@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}
}
@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}
}
@article {2867,
	title = {A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition},
	journal = {Sensors},
	volume = {16},
	year = {2016},
	abstract = {<p class="rtejustify">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, <em>i.e.</em>, 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.</p>
},
	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}
}
