A two DoF finger for a biomechatronic artificial hand

Current prosthetic hands are basically simple grippers with one or two degrees of freedom, which barely restore the capability of the thumb-index pinch. Although most amputees consider this performance as acceptable for usual tasks, there is ample room for improvement by exploiting recent progresses in mechatronics design and technology. We are developing a novel prosthetic hand featured by multiple degrees of freedom, tactile sensing capabilities, and distributed control. Our main goal is to pursue an integrated design approach in order to fulfill critical requirements such as cosmetics, controllability, low weight, low energy consumption and noiselessness. This approach can be synthesized by the definition "biomechatronic design", which means developing mechatronic systems inspired by living beings and able to work harmoniously with them. This paper describes the first implementation of one single finger of a future biomechatronic hand. The finger has a modular design, which allows to obtain hands with different degrees of freedom and grasping capabilities. Current developments include the implementation of a hand comprising three fingers (opposing thumb, index and middle) and an embedded controller

Brain response to a humanoid robot in areas implicated in the perception of human emotional gestures

BACKGROUND: The humanoid robot WE4-RII was designed to express human emotions in order to improve human-robot interaction. We can read the emotions depicted in its gestures, yet might utilize different neural processes than those used for reading the emotions in human agents. METHODOLOGY: Here, fMRI was used to assess how brain areas activated by the perception of human basic emotions (facial expression of Anger, Joy, Disgust) and silent speech respond to a humanoid robot impersonating the same emotions, while participants were instructed to attend either to the emotion or to the motion depicted. PRINCIPAL FINDINGS: Increased responses to robot compared to human stimuli in the occipital and posterior temporal cortices suggest additional visual processing when perceiving a mechanical anthropomorphic agent. In contrast, activity in cortical areas endowed with mirror properties, like left Broca’s area for the perception of speech, and in the processing of emotions like the left anterior insula for the perception of disgust and the orbitofrontal cortex for the perception of anger, is reduced for robot stimuli, suggesting lesser resonance with the mechanical agent. Finally, instructions to explicitly attend to the emotion significantly increased response to robot, but not human facial expressions in the anterior part of the left inferior frontal gyrus, a neural marker of motor resonance. CONCLUSIONS: Motor resonance towards a humanoid robot, but not a human, display of facial emotion is increased when attention is directed towards judging emotions. SIGNIFICANCE: Artificial agents can be used to assess how factors like anthropomorphism affect neural response to the perception of human actions

Mean Detection Time.

<p>The Figure shows the Mean Detection Time (<i>MDT</i>) obtained for each type of perturbation (i.e., <i>NR</i>, <i>NE</i>, <i>E</i>, <i>SE</i>, <i>SR</i>, <i>NL</i>, <i>NW</i>, <i>W</i>, <i>SW</i>, <i>SL</i>) averaged across all participants (dark gray area) plus one standard deviation (light gray area) considering the all-segments (<i>ALL</i>), the feet (<i>F</i>), the hands (<i>H</i>) and the feet-hands (<i>F-H</i>) combinations. All the values are expressed in <i>ms</i>.</p

The table shows the Mean Detection Time (<i>MDT</i>), <i>Sensitivity</i>, <i>Specificity</i> and <i>Accuracy</i> for the all-segments combination (<i>ALL</i>) and the reduced-segments combinations chosen after the <i>Ranking</i>.

<p>The accounted combinations are feet (<i>F</i>), hands (<i>H</i>), and feet-hands (<i>F-H</i>). The p-values are related respectively to the two-ways ANOVA (i.e., effect of the direction and side of the perturbation) and the t-tests (i.e., difference of each reduced-segments combination with respect to the all-segments one) on the <i>MDT</i> obtained for each subject and each type of perturbation (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092037#s2" target="_blank"><i>Materials and Methods</i></a>). When a p-value is statistically significant (<i>p</i><0.05), it is highlighted in bold.</p

Experimental setup.

<p>The subplot A consists of a picture of the SENLY platform. The subplot B represents the 10 types of perturbations. Each perturbation involved the combination of longitudinal (i.e., North, <i>N</i> or South, <i>S</i>) and transversal (i.e., East, <i>E</i>, or West, <i>W</i>) movements of the belt provided while participants were walking steadily. Five perturbations were delivered on the left foot (i.e., <i>NL</i>, <i>NW</i>, <i>W</i>, <i>SW</i>, <i>SL</i>) and five on the right foot (i.e., <i>NR</i>, <i>NE</i>, <i>E</i>, <i>SE</i>, <i>SR</i>). The subplot C shows an example of the reconstruction of the biomechanical model of a representative subject. The vertexes of each polygon and the dots represent respectively the markers position and the <i>CoM</i> of each body segment with the corresponding acronyms.</p

Total Segment Weight.

<p>The Figure shows the mean and one standard deviation (error bar) of the Total Segment Weight (<i>TSW</i>) of each body segment that is the cumulative weight of the accounted segment on the <i>ICs</i> extracted. The <i>TSW</i> was normalized and expressed as a percentage. The 15 segments are: head/neck (<i>H/N</i>), chest (<i>T</i>), abdomen/pelvis (<i>P</i>), upper arms (<i>LA</i> and <i>RA</i>), forearms (<i>LFA</i> and <i>RFA</i>), hands (<i>LH</i> and <i>RH</i>), thighs (<i>LT</i> and <i>RT</i>), shanks (<i>LS</i> and <i>RS</i>) and feet (<i>LF</i> and <i>RF</i>). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092037#pone.0092037.s001" target="_blank">Appendix S1</a> for further details.</p

Analysis of neuritic terminals.

<p>(A) Typical bright field images of PC12 cells differentiated by NGF on period 1, 1.5 and 2 gratings and on flat substrate (from the top, respectively). White arrows: grating direction; bars = 20 µm. (B) Typical confocal images of the morphological aspect of terminals with non-spread growth cones: PC12 neurite terminals grown on period 1, 1.5 and 2 gratings and on flat substrate, and stained for β3-Tubulin (green) and actin (red). Each panel side = 25 µm; square inset: grating direction. (C) Analysis of PC12 neuritic terminals over different nanogratings (periods 1, 1.5, 2 µm) and flat substrate: terminals were characterized with respect to their morphology as spread or non-spread growth cones. The analysis was carried out on 323 terminals. (D) SEM images of PC12 growth cones on period 1 nanogratings: a spread growth cone (left), magnification = 3110 X, bar length =  1 µm; a non-spread growth cone, presenting lateral transient processes (right), magnification = 12550 X, bar length =  1 µm.</p

In silico simulations of beam splitting experiments.

<p>(A) In silico simulation of a group of 30 cells on a swallowtail grating. Different phases of neuritic outgrowth are shown on the planes (in perspective) over time (t). In simulations, geometric and fasciculation effects were considered together. (B) Geometrical angles to model the swallowtail: the φ angle accounted for the steepness of the change between the straight grating and the following bifurcation. (C) Percentage of turning axons with different values of the φ (swallowtail) angle. This percentage decreased as the φ angle increased, in agreement with literature <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070304#pone.0070304-Francisco1" target="_blank">[39]</a>.</p

From biological experiments to computational models.

<p>(A) A SEM image of filopodia emerging from a non-spread growth cone (bar = 1 µm). (B) FE model of a non-spread growth cone showing a simplified geometry together with an emerging filopodium. The set of parameters necessary to characterize the nanograting geometry is also shown: ridge width r_w, groove width g_w, and ridge depth r_d. (C) Bidimensional model of interactions between non-spread growth cone and ridge surface. Point K2 (together with K1, symmetric with respect to the centreline of the filopodial shaft) shows the limit angle β_lim. The quantity h^* was connected to the actual intersection angle through a fraction of the ridge width (a). (D,E) Quantile-quantile plot of the quantity h as derived from in silico simulations, together with its box plot.</p

Analytic model.

<p>(A) Quantitative prediction of the ridge width to achieve a given mean alignment angle β. All experimental points were obtained keeping the ridge depth constant (350 nm) while the ridge width varied in the range 500–2000 nm. Inset: magnification for small values of β. (B) Influence of the ridge depth on the value of β when the ridge width was kept constant and the ridge depth varied between 0 (flat) and 350 nm.</p