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.2016 Feb 5;16(2):208.
doi: 10.3390/s16020208.

Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation

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Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation

Giuseppe Airò Farulla et al. Sensors (Basel)..

Abstract

Vision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic system, without requiring complex calibration procedures. Moreover, VPE interfaces are gaining momentum as they are highly intuitive, such that they can be used from untrained personnel (e.g., a generic caregiver) even in delicate tasks as rehabilitation exercises. In this paper, we present a novel master-slave setup for hand telerehabilitation with an intuitive and simple interface for remote control of a wearable hand exoskeleton, named HX. While performing rehabilitative exercises, the master unit evaluates the 3D position of a human operator's hand joints in real-time using only a RGB-D camera, and commands remotely the slave exoskeleton. Within the slave unit, the exoskeleton replicates hand movements and an external grip sensor records interaction forces, that are fed back to the operator-therapist, allowing a direct real-time assessment of the rehabilitative task. Experimental data collected with an operator and six volunteers are provided to show the feasibility of the proposed system and its performances. The results demonstrate that, leveraging on our system, the operator was able to directly control volunteers' hands movements.

Keywords: hand exoskeleton; hand telerehabilitation; motion tracking; upper limb rehabilitation.

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Figures

Figure 1
Figure 1
Model (a) and input data (b,c) used for training the RF classifier.
Figure 2
Figure 2
HX while holding the sensorized object in a pinch (a) and lateral (b) grasping exercise. The DoMs of the HX device are: (1) the flexion/extension of the index MCP; (2) of the index P-DIP (under-actuated); (3) of the thumb MCP and IP (under-actuated) and (4) the CMC opposition. Other Degrees-of-Freedom (DoF), like thumb intra/extra rotation and the index abduction/adduction, are passive [29]. The HX is used to grasp the sensorized object, whose squeezable soft-pads provide force information on the basis of a optoelectronic deformation transduction [34].
Figure 3
Figure 3
Pinch (a–d) and lateral (e–h) grasping sequences with overimposed fingertips as estimated by our VPE algorithm.
Figure 4
Figure 4
Graphical illustration of the distances computed to evaluate the percentage of completeness for the rehabilitative exercises: (a) pinch grasp; (b) lateral grasp. We used the 3D hand model from the libhand library [35] for illustration purpose.
Figure 5
Figure 5
Our experimental setup provides an RT communication link, a natural direct driving of the gesture, and an on-the-fly supervision method of the operator.
Figure 6
Figure 6
Experimental setup pipeline. (a) master unit: the VPE camera computes the joints positions of the operator’s hand; (b) slave unit: the subject wears the HX exoskeleton, which drives his/her fingers towards closure; the subject can freely move the arm; (c) squeezable grip sensor; (d) bi-directional link is realized through a UDP/IP communication between the VPE acquiring PC and the real-time control board driving the HX; (e) in the same PC, the operator visualizes in RT gripping force feedback from the slave unit. Down: same setup, addressing a lateral grasp. The white panel prevents the subject from having a visual clue of the operator’s intentions.
Figure 7
Figure 7
Example profiles of the tele-rehabilitation results. The first panel of (a) and (b) shows master desired (pre-filtering) closing percentagep, and grip force as recorded by the sensorized object, the other panels shows the slave desired (red) and measured (blue) position of the four DoM.
Figure 8
Figure 8
Aggregated cross-results of operator’s task execution speed and RMSE of HX motion from the desired, respectively for (a) pinch and (b) lateral grasps. The histogram bars represent the RMSE in degrees, while each task repetition is reported as a circle dot. The black small line in top of each histogram represents the mean speed, the black winds around it represent the standard deviation, and the gray wind indicates mean speed plus twice the standard deviation. Results are divided among the four DoM.
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