and Robot Hands}, volume = {Springer Series on Touch and Haptic Systems}, year = {2016}, pages = {259-283}, publisher = {Springer}, organization = {Springer}, abstract = {

Most of the neuroscientific results on synergies and their technical implementations in robotic systems, which are widely discussed throughout this book (see e.g. Chaps.\ 2, 3, 4, 8, 10, 12 and 13), moved from the analysis of hand kinematics in free motion or during the interaction with the external environment. This observation motivates both the need for the development of suitable and manageable models for kinematic recordings, as described in Chap.\ 14, and the calling for accurate and economic systems or {\textquotedblleft}gloves{\textquotedblright} able to provide reliable hand pose reconstructions. However, this latter aspect, which represents a challenging point also for many human-machine applications, is hardly achievable in economically and ergonomically viable sensing gloves, which are often imprecise and limited. To overcome these limitations, in this chapter we propose to exploit the bi-directional relationship between neuroscience and robotic/artificial systems, showing how the findings achieved in one field can inspire and be used to advance the state of art in the other one, and vice versa. More specifically, our leading approach is to use the concept of kinematic synergies to optimally estimate the posture of a human hand using non-ideal sensing gloves. Our strategy is to collect and organize synergistic information and to fuse it with insufficient and inaccurate glove measurements in a consistent manner and with no extra costs. Furthermore, we will push forward such an analysis to the dual problem of how to design pose sensing devices, i.e.\ how and where to place sensors on a glove, to get maximum information about the actual hand posture, especially with a limited number of sensors. We will study the optimal design of gloves of different nature. Conclusions that can be drawn take inspiration from and might inspire further investigations on the biology of human hand receptors. Experimental evaluations of these techniques are reported and discussed.

}, isbn = {978-3-319-26705-0}, doi = {10.1007/978-3-319-26706-7_15}, author = {M. Bianchi and Salaris, P. and A. Bicchi} } @article {2867, title = {A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition}, journal = {Sensors}, volume = {16}, year = {2016}, abstract = {

Achieving 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.

}, keywords = {Haptics}, doi = {10.3390/s16060811}, url = {http://www.mdpi.com/1424-8220/16/6/811}, author = {S. Ciotti and E. Battaglia and N. Carbonaro and A. Bicchi and A. Tognetti and M. Bianchi} } @article {1230, title = {Synergy-Based Hand Pose Sensing: Optimal Glove Design}, journal = {International Journal of Robotics Research}, volume = {32}, year = {2013}, pages = {396-406}, abstract = {

In this paper we study the problem of improving human hand pose sensing device performance by exploiting the knowledge on how humans most frequently use their hands in grasping tasks. In a companion paper we studied the problem of maximizing the reconstruction accuracy of the hand pose from partial and noisy data provided by any given pose sensing device (a sensorized "glove") taking into account statistical a priori information. In this paper we consider the dual problem of how to design pose sensing devices, i.e. how and where to place sensors on a glove, to get maximum information about the actual hand posture. We study the continuous case, whereas individual sensing elements in the glove measure a linear combination of joint angles, the discrete case, whereas each measure corresponds to a single joint angle, and the most general hybrid case, whereas both continuous and discrete sensing elements are available. The objective is to provide, for given a priori information and fixed number of measurements, the optimal design minimizing in average the reconstruction error. Solutions relying on the geometrical synergy definition as well as gradient flow-based techniques are provided. Simulations of reconstruction performance show the effectiveness of the proposed optimal design.

}, keywords = {Robotics}, doi = {10.1177/0278364912474079}, author = {M. Bianchi and P Salaris and A. Bicchi} } @article {1231, title = {Synergy-based Hand Pose Sensing: Reconstruction Enhancement}, journal = {International Journal of Robotics Research}, volume = {32}, year = {2013}, pages = { 407-424}, abstract = {

Low-cost sensing gloves for reconstruction posture provide measurements which are limited under several regards. They are generated through an imperfectly known model, are subject to noise, and may be less than the number of Degrees of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of the hand posture is an ill-posed problem, and performance can be very poor. This paper examines the problem of estimating the posture of a human hand using(low-cost) sensing gloves, and how to improve their performance by exploiting the knowledge on how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware - hence basically at no extra cost - we propose to collect, organize, and exploit information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such an a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide a good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low-cost glove are reported which demonstrate the effectiveness of the proposed techniques.

}, keywords = {Robotics}, doi = {10.1177/0278364912474078}, author = {M. Bianchi and P Salaris and A. Bicchi} } @conference {1240, title = {Synergy-based Optimal Desing of Hand Pose Sensing}, booktitle = {International Conference of Intelligent Robots and Systems - IROS 2012- JTCF Award}, year = {2012}, month = {October 7 - 12}, pages = {3929 - 3935 }, address = {Vilamoura, Algarve, Portugal}, keywords = {Robotics}, author = {M. Bianchi and P Salaris and A. Bicchi} } @article {3648, title = {Skin stretch haptic feedback to convey closure information in anthropomorphic, under-actuated upper limb soft prostheses}, journal = {IEEE Transactions on Haptics ( Early Access )}, author = {E. Battaglia and J. Clark and M. Bianchi and M. G. Catalano and A. Bicchi and M. K. O{\textquoteright}Malley} }