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. 2011;11(1):207-27.
doi: 10.3390/s110100207. Epub 2010 Dec 28.

Sensing pressure distribution on a lower-limb exoskeleton physical human-machine interface

Stefano Marco Maria De Rossi  1 , Nicola Vitiello, Tommaso Lenzi, Renaud Ronsse, Bram Koopman, Alessandro Persichetti, Fabrizio Vecchi, Auke Jan Ijspeert, Herman van der Kooij, Maria Chiara Carrozza
Affiliations

Sensing pressure distribution on a lower-limb exoskeleton physical human-machine interface

Stefano Marco Maria De Rossi et al. Sensors (Basel). 2011.

Abstract

A sensory apparatus to monitor pressure distribution on the physical human-robot interface of lower-limb exoskeletons is presented. We propose a distributed measure of the interaction pressure over the whole contact area between the user and the machine as an alternative measurement method of human-robot interaction. To obtain this measure, an array of newly-developed soft silicone pressure sensors is inserted between the limb and the mechanical interface that connects the robot to the user, in direct contact with the wearer's skin. Compared to state-of-the-art measures, the advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for the assessment of safety and comfort of human-robot interaction. This paper presents the new sensor and its characterization, and the development of an interaction measurement apparatus, which is applied to a lower-limb rehabilitation robot. The system is calibrated, and an example its use during a prototypical gait training task is presented.

Keywords: distributed force sensor; human-robot interaction; lower-limb exoskeleton; physical human-machine interface.

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Figures

Figure 1.
Figure 1.
(a) A cross section of the sensor, showing the light transmitter (TX) and receiver (RX) (b) Position of the eight sensitive elements. (c) Overall view of an 8-channel Skilsens Pad.
Figure 2.
Figure 2.
Cross section of the Skilsens pad. Highlighted are the internal (R2) and external (R1) radii, the upper thickness (H2) and lower thickness (W), and the inner height (H1).
Figure 3.
Figure 3.
3D CAD representation of the simulated setup. In transparent brown, the rigid flat indenter, in grey, the silicone structure and in green, the PCB. (a) Undeformed structure. (b) Map of total stress. Blue corresponds to higher stress areas, green to lower stress areas. (c) Map of total deformation. In blue, the areas suffering a bigger deformation.
Figure 4.
Figure 4.
Force/Deformation characterization of the pad, after five loading-unloading cycles.
Figure 5.
Figure 5.
Voltage Output/Force behavior of the sensor. The fitted model is reported for each of the eight channels.
Figure 6.
Figure 6.
Force estimation results under non-uniform loading conditions (a) with a curvature of 3 m−1, (b) with a curvature of 5 m−1.
Figure 7.
Figure 7.
(a) The LOPES gait rehabilitation exoskeleton. The right leg upper cuff is equipped with the sensory system. (b) The LOPES exoskeleton during operation.
Figure 8.
Figure 8.
(a) Transversal section of the sensorized fastening belt. (b) 3D sketch of the sensor housing. (c) Experimental setup for the cuff used in the experiments.
Figure 9.
Figure 9.
Hip torque steps performed during the characterization.
Figure 10.
Figure 10.
Static characterization results: (a) Force on the three pads, compared with the force recorded by the load cell. (b) Pressure distribution on the sensor at zero and peak interaction. Channels are ordered from top (1) to bottom (8).
Figure 11.
Figure 11.
Dynamic characterization results: Force acting on the three pads, compared with the force recorded by the load cell.
Figure 1.
Figure 1.
(a) Force on the three pads compared with the measurement of the load cell, during a walking task. (b) Pressure distribution on one of the frontal pads.
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