In this study, we report the novel energy behavior of high-performance nanostructured materials in a segmented thermoelectric micro-generator (TEG). Several physical elements of the materials must be considered to determine their behavior in the thermoelectric energy conversion: temperature dependence of material properties, geometric structure, segmentation, and the symmetry of each or both p-type and n-type nanostructure semiconductor thermoelements. Recently, many efforts have reported effects independent on the thermoelectric performance of semiconductor materials. In this work, exhaustive research on the performance of high-performance nanostructured materials in a segmented thermoelectric micro-generator (TEG) was carried out. Our results show the efficiency and output power of the TEG using the temperature-dependent model, i.e., a variable internal resistance for a load resistance of the system. Our approach allows us to analyze symmetrical and asymmetric geometries, showing maximum and minimum peaks values in the performance of the TEG for specific  There are three methods to improve the performance of thermoelectric systems: material preparation, device design, and operation control. The development of new materials seeks to obtain materials with a high figure of merit values. The device’s design requires an optimal relationship between the geometric factors, length, and cross-sectional area of the thermoelements, while the operating conditions of the TEG analyze the difference in temperature, thermal and electrical resistance, among other factors3. Recent researches have shown novel methods to improve the efficiency of thermoelectric materials; in general, nanostructuring is focused on reducing thermal conductivity without significantly affecting electrical conductivity and increasing the Seebeck coefficient. Nanostructured materials have been an important advance in the optimization of the figure of merit (ZT). The maximum efficiency of a thermoelectric material depends on thermoelectric properties through the figure of merit, 5. Hu et al.6 proposed a method based on hot deformation (HD)-induced multi-scale microstructures. They showed that the thermal conductivity of the lattice was considerably reduced by the presence of in situ nanostructures, which were induced by recrystallization and high-density lattice defects. The preferred orientation formation during the HD process improves the electrical conductivity, and the donor effect partially compensates for the concentration of the gap, increasing the Seebeck coefficient. The high performance of nanostructured materials is due to the reduction of bipolar conduction in thermal conductivity through the nano inclusions by milling and spark plasma sintering7. Furthermore, it has been proved that by controlling hierarchical architecture, from atomic-scale to mesoscale of a PbTe p-type nanostructured material, a large enhancement in the thermoelectric performance is achieved with values of 8. Determine the optimal geometric parameters, i.e., cross-sectional area, length, and the number of thermoelements pairs, which directly affect the performance of TEG systems is another strategy to optimize energy harvesting from waste heat. Recent work showed that changing the shape of the legs  and the alignment of the elements (inclination angle between the edge and the base), impacts temperature distribution, output power, efficiency conversion, and thermal stress distribution and also can improve TEG performance11. Shittu et al.12 showed that when making a severe geometric modification in one of the thermoelements, the output power can be increased; In the case of a rectangular thermoelement in conjunction with a trapezoidal thermoelement, the effect of asymmetry and segmentation is reflected in the increase of the output power by 117A thermoelectric generator (TEG) is a solid-state device with significant advantages, no moving parts, no constant maintenance, not use fossil fuels and silent in operation, etc. In recent years the benefits of TEG have caught interest as a viable alternative in generating energy friendly to the environment. However, the efficiency of commercial thermoelectric devices does not exceed 5–1013 show a segmented thermoelectric system with symmetric and asymmetric models. Symmetrical models are constructed with an area of 1474.56 mm2, it combines traditional and nanostructured thermoelectric materials with the constant load resistance. The optimization of the area relationship between thermoelements  and segmented annular generators (SATEG) show that the performance is optimal when the annular shape parameter 18 studies the influence pin shape configuration and with exponential area variation of the thermoelements on the performance of segmented TEGs. It shows that, the proper selection of the geometric shape of the thermoelement, temperature ratio  appear in the efficiency for critical values of the The efficiency for Figure Figure For Figure In the 2p-3n segmentation, there are also critical values of the geometric shape factor Asymmetric geometric systems show lower performance than conventional segmented thermoelectric devices—this due to the load resistance. In the asymmetric systems, since the internal resistance of the trapezoidal shape is different from the rectangular shape, the heat flux is affected. In trapezoidal shapes, the heat transfer rate is lower in small cross-sectional areas. In contrast, the heat flux is homogeneous for rectangular shapes. The maximum efficiency and power values for the symmetric and asymmetric models in the two segmentations 3p-2n and 2p-3n are shown in the Table Our work analyzes the performance of the segmented TEG, for different cases under the same operating conditions with a wide range of possibilities, due to the variable value of The effect of asymmetric segmented thermoelements on the performance of a thermoelectric generator has shown a significant improvement compared to standard rectangular thermoelements. Our results show the performance behavior for the symmetric and asymmetric segmented system. For asymmetric shapes, by establishing the same operating conditions to standard thermoelements but considering the optimal load resistance ratio and the internal resistance, which both differ from the standard form, power output and efficiency show peaks of maximum and minimum performance depending on the geometric form factor In previous work, the investigation of the thermoelement geometric shape together with the load resistance showed that the performance of a thermoelectric device depends directly on the load resistance and the internal resistance of the TEG, due to a decrease in the thermal and electrical conductivity, which in turn produces a reduction in the temperature difference Besides, previous works have shown that it is possible to determine the angle of inclination and the shape of the thermoelements in order to improve the TEG performance. However, in most studies, an optimized constant load resistance calculated according to average values is taken into account. A constant load resistance limits the scope of the maximum performance of the TEG. The results shown in this work reveal the real effect of the geometric shape and the temperature-dependent thermoelectric properties on the performance of the TEG. Then, to obtain the maximum performance, the load resistance must be chosen in relation to the internal resistance based on the geometric shape factor and the temperature-dependent thermoelectric properties of the nanostructured materials.Figure It is observed that the efficiency increases with Figure 29, which shows that an asymmetrical geometric factor has a higher performance when the cross-sectional area of the thermoelement is larger on the hot face, in addition to having a high load resistance ratio. The results are shown in Fig. Figure The global temperature difference defines the operating range for each material, which implies that the internal resistance of the TEG depends on 30.The geometric factor effect on TEG performance and temperature profile is minimum in a thermoelement with homogeneous cross-sectional area and constant volume. Therefore, by comparing rectangular thermoelements to cylindrical and octagonal thermoelements, their performance is approximately equal, this fact is because the internal resistance of the generator is similar regardless of the geometric factorThe geometric factor of the element did not have any significant influence on the maximum power in some specific load resistance values. This is because the maximum power was calculated using different temperature differences, and the internal electrical resistance is, in turn, calculated in function of the geometry of the elements. Considering the same temperature difference between the internal resistances, we can generate the same output power, and the surface differences are small enough not to distinguish operation condition patterns. However, our results show that the efficiency and out power increases almost linearly with the temperature and the geometric factor: increasing temperature difference increases the hot side temperature. Consequently, at a higher temperature, higher efficiency is obtained for improving the conversion efficiency of the device. This matter because, with making a thermal difference at both sides of the n-type semiconductor, which has a negative Seebeck coefficient, electron flux will release toward the cold side that has less level of energy. Consequently, the negative electric potential will emerge on this side, and in the p-type arm which has positive Seebeck coefficient, holes will treat unlike of electrons in n-type arm.The trapezoidal shapes have the same volume as the rectangular shape. However, they have a cross-sectional area that is not homogeneous along the thermoelement, causing a different internal resistance of the TEG compared to the rectangular one; this effect modifies the optimal ratio between load resistance and internal resistance with a significant impact on performance. Maximum efficiency and output power can be obtained when the load resistance is equal to the internal resistance of the TEG. However, when considering temperature-dependent materials, the internal resistance varies with the temperature difference. Therefore, the external load resistance varies until the maximum performance of the TEG is obtained, which represents the corresponding load condition for each system.In this study, we numerically simulate both by controlling the external resistance. The output power becomes smaller when is smaller, i.e. g to Eq. , the avaIn this work it has been shown that the geometric factor together with the load resistance plays an important role on TEG performance and its impact on performance is even more significant when the cross-sectional area is not homogeneous. The results show that an incorrect selection of load resistance for the trapezoidal shape causes a performance below the rectangular shape as shown in the Figs. However, with the inverted trapezoidal shape and the optimal load resistance, maximum performance is obtained, above the rectangular shape, as shown in the Fig. It is well known that experimental results differ from simulations results since small variations of size can produce changes in performance. Previously investigation of TEG’s attempt to identify the parameters causing these differences. If a constant temperature difference is assumed, mathematical solutions are easy and suitable; however, TE modules do not work in ideal parameters. Thereby, it is possible to estimate surfaces of efficiency and output power in this situation and to establish equivalence between different structures. Understanding the physical aspects investigated in this paper in conjunction with the thermoelectric properties of heterogeneous element structures is critically important for future design and advancement in solid-state devices. Temperature-dependent properties material is used to ensure result accuracy, as is presented in Fig. With the proper selection of design parameters, we can establish a direct relationship between the systems studied under various operating conditions, and thus, expand the range of application of these devices by not being limited by their geometric shape.31. The fabrication of TEG’s has also been shown using high ZT nanostructured bulk (nanobulk) half- Heusler alloys using a highly scalable process32.A combination of electrochemical deposition techniques and integrated circuit technology have been developed to manufacture high-performance microdevices. It has been shown that electrodeposition both n-type and p-type thick In this work, we report the behavior of various high ZT nanostructured thermoelectric materials, which for the purposes of waste heat energy harvesting applications, could be used for nanobulk applications. However, in future work, the more extensive analysis on the effect of the size of nanostructured materials for applications such as thin and thin films, nanowires among other,s will be investigated.Traditionally, the analysis of thermoelectric device optimization methods is limit by a focus on a specific objective. In this paper, a new multi-objective study for a TEG optimization model has been described according to geometric factor, segmentation, load resistance, and temperature-dependent nanostructured material properties. The following conclusions are obtained from the theoretical performance analysis of a TEG with segmented symmetric and asymmetric thermoelements and nanostructured materials.In the 2p-3n symmetric system, when the load resistance is established according to average properties material and The effect of asymmetrical shape in the thermoelements on performance shows slight inflections, for a specific value ranges of Compared with previous work, the temperature dependence of the nanostructured materials and the geometric form factor are considered in this work, which results in changes in the internal resistance for all temperature gradients considered. Still, the value of the load resistance remains constant. With these considerations, new slopes (or inflections) appear in the efficiency for critical values of However, when modifying the load resistance, it is shown that the inverted trapezoidal geometry obtains an increase in efficiency in 7


Embodiment is the percept that something not originally belonging to the self becomes part of the body. Feeling embodiment for a prosthesis may counteract amputees’ altered image of the body and increase prosthesis acceptability. Prosthesis embodiment has been studied longitudinally in an amputee receiving feedback through intraneural and perineural multichannel electrodes implanted in her stump. Three factors—invasive (vs non-invasive) stimulation, training, and anthropomorphism—have been tested through two multisensory integration tasks: visuo-tactile integration (VTI) and crossing-hand effect in temporal order judgment (TOJ), the former more sensible to an extension of a safe margin around the body and the latter to action-oriented remapping. Results from the amputee participant were compared with the ones from healthy controls. Testing the participant with intraneural stimulation produced an extension of peripersonal space, a sign of prosthesis embodiment. One-month training extended the peripersonal space selectively on the side wearing the prostheses. More and less-anthropomorphic prostheses benefited of intraneural feedback and extended the peripersonal space. However, the worsening of TOJ performance following arm crossing was present only wearing the more trained, despite less anthropomorphic, prosthesis, suggesting that training was critical for our participant to achieve operative tool-like embodiment. Despite improved mechatronic features have made hand prostheses more dexterous, their abandonment exceeds 30% . BesidesAs a multipurpose tool allowing capabilities and conveying sensory inflows, the hand is located at the human-environment frontier and defines the boundary of the body, so that its loss greatly affects how amputees perceive themselves and their body . SomatosThose reasons generated a strong research effort to develop and test prosthesis-user interfacing systems that, in parallel to a better motor control, can offer rich and pleasant feedback. Sensory feedback from hand prostheses have been delivered employing intraneural  and periConvergent multisensory afference build the representation of the body in the brain, which has been shown to be flexible to the point of integrating external objects not belonging to the self; the perceptual process producing such integration is known as embodiment . Thus, aFew reports tested the embodiment of worn prostheses able to deliver sensory feedback in amputees with targeted sensory reinnervation , perineuHowever, the translation of RHI findings to prosthesis embodiment in amputees is not free from possible pitfalls; RHI setup is very structured and artificial, the fake hand is not worn and typically cannot move, and the illusion is only temporary .Easily gathered clues on embodiment also come from changes reported by the subject in the perceived length of the phantom limb . Still, Any outer event is caught by different sensory modalities; their flow of information is integrated by our brain to have a high probability of experiencing the fact with accuracy. The way the brain integrates senses while experiencing an event depends on the relation of that event with our body. For instance, our brain integrates faster somatosensory and visual stimuli delivered close to the body, within the so-called peripersonal space (PPS) . PPS is In the investigation of multisensory integration, the computation of sensory stimuli can be assessed in reference to the somatotopic map or in reference to the external, egocentric space. On one hand, the former can be achieved through visuo-tactile integration (VTI) tasks. In a VTI, the participant has to respond to a stimulus delivered to a limb, while a concurrent incoming visual stimulus is delivered at different distances. The closer to the body the visual stimulus is, the faster the reaction time (RT). Since VTI tests the area of visuo-tactile integration, which is extended by an embodied extracorporeal tool, it has been employed to assess embodiment in the animal and in humans .The same approach, albeit using audio-tactile stimuli, has been recently adopted to assess the extension of PPS as a proxy of tools  and prosOn the other hand, the computation of sensory stimuli in reference to the external, egocentric space can be investigated through a temporal order judgment (TOJ) task . In the In the present study, we recruited a chronic amputee volunteer and implanted intraneural and perineural multichannel electrodes on her stump to deliver sensory feedback from the prosthesis. After a period of training, she was able to perform grasps and dexterous manipulation, thanks to a closed-loop control enabled by neural feedback . In thisCompared to previous work, this is the first study that longitudinally investigates multiple determinants of prosthesis embodiment through proxies of body representation not derived from the rubber hand illusion or telescoping assessments, but investigating how the relation that multisensory integration has with the body impacts on reaction time. This has been done in a participant naïve for active prosthesis use and in a context of ecologic continuative use of a worn and neurally interfaced hand prosthesis.The impact of intraneural stimulation on embodiment was investigated because invasive stimulation showed to achieve a more physiological sensory feedback , and theTo evaluate statistical significance of the results achieved with our participant in both experiments, they were compared with the data coming from a group of healthy controls.The part of this study involving the amputee participant was conducted at Campus Bio-Medico University Hospital of Rome in accordance with the Declaration of Helsinki and following amendments, and it was approved by the local Ethics Committee and by the assigned office of the Italian Ministry of Health. The volunteer participant signed an informed consent form. She is a right-handed female, 40 years old at the time of the experiment. Almost 30 years before, she was exposed to an explosion that produced a transradial left upper limb amputation.The participant underwent a surgical procedure under general anesthesia where two intraneural multichannel electrodes (ds-FILEs) and one cuff electrode  were implanted in the ulnar nerve trunk, and the same was done in the median nerve trunk , achieviA: IH2 Azzurra, Prensilia s.r.l.1) and with a more anthropomorphic commercial device . Prosthesis A is an optimal robotic hand research platform, mostly open and flexible to be utilized in different lab experiments on human grip and manipulation. Prosthesis B is designed for amputee end-users to be employed in their daily living tasks at home, and during their social activities. The shape of prosthesis B was closer to the one of the human hand , and its weight and size were very similar to the ones of our participant contralateral healthy hand  embedded into the socket, while forces of interaction with objects were measured with force-sensing resistors (Interlink Electronics Inc.) embedded in both prostheses’ fingers and fed back through neural interfaces.ad hoc developed algorithm based on non-linear logistic regression allowed to perform power, pinch, lateral grasps, rest, open the hand, and to apply three levels of force. Every time the participant manipulation was evaluated, she performed 24 repetitions of each of the following four tasks: (A) Lateral grasp of large and small objects; (B) pick and place of large objects with a power grasp; (C) pick and place of small objects with a precision grasp; and (D) manipulation tasks featuring: pouring water from a bottle to a cup and sorting cylindric and circular-shaped objects  Pre-training, non-invasive stimulation: baseline measure, taken when the electrodes were already implanted but the subject had not yet started the training with the prosthesis. Stimulation was delivered to the stump through mechanical tappers.(•POST_I) Post-training, invasive stimulation: 50 days after PRE and after 30 days of training with the prostheses. Somatosensory stimulation was delivered to the severed limb through intraneural electrodes.(•POST_NI) Post-training, non-invasive stimulation: after electrodes removal. Stimulation was delivered to the severed limb through mechanical tappers.POST_NI (post_non invasive) collected after electrodes removal was compared with POST_I (post_invasive) performed while the participant still had the intraneural electrodes implanted. In both sessions, the healthy limb was stimulated non-invasively. In POST_I the stimulation was delivered intraneurally to the affected left limb. For a comprehensive description of the features and location of referred sensation, please see To test the impact of invasive stimulation on the achieved embodiment, the data from two sessions were compared: (ii)PRE), and after training (POST_NI).To investigate the impact of training on embodiment of neurally interfaced prosthesis, the participant, who was naïve for active prosthesis use, was tested non-invasively before ((iii)POST_I, the participant was tested while wearing the two different prosthetic devices (Prosthesis A and Prosthesis B).To investigate embodiment in relation to the anthropomorphism of the prosthesis, in the Two different groups of healthy subjects were enrolled as controls for the two experiments. Thirty-six participants  performed the VTI task and 11 participants  performed the TOJ. This research was approved by the local Ethics Committee of the Department of Psychology, University of Milano-Bicocca.As mentioned in the previous paragraph, experiments were designed to deliver sensory stimuli on the fingertips of both hands’ index finger and were of two types:(i)ad hoc built relay box . Healthy participants, used as control groups, were always stimulated non-invasively. Non-invasive stimulation was also always used for stimulating amputee healthy right upper limb, as well as in PRE and POST_NI to stimulate her severed left limb. This was achieved by placing the tapper right upon the area of the skin that elicited, in the volunteer, the feeling of being touched on her phantom left index fingertip. The area where the fingers were reported to be was identified with two methods: firstly, by asking the subject where she felt a referred sensation of any digit, and secondly, by touching the skin of the stump until she referred to be touched on that finger. For all the fingers, the maps identified by the two methods were coherent. The skin area marked as digit II was employed as stimulation point. An inactive tapper was placed upon the prosthesis index finger to emulate the one placed on the healthy hand and to reinforce the perception in the volunteer that the stimulation was coming from the index finger on both sides.Non-invasive tactile stimuli, administered by means of solenoid tappers (magnetic rod diameter: 4 mm) controlled by an (ii)POST_I to stimulate the amputee participant severed left limb, employing parameters that induced a sensation that she referred to be closer to the one evoked by the mechanical tapper, in terms of intensity, modality, and referred territory. Indeed, after electrode implantation, a whole-contact psychophysical sensory mapping was performed to establish the match between stimulated contact, modality amplitude, and referred territory of the evoked sensation. Moreover, sensory mapping has been tested and retested day after day before PRE to have an estimation of the day-by-day reliability of the evoked sensations. The neural electrodes elicited sensations that the participant referred to 13 different locations of the anterior and the posterior parts of the phantom hand. The contacts that were used for the real-time closed-loop control of the prosthesis were chosen because they: (i) did not evoke muscle twitch at the beginning of the test period and (ii) changed over time the induced sensation from eliciting the sensation of movement to the sense of touch. In both tasks, invasive stimulation was delivered in the form of square pulse stimuli to the channel number 12 of the intraneural electrode (ds-FILE) positioned proximally within the median nerve  . Stimulation parameters and channel were selected on the basis of the sensations reported by the participant in multiple assessment tests. For a comprehensive description of the features, stability, and location of referred sensations, please see Intraneural invasive stimulation was exploited in In this experiment, a VTI task was carried out to assess the extent of the peripersonal space (PPS) in different conditions and at different time points.The participant was seated beside the wall with her tested upper limb resting on a table in a prone position . A PC-drA and prosthesis B , and the bare stump. The participant’s elbow was always kept at 42 cm from the limit of the projecting area. This resulted in a distance from the projecting area of 30 cm from the endpoint of the stump, while in all the other conditions, the extremity of the limb or the prosthesis was in contact with the limit of the projecting area.On all testing sessions, the space of VTI was tested for the participant’s right healthy limb, the severed left limb wearing prosthesis Along with the visual stimulus, in 85% of the trials, a tactile stimulus was randomly delivered when the visual approaching stimulus was at one of six distance points  from the end of the projecting area. The participant was asked to press a pedal as soon as she perceived the tactile stimulus. The remaining 15% of the trials were “catch trials,” where no tactile stimulus was administered and no response was expected.The task lasted approximately 8 min, consisting in a total of 168 trials  and 800 ms of inter-trial interval. A small training phase with 15 stimuli presentation preceded the experimental task.Distance was computed as a continuous variable.Data were analyzed by addressing the three experimental questions defined in the section “Introduction.” Analysis was implemented in order to weigh different factors, while minimizing the total number of comparisons. The dependent variable which was taken into consideration was the RT in response to the tactile stimulus . The fac•non-invasive vs intraneural), we implemented a linear mixed model (LMM). The model was analyzed with an analysis of variance (ANOVA) with Satterthwaite approximation for degrees of freedom. We included as predictors the continuous factor Distance and the dichotomous factor Stimulation . The number of the trial was added as a random effect variable. The two levels of factor Stimulation have been implemented by pooling together all the conditions exploiting intraneural interface at POST_I  tested against the same conditions recorded at POST_NI (level non-invasive). To maintain homogeneity of samples, data from PRE were not included because they lack prosthesis B. Before being available to perform the non-invasive stimulation session, the participant had to go through all the activities linked with presurgical exams, surgery for electrode removal, and recovery after the surgery. Thus, POST_NI session was performed 22 days after the POST_I session. The time spent training with the prostheses between POST_I and POST_NI was marginal because the participant was involved in perioperative procedures; thus, the impact of any additional training between the two sessions should be considered negligible.STIMULATION. In order to assess the relative weight of the stimulation interface  and Time (PRE vs POST_NI). We have chosen these time points to avoid introducing noise in the data due to different types of stimulation . Additionally, in order to highlight the effects of training on the performance of the task, we conducted two independent ANOVA with the predictors Distance and Time (PRE vs POST_NI).TRAINING. In order to assess the relative weight of the training to control the prosthesis, we used the same approach of stimulation analysis. We employed a LMM, analyzed with an ANOVA with Satterthwaite approximation for degrees of freedom. The number of the trial was added as a random effect variable. Predictors were the continuous variable •Distance and Hand . The three levels of factor Hand have been implemented with data recorded at POST_I. POST_I has been chosen because it was the only time point when prosthesis feedback could have been done through intraneural stimulation, and because the proficiency with the prosthesis was already achieved. Additionally, we ran an analysis to evaluate the performance in the spatial transition from peripersonal to extrapersonal space. We selected the distances of 15 (near) and 45 (far) cm. When the participant was tested on the stump condition, thus not wearing any kind of prosthesis, the tip of the stump was 30 cm away of the tip of the prosthesis . By doing so, the same distances resulted in a 45 (near) and 75 (far) cm away, although the physical positions of both the visual stimulus and stump were exactly the same in both conditions. Thus, we adopted a LMM, analyzed with an ANOVA with Satterthwaite approximation for degrees of freedom including the number of trials as a random effect variable. The predictors were the Distance (near vs far) and Hand (prosthesis A vs stump).PROSTHESIS. In order to assess the relative weight of the employed prostheses, and whether they were embodied, we again employed a LMM, analyzed with an ANOVA with Satterthwaite approximation for degrees of freedom. The number of the trial was added as a random effect variable. Predictors were In this experiment, a TOJ of two stimuli delivered on the upper limb was carried out to investigate the participant’s body awareness in different conditions and at different time points.vice versa.The participant was seated in front of a table, with both her upper limbs lying on its surface in a prone position . Two tacEach trial started with a visual cue (100 ms red LED light) which was followed, after 300 ms, by two tactile stimuli, delivered one to each limb. Before the experiment started, two colored stickers were applied to the participant’s arms (the association between color and arms varied across the testing conditions) and they were used as a code to indicate the stimulated limb without referring to laterality tags (left/right).Each experimental condition was tested with eight experimental blocks, four while the subject’s hands were uncrossed (with a gap of 40 cm between her hands) and the other four when her hands were crossed. In the crossed conditions, in half of these blocks, the right limb was kept over the left limb and in the other half, the left limb was kept over the right limb. In four experimental blocks, the participant was asked to verbally report whether the first of the two stimuli was administered on the right or the left limb, while in the other four, on the contrary, she had to report where the second stimulus occurred.POST_I, the task was repeated twice, using either prosthesis A or prosthesis B.Testing each condition, consisting in a total of 200 trials (20 repetitions per SOA) for the uncrossed limbs and the same amount of trials for the crossed limbs, lasted approximately 35 min. In stimulus onset asynchrony (SOA)” as independent variable (x-axis) and “the probability to judge the right limb as the one firstly stimulated” as the dependent variable (y-axis). Then, data distribution was fitted with a psychophysics sigmoid function:The order judgment of the subject in each condition was plotted with the different “where the two parameters PSS and EA represent:Point of subjective simultaneity (PSS):p = 0.5) to be felt on the right and on the left limb. It testifies the laterality stimulation bias measured in milliseconds.This is the SOA value on the curve where the first stimulus had the same probability (Esteem accuracy (EA):p = 0.5) to reach the value p = 1. It is the inverse of the slope of the curve multiplied by 0.5. The shorter it is, the more accurate the esteem.This is the SOA needed for the line tangent to the curve at . The control group must have done exactly the same task of the single case participant, then a Student-t distribution was adopted for matching the participant’s performance. Under simulations, the method proved to reliably keep under control the alpha error probability to the nominal value of.05  were compared through their PSS in the uncrossed condition. The uncrossed condition is the standard situation of the TOJ task, without conflicting information about laterality, thus results in the highest accuracy. Therefore, this is the best condition to evaluate any laterality temporal bias which may be only due to the type of stimulation.STIMULATION: Pooling together the bare stump, prosthesis •PRE session, the participant was extremely inaccurate and variable in performing the crossed condition, so data have not been analyzed.TRAINING: In •POST_I the worsening of Esteem Accuracy due to hand crossing, while the participant was wearing prosthesis A or prosthesis B.PROSTHESIS: To assess embodiment of prostheses with intraneural sensory feedback, we evaluated in Distance . Critically, the participant accomplished the task differently according to the type of somatosensory stimulation, as shown by the main effect of Stimulation . Moreover, the type of stimulation affected the pattern of the RT/distance curve, as shown by the interaction Distance × Stimulation    suggests that the participant behaved differently from the control healthy group. The same remarkable difference was not observed when the participant was stimulated invasively  = 3.3954, p = 0.066], showing a behavior similar to that of the control group  = 2.582, p = 0.027, Z-CC = 2.69 CI ; noninvasive/non-invasive vs control: t(10) = 0.946, p = 0.336)].In the TOJ with uncrossed limbs, when the right limb was stimulated non-invasively and the left invasively, there was a significant right laterality bias, so that the participant perceived intraneural left stimulation with about 30 ms delay compared to the healthy limb . PSS was significantly different than the one of the control group only when the left limb was stimulated invasively , Distance × Time  and Distance × Hand × Time  interactions. The training induced in the healthy limb a general speeding up of RTs for all distances, as suggested by the presence of main effect of Time , but it did not change the pattern of the RT/distance; absence of Distance × Time interaction . Conversely, testing the prosthesis, the training changed the pattern of the RT/distance curve as shown by the presence of interaction Distance × Time , besides the main effects of Distance  and Time . Change of pattern, with a decrease of RTs in far space, going from 60 to 90 cm, suggested an extension of the PPS and it is in favor of a positive effect of training on the embodiment of the prosthesis  and data have not been analyzed.Hand . The direct comparison between the levels of the factor Hand showed that the healthy limb was different from both the prosthesis A (p < 0.001) and the prosthesis B (p < 0.001), while prosthesis A did not differ from prosthesis B (p = 0.428). Despite the different average RT, the two prostheses and the healthy limb did not show a statistically different pattern of response across the distances  = 1.180, p = 0.309), suggesting that the stimuli approaching the prosthesis were processed similarly to those approaching the healthy limb. This is in favor of an embodiment of both prostheses  × Hand (stump vs prosthesis) interaction .Moreover, VTI gave an additional cue of prosthesis embodiment. The first and third distances, i.e., 15 and 45 cm from the prosthesis respectively corresponded to 45 and 75 cm from the stump, because the tip of the stump was 30 cm shorter than the tip of the prosthesis. Considering the bare stump, the shift from PPS to extrapersonal space would likely fall within a 45–75 cm range , so thatSo far, the VTI task showed that both prostheses tested with intraneural stimulation behaved as the healthy limb, thus suggesting their embodiment, while the TOJ gave contrasting results.A (EA: 55.9 vs 114.1 ms). Indeed, esteem accuracy with this prosthesis was not significantly different with the ones of the healthy subject control group, both in crossed  and uncrossed hand  conditions. By contrast, the crossed/uncrossed difference was absent for the more-anthropomorphic less-trained prosthesis B (EA: 121.7 vs 113.5 ms), where esteem accuracy was significantly different from controls in the uncrossed hand condition  and not in the crossed hand condition  , was present only for the less-anthropomorphic more-trained prosthesis = 0.653] .This study was designed to investigate the embodiment achieved in a context of ecologic continuative use of a worn and neurally interfaced hand prosthesis. Embodiment was favored by the closure of the sensorimotor control loop of the prosthesis, enabled by a more natural and rich sensory feedback delivered through multichannel neural interface.It has peculiar features compared to the previous studies approaching prosthesis embodiment. We had the opportunity to test our subject longitudinally, before and after a one-month period when she achieved proficiency in controlling the prosthesis. Moreover, the studied subject had a stable chronic amputation; thus, at the time of the study, she was not going through any spontaneous recovery plasticity, and she had not used active prostheses before the study, allowing us to have a clean measure of her baseline prosthesis embodiment at the beginning of the experiment.Amputee subjects carrying intraneural electrodes implantation to receive prosthesis sensory feedback are still rare; thus, single-case studies are worth running to gather clues on the embodiment of neurally interfaced prostheses. To minimize possible biases linked with studying single cases, we decided not to rely on subjective and explicit statements, but to employ two implicit and objective paradigms, to test-retest the subject, and to statistically compare the results from the volunteer with those from a healthy subject control group.The paradigms investigated prosthesis embodiment through the study of multisensory integration and spatial remapping; they were developed in healthy subjects  and demoDuring the period of training, the participant used both prostheses, always receiving a rich tactile feedback related to manipulation activities made with the prosthesis through intraneural and perineural multichannel electrodes. However, when the participant was tested in the multisensory integration tasks, somatosensory stimuli were delivered in different sessions, either with non-invasive mechanical tappers or with invasive intraneural stimulation to highlight possible advantages of the latter on embodiment.Intraneural stimulation did not speed VTI RT, and when TOJ was tested by stimulating the right limb non-invasively and the left limb invasively, there was a laterality bias toward the healthy right limb. Indeed, intraneural stimulation had to be delivered about 30 ms before the contralateral to have the same probability to be felt as the first. Since electrodes are implanted in the median nerve of the arm and since intraneural stimulation does not need mechanoreceptor transduction, the longer duration of the tapper stimulation (40 ms vs 200 μs) has likely hidden the perception of the intraneural stimulation.Being the electrode implanted only in the left limb, we could not test TOJ performed with both sides stimulated intraneurally and demonstrate any improvement of performance due to intraneural stimulation. However, we could show that when stimulation was delivered non-invasively on the right and invasively on the left limb, TOJ performance did not become worse, despite the asymmetry of stimulation.Even more importantly, VTI showed that with intraneural stimulation the RT/Distance pattern of the amputee wearing the prosthesis was more similar to the one of the healthy subject control group. This normalization of the relation between multisensory integration and distance from the body is in favor of a selective advantage of intraneural stimulation on prosthesis embodiment.To the best of our knowledge, this is the first study that demonstrated in an amputee that training the control of the prosthesis favors the process of embodiment by testing-retesting the participant before and after the training period. To avoid any bias due to different stimulation, the effect of training was always tested with the same stimulation, i.e., non-invasively.During the month of training, the participant was often involved in performing skilled bimanual tasks; thus, both limbs were trained. When the effect of training on the PPS expansion was assessed through the VTI task, we found a differential effect depending on the tested limb. The healthy limb showed an overall increase of performance, boosting up the effect of incoming visual stimuli on touch detection, thus decreasing RTs at all distances of the visual approaching stimulus. Notably, the affected limb showed a modification of the RT/Distance pattern, with a clear extension of the PPS, which is a strong clue of extracorporeal device embodiment . From thIn the VTI, prostheses recalibrated the PPS around the subject, as shown by the shift of the PPS/Extrapersonal boundary. Indeed, when the subject was tested with the bare stump, the boundary distanced between 30 and 60 cm from the stump, and when the subject was tested wearing the prosthesis, the boundary distanced more than 60 cm. This was in line with the previously demonstrated partial recovery of PPS shrank by the amputation . MoreoveRecently, it has been shown that even prostheses unable to give any sensory feedback were able to induce the crossing hand effect, typical of healthy limbs . It is wNotably, previous studies that reported the hand crossing effect on TOJ, always stimulated the tip of the hand, drumstick, or prosthesis, which could be relocated in the contralateral space quite far from the midsagittal plane in the limb/tool crossing. By contrast, intraneural stimulation was delivered to our participant through electrodes placed on the median portion of the arm, which even in the crossed condition remained in the same side of the space, as if electrodes were uncrossed. Thus, in our case, we had a dissociation between the side where the stimulation was physically delivered and the side where it was referred. Performance worsening occurred with uncrossed real stimulation which was perceived as crossed only because of the sensation remapping, emphasizing the link between prosthesis embodiment and hand crossing effect.Both VTI and TOJ crossing hand effect are based upon multisensory integration. Somatosensory feedbacks, such as touch, proprioception, and the efference copy are coded in an egocentric reference frame, where they are compared to “where I am.” Conversely, environmental feedbacks, such as sight and hearing, are coded in an allocentric reference frame, i.e., where the information is compared to the rest of the environment. To integrate information that does not share the same reference frame, a process of remapping is needed.In sensory remapping, a sensory modality is not just recoded in the frame of another modality. Instead, it is recoded in a new space that mixes the spaces of the two modalities using weights in line with a policy based on Bayesian integration  such as The knowledge of where the environment is in relation to the hand and the awareness of what the hand movement will affect are both needed to achieve an effective hand-environment interaction. Thus, a close sensorimotor control loop involves a double transformation: along the afferent branch, environmental information must be re-referenced in a bodily frame, while in the efferent branch, the knowledge of the body coordinates must be re-referenced in the allocentric frame of the environment.In VTI and related acoustic-tactile integration tasks, environmental inputs collected by sight or hearing assume different valence, and are able to enhance the effect of touch, depending on where they are in respect to the body . This prPPS is the space around the body where external events are considered more relevant. It has been suggested that we have a motor-based PPS that has the higher possibility to be directly acted upon by the body, and a defense-based PPS where external stimuli may be more effective upon the body .In our VTI task, we tested fast approaching stimuli. Looming stimuli are potentially more dangerous than static, so that they induce a protection response with enhanced tactile sensitivity in their predicted time and site of impact . It’s liIn the uncrossed hand condition of the TOJ task, egocentric and allocentric reference frames are concordant and somatosensory stimuli are analyzed in their original bodily referenced frame. On the contrary, with crossed hands, the delivered somatosensory stimuli assume a different value depending on where they occur in the egocentric space; thus, the judgment subtends a compulsory remapping of the location of tactile stimuli in external world coordinates . AccordiDespite TOJ being a tactile task, several cues are in favor of a motor-based origin of the transformation it tests, because external spatial coordinates allow the movement toward the tactile event. Indeed, TOJ is modulated by hand movements, which are able to compress time interval . The croad hoc developed EMG control and received tactile feedback from the prostheses related to dexterous manipulation and slippage through invasive nerve stimulation , and this may be the reason of a different embodiment of the two prostheses according to the TOJ. Only the more trained prosthesis, despite being less anthropomorphic, was able to trigger the bodily-into-environment remapping and to induce the hand crossing effect.The time our volunteer spent in training skilled manipulation with each of two prostheses was very different  for the publication of any potentially identifiable images or data included in this article.GD, DR, and AMa designed the study, analyzed the data, and wrote the manuscript. CS, AMi, and MD’A performed the study, analyzed the data, and wrote the manuscript. VDe performed the surgery and collaborated during the experiments. VDi designed the study and collaborated during the experiments. RS and LZ collaborated during the experiments. EG supervised the experiments. All authors discussed the results and commented on the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In recent years, the generative adversarial network (GAN)-based image translation model has achieved great success in image synthesis, image inpainting, image super-resolution, and other tasks. However, the images generated by these models often have problems such as insufficient details and low quality. Especially for the task of map generation, the generated electronic map cannot achieve effects comparable to industrial production in terms of accuracy and aesthetics. This paper proposes a model called Map Generative Adversarial Networks (MapGAN) for generating multitype electronic maps accurately and quickly based on both remote sensing images and render matrices. MapGAN improves the generator architecture of Pix2pixHD and adds a classifier to enhance the model, enabling it to learn the characteristics and style differences of different types of maps. Using the datasets of Google Maps, Baidu maps, and Map World maps, we compare MapGAN with some recent image translation models in the fields of one-to-one map generation and one-to-many domain map generation. The results show that the quality of the electronic maps generated by MapGAN is optimal in terms of both intuitive vision and classic evaluation indicators. In modern society, the electronic map plays an important role in daily travel navigation, geographic information query, and other services. However, there are still some blind spots in the coverage of electronic maps (such as some remote areas), which to some extent, limits the service level of geographic information data for users and the guidance level for socioeconomic and political purposes. At the same time, the production of electronic maps generally requires vectorization of paper maps first and then involves complex graphic editing manually by industry standards, which consumes a lot of manpower and resources. Inspired by Liao Ke’s work , in viewHowever, using deep generation models to translate remote sensing images to electronic maps involves many difficulties. First, although the image generated by the existing deep generation model obtains good results at a low frequency, the texture is unclear, and the gradient of adjacent pixels is small at a high frequency. Second, electronic maps require continuous roads. However, in remote sensing images, roads may be blocked by other features (such as woodlands and houses) and not shown completely. Network models need to learn to ignore these obstructions and show the blocked part of the road. Third, to produce a standard electronic map, the network model needs to render different elements with correct colors and learn to align the boundaries of some color blocks for the sake of visual aesthetics, which requires the model to have a higher semantic understanding of images.Some former researchers have experimented with their proposed model on the translation task using remote sensing images and Google maps. However, the purpose of the previous researchers’ map translation task was to prove the feasibility of their proposed model. As long as the generated images were in the style of electronic maps, they were satisfactory because this proved that their model had image translation capabilities, although the quality of generated maps was not high, and it was easy to distinguish between the real electronic maps and the generated electronic maps. For example, Pix2pix  establisThis paper proposes a GAN-based generation model, Map Generative Adversarial Networks (MapGAN), for generating multitype electronic maps from remote sensing images and rendering matrices of special feature elements at the same time. MapGAN improves the generator of Pix2pixHD  and addsA deep generation model is a parametric generation model that uses deep neural network technology to learn the probability distribution of given sample data to generate new data. The deep neural network gives the generative model a stronger learning ability through the complex combination of multilayer perceptrons. In particular, the introduction of convolution technology has greatly improved the ability of the generative model in image processing and image generation. As the predecessor of the deep generation model, the Restricted Boltzmann Machines (RBM)  has a reIn 2014, Goodfellow  proposedImage translation refers to the processing of the images in domain A to output the corresponding images in domain B. Many problems in the field of computer vision can be attributed to image translation. For example, the problem of image super-resolution can be seen as the translation from low-resolution images to high-resolution images, and image colorization can be seen as the translation from single-channel grayscale images to multichannel color images. There are two ways to implement image translation: supervised learning and unsupervised learning. Given two edge probability distributions, there are infinite kinds of joint probability distributions that meet the requirements, so the unsupervised image translation problem is relatively difficult to implement. Nevertheless, since it is easier to find a single type of dataset in practice, the application of unsupervised image translation models is more convenient.Although convolutional neural networks have achieved amazing results in solving image translation tasks of painted art, there are still problems such as the low quality of generated images and complex loss function design. Until the emergence of GANs, a new method was sought for the task of image translation. The GAN solved the problems of the convolutional neural network by training a discriminator to learn the loss function automatically, and this generated higher quality images. In recent years, many image translation models have been developed based on GANs. Based on the CGAN, Pix2pix’s  generatoMost image translation models are used to solve specific problems. For some universal translation models, such as Pix2pix and CycleGAN, when applied to map generation tasks, the generated electronic maps cannot accurately identify and render elements such as woodland, waters, and roads. At the same time, there are problems such as blurred texture and low quality. In contrast, MapGAN builds the model from the specific scene of the electronic map generation and uses some targeted optimization measures to improve the accuracy and aesthetics of the generated maps.In this section, we first discuss the reason for and method of using auxiliary information called render matrices to control color rendering in the task of generating electronic maps, then we describe our proposed MapGAN, a framework to generate multitype electronic maps based on remote sensing images and a render matrix.In each type of electronic map, different colors represent different types of features. Whether the color rendering is correct is an important aspect for evaluation of the quality of the generated electronic map. However, there are many difficulties for the model in learning how to correctly render the generated electronic map, as shown below: (1) Standard color rendering of an electronic map needs to refer to various aspects of the map’s attribute information. For example, the highway is rendered as orange based on the geographic feature information extracted from the geographic entity database. Unfortunately, such attribute information cannot be extracted from remote sensing images by neural networks. (2) The model needs to consider aesthetics when rendering the generated electronic map colors. For example, sometimes to improve the aesthetics of the map, the standard electronic map production process renders green spaces into standard squares, although this may include some nongreen space components, as shown in To address this problem, we used auxiliary information called render matrices to control the color rendering that needs to consider the properties of geographical entities and aesthetics. A render matrix is a two-dimensional matrix with the same width and height as the remote sensing image in the model input. Each render matrix contains the rendering information of a certain feature in various electronic maps to be generated, and this information is encoded and stored in each matrix element. The rendering information stored by the matrix elements is divided into three categories: Category 1 represents that the corresponding position in all types of generated electronic maps does not need to render this type of feature. Category 2 represents that the corresponding position in a certain type of electronic map needs to render this type of feature. Category 3 represents that the corresponding position in several types of electronic maps needs to render this type of feature. For example, when generating the two types of electronic maps, map A and map B, based on a remote sensing image, the correct color rendering of high-speed roads needs to refer to the third-party road network information, which cannot be directly obtained from the remote sensing image, so we used a render matrix as auxiliary information in the model input. The specific coding and meaning of the matrix element are shown in Our goal was to train the generator To help MapGAN to learn the meaning of the render matrix and the characteristics and differences of different types of electronic maps more quickly and efficiently, we proposed the following loss terms to form the final loss function of MapGAN:Feature Render Loss. Note that we can use a binary graph In Formula (1), Map Classification Loss. The discriminator of the generated network can only guarantee the reality of the generated image; that is, it can only ensure that the generated image is an electronic map, but in the task of generating multiple types of electronic maps, the category accuracy of the generated electronic map cannot be guaranteed. To address this problem, MapGAN judges the type of the generated map by adding a pretrained map classifier In Formula (2), AdaptivePerceptualLoss. The map generation discussed in this paper can be seen as a special style transfer. In this study, the content image was the remote sensing image, and the style image was the electronic map. The purpose of map generation was to retain certain content of the remote sensing image and convert it into the network electronic map style image. Gatys [e. Gatys  designede. Gatys  used simIn Formula (3), In Formulas (4), (5), and (6), Adaptive Adversarial Loss. Adversarial training is widely used in image translation models. During the training process, the discriminator is used to automatically learn the loss function to guide the generator to generate more realistic images. However, Mao [ver, Mao  pointed In Formulas (7) and (8), Full Objective. Combining the above four loss terms, the objective functions to optimize Generator. The generator architecture of the MapGAN model is based on the network model used in Pix2pixHD. It consists of three parts: the downsampling layer, the residual block [al block , and theDiscriminator. Adapted from Pix2pix [ Pix2pix , the disClassifier. In this study, we used the Xception model as our classifier, which is an improvement on the Inception v3 model. It uses depthwise separable convolution to replace the replacement convolution operation, thereby improving the model’s effect without increasing the network complexity.We first tested MapGAN in the one-to-one domain map generation scenario of generating Google electronic maps from Google remote sensing images. In this section, we also show the empirical results of MapGAN in the one-to-many domain map generation scenario that generates Baidu and Map World electronic maps from Baidu remote sensing images at the same time. In both map generation experiments, we conducted relevant ablation studies and compared results with the state-of-art model in terms of visual and quantitative evaluation.As our baseline models, we used Pix2pix , a mode Pix2pix is a general-purpose image translation architecture based on conditional adversarial networks that is trained by supervised learning. Since Pix2pix was proposed, a large number of improved image translation models have been proposed, However, due to the versatility and canonicity of Pix2pix, we still used it as a baseline model in this study.An MSGAN, mode seeking generative adversarial network is equivalent to adding a mode seeking regularization term based on the CGAN to improve the stability and diversity of model training.BicycleGAN is a one-to-many domain image translation model. BicycleGAN’s innovation lies in ensuring the bijective consistency between the latent code and the output mode, thus preventing the problem of mode collapse and generating better results.StarGAN is a novel model that can perform image-to-image translations for multiple domains using only a single model. By using a domain label and mask vector, StarGAN allows simultaneous training of multiple datasets with different domains within a single network, which leads to StarGAN’s superior quality of translated images compared to existing models.The Google Maps dataset was made from satellite and electronic map tiles from Google maps, with image sizes of 600 × 600 pixels . The traThe Baidu and Map World Maps dataset was made by downloading Baidu and Map World electronic maps and remote sensing images of the corresponding areas using BIGE MAP software. The size of each image was 256 × 256 pixels. The training set contained 1500 training samples, and the test set contained 500 training samples. Each training sample contained a remote sensing image and a Baidu and Map World electronic map of the same area. We used this database in the one-to-many domain map generation experiment. Two sample examples are shown in Given the unimodal nature of the target translation function in the task of generating electronic maps, we mainly used the average pixel translation accuracy to evaluate the performance of the model in map translation tasks. For a single pixel, if the difference in pixel values is less than 16, the translation result of the pixel is correct; otherwise, the translation result of the pixel is incorrect. The percentage of correctly translated pixels in a remote sensing image is the accuracy of the model in a map translation task. To evaluate the image quality of model generation comprehensively, we used Kernel Maximum Mean Discrepancy (Kernel MMD), Fréchet Inception Distance (FID) , mode scMapGAN used some of the model settings recommended by the DCGAN . The outThe experiment was carried out on a workstation with 1 NVIDIA M40 GPU, 4 Inter Xeon Platinum 8163@2.5ghz CPUs, and 30 GB of RAM. It took about three hours to train the model in our one-to-one domain map generation task and five hours in the one-to-many domain map generation task.In this experiment, since our goal was to generate only one kind of electronic map, the classifier was not used in the training process, and we expressed each render matrix as a binary map. The features represented in the binary map were water, woodland, and highway roads.To construct the feature loss term more accurately, we explored and selected the reasonable feature extraction network layer when constructing the We explored the impact of different weight settings of loss terms on the accuracy of the model map translation. For the convenience of description, the symbol We investigated the influence of three binary graph channels in the model input on the color rendering of the generated electronic map. The three binary graph channels were woodland area binary graph, water land area binary graph, and highway area binary graph. In the formal training of MapGAN, the binary graphs of three channels corresponded to the remote sensing images one by one; however, this experiment broke this correspondence and used a random combination to construct the model input to explore the influence of three binary graph channels on the color rendering of the generated electronic map. The results are shown in We used the optimal MapGAN model developed by a large number of experiments to generate tile maps in this experiment and compared the quality of generated maps with some recent image translation models using the same test set. To test the effect of the render matrix on the model generation effect, we removed the render matrix from the model input and evaluated it again. In this experiment, we used the same parameter settings as in We compared the Baidu and Map World electronic maps generated by MapGAN and StarGAN. Note that the reason why we chose the unsupervised StarGAN as the comparison model is because almost all the one-to-many domain adversarial generation models are unsupervised types, and StarGAN is the most suitable one. The results are shown in We used a classifier to help the model generate multiple maps of the correct type when training the model. To test the influence of the classifier on MapGAN, we removed it from the model and trained it again. The model test results are shown in The research results in this paper mainly propose a MapGAN model that can generate kinds of realistic electronic maps based on remote sensing images and render matrices at the same time. The model uses render matrices to control the generation and color rendering of special feature elements. We also used a classifier and map classification loss term to give the model the ability to generate multiple types of maps. Experiments show that each type of electronic map generated by MapGAN exceeds the existing image translation models under many evaluation indicators including pixel-level translation accuracy, Kernel MMD, FID, mode score, and inception score. However, due to the difficulty of obtaining trainable datasets, this study only tested the model performance when generating two types of maps, and it was not known how many electronic maps a model could simultaneously generate at most. If an electronic map with many features needs to refer to third-party database information for color rendering, multiple render matrices need to be created, but considering that most of the elements of the render matrix in the model input are 0, there exists the problem of memory resource waste to some extent, which slows down the training speed of the model. In the next step, creating more trainable datasets to test our model and finding a new encoding method for the render matrix will be the direction and focus of research.


Robotics is one of the key learnings in a world where learners will interact with multiple robotic technologies and operating systems throughout their lives. However, school teachers, especially in the elementary and primary education stages, often have difficulties incorporating these tools in the classroom. Four elementary teachers in three schools in Catalonia were trained to introduce robotics in the classroom to seventy-five students. The main actions consisted in classroom accompaniment by a university-trained support teacher, curricular materials’ development, and assessment of the students’ and teachers’ learning. The designed contents and evaluation criteria took into account the potential of educational robotics to improve soft skills and to promote Science, Technology, Engineering, Arts, and Mathematics (STEAM) interdisciplinary learning. Teachers perceived the training to be supportive and useful and ended the school year feeling confident with the used robotic platform (KIBO). The assessment of the students’ learning showed an average mark of 7.1–7.7 over 10 in the final evaluation criteria. Moreover, students’ learning was higher in the classes where the teachers had higher initial interest in the training. We present and analyse the actions carried out, with a critical and constructive look at extending the experience to other educational centers. Learning robotics forms part of the core learning skills in a world where students have to engage with multiple robotic technologies and operating systems throughout their lives ,2. In a Previous studies have shown that students have rich learning experiences in robotics with a learn-by-doing approach, constructing their own robots and programming them with scaffolds ,11, withEarly childhood is a critical developmental period for learning and therefore an excellent opportunity to incorporate robotics and other new technologies into curricular activities . It can Teachers need a deep understanding of computer science concepts and pedagogical practices to be able to design robotic experiences and assessments that truly enable students to acquire computational thinking and the development of soft-skills . TraditiElementary teachers, with the challenge of being knowledgeable in many subjects and with a general lack of an adequate technological training during the course of their teacher education program, do not usually have genuine knowledge of technological and robotic tools . An inadLa Salle Ramon Llull University in partnership with Pedagogia La Salle Catalunya has recently started EduEnginy, a project that promotes computational and engineering thinking, problem-solving, and STEAM through robotic technology. In the framework of the EduEnginy project, a roadmap has been created to ensure that robotics is adopted in compulsory education curricula in all the schools of La Salle in Catalonia before 2021. The main actions consist in classroom accompaniment and development of curricular materials. It benefits from the experience gained in the pilot test during the 2016/2017 school year . The objhttps://kinderlabrobotics.com/kibo/). Each school acquired 1 to 4 units of this educational robot, which were then used in groups of 4 to 15 students. KIBO . They were accompanied throughout the course in 2017/2018 while teaching to 5 different groups of 15 students (65 children 4–6 years old). After an initial meeting and presentation with all the elementary stage coordinators of all schools of La Salle Catalonia, the management teams of various schools asked to receive the training, motivated but not obliged by the pedagogy team of these schools’ network.The teacher training was structured according to the phases proposed in After a curriculum adaptation to the school idiosyncrasy, sixteen 45–60 min long sessions were designed for a period of 8 months starting in fall. The training started with a three-day teacher professional development workshop in the summer before the implementation, which covered the essentials of robotics in early education. It followed a two-hour workshop in each school focused on the presentation of the curriculum and on an initial questionnaire to the teachers . During In summary, the training of this study was designed to allow early childhood teachers to learn-by-doing, that is, to learn about robotics while teaching their students. It favoured the acquisition of technological skills when the teacher truly needed them (while teaching), avoiding standalone courses that divide between how and when skills are learned. This approach is supported by the TPCK  model as a framework for good teachers in the 21st century [While training the elementary school teachers, a curriculum was designed adapted to the needs of the educational stage. The designed curriculum consisted in 16 sessions lasting between 45 and 60 min, organised around computer science concepts introduced along the way, such as instruction, sequence, iteration, and conditional. Moreover, each of the sixteen sessions had activities related to interdisciplinary STEAM learning and were closely inspired by the results in the previous pilot experience  and by sThe use of KIBO robots in the curriculum not only included the technology but also integrated it into the teaching target content. As such, robotics was used for interdisciplinary learning in all the STEAM subjects: science , technology (sessions 1 through 15), engineering , arts , and mathematics (sessions 7 and 8). Moreover, the curriculum was developed using the Positive Technological Development (PTD) framework  recommenThe sessions were taught to classes of 15 students, following a similar structure in all of them: (1) introduction or reinforcement of concepts, (2) challenge with the robot, (3) technology circle to reflect on the learning received, and (4) free exploration. The challenges with the robot were performed by dividing the students into heterogeneous groups of 4, with interchangeable roles of “builders”, “programmers”, and “responsible for the pieces”. The titles and short descriptions of the sessions of the developed curriculum are presented in the The training for the schools has been evaluated with a mixed-method research approach. This method is based on a pragmatic paradigm that contemplates the combination of quantitative and qualitative items to evaluate teachers’ and students’ learning. It is is suitable for studies for small samples (less than 10), such as ours, and has successfully been applied in previous studies in educational technology-rich contexts . The quaItems to evaluate the teachers’ learning: Questionnaires for the teachers at the beginning and at the end of the training  , derivedIn summary, we have used the following items:The evaluation criteria used in this study relate closely to the evaluation criteria, the so-called capacities, established by the local Catalan curriculum of elementary schools : learn thttp://www.torreforta.lasalle.cat/la-robotica-ja-forma-part-de-les-activitats-curriculars-dels-nens-i-nenes-de-p5-de-la-salle-torreforta/, https://figueres.lasalle.cat/programes/). Moreover, the attitude of the management team from each school was decisive for the training. The initial interest of the teachers responded closely to the positive attitude of the director of the school regarding the promotion of robotics and STEAM. This finding agrees with Thibaut et al. [Taking as reference the interaction of the school teachers with the support teacher during the training, school teachers proved to be communicative, especially in the face-to-face sessions. Stage coordinators adopted a very supportive attitude as well, building bridges with the management team and actively intervening in the dissemination of the experience in different media . A better explanation of the expectations of the students’ expected learning at the initial meeting with the school could improve this score. Finally, we noticed that the training partly made teachers rethink their teaching practice (3.00/5). Two factors may affect this latter value: (i) it is the first time that the teachers of our study received a training like this and we are aware that it takes time to reflect on a change in their modus operandi, and (ii) the current educational model does not generally favour temporary spaces of reflection to cause significant changes in the teaching practices.Three teachers out of four designed a final extra session with the robot. The session they designed not only included the technology but also integrated it into the target teaching content. Their designed sessions, though, were efficient but not disruptive; the training was rather a tool to empower them, to become familiar with a methodology to teach robotics according to well-researched methodologies, and to increase their confidence in integrating robotics into their day-to-day practice.In summary, the school teachers rated having support from EduEnginy and La Salle Ramon Llull University as a valuable factor and started to integrate robotic technologies in their educational practice. These positive results agree with studies which show advantages of using teacher partnerships in the classroom ,16,23. TEvaluation of students’ learning is based on the data collected in five groups of fifteen 4–6-year-old students from three different schools in Catalonia and four teachers (N = 75). The data originated from three control points and used the PTD evaluation criteria as described in On the other hand, the results show differences of up to two points out of ten in the average of the final assessments of teachers  and diffThe pseudocolor plot of the correlation coefficients  indicateFinally, various important observations were collected from interviews with teachers: (i) robotics helped all the students in their problem-solving abilities; (ii) small groups of 15 students were more suitable than larger groups of 30 students during the robotic session; (iii) psycho-motive and assembly-related activities were successful among the students; (iv) activities involving repeating patterns presented difficulty; (v) collaborative learning was complex to implement since some students monopolised the robot; and (vi) it was complicated for the teachers to assess the initial level of “content creation” evaluation criterion at the beginning of the training. The latter could partly explain the differences of learning increase between Q1 and Q3 students.The increase of problem-solving skills observed in the students is aligned with findings from prior research indicating that the use of robotics can lead to improvements in problem-solving skills ,18.The methodology of teacher training to introduce educational robotics at an early age presented in this study is solid enough to reproduce it in other schools. It has confirmed the good results obtained in the pilot test  and has Keeping in mind the learned experience and the analysis of the results, we suggest two main areas of improvement: improvement in the methodology of training for teachers and improvement in the pedagogical practices with students. Regarding the methodology of teacher training, we suggest allocating more resources to teachers with lower programming abilities and higher initial interest. Even though all teachers felt the training was useful and personalised, students tended to learn more with teachers who had higher initial interest in the training. In the face of future interventions, this initial interest should be encouraged or awakened (or by ensuring that those who participate in the project do so on their own initiative without pressure from the management team of the school). We also suggest the incorporation of other tools to assess the teachers’ subjective experience of the training such as BLA (Bipolar Laddering) or IMI (Intrinsic Motivation Inventories). The practice of selecting the best teachers to teach other teachers at the end of the school year was very beneficial; it empowered the more talented and outstanding teachers. We recommend, thus, to extend this practice. The best teachers would be those who have better integrated didactic and methodological aspects but are not always the best programmers.Regarding the pedagogical practices with students, we suggest improving the accuracy of the initial evaluation of the students so that it becomes more objective and to introduce more evaluation points and other evaluation tools such as interviews and video recordings. We are keen to rethink the collaborative learning among students. We propose the creation of “corners” or “learning environments” in the classroom, where the children could work in pairs or individually instead of larger groups. The choice of space by the child is free as well as the manipulation of the objects that make up the space. It would be, therefore, a free open space. The teacher could ask the children questions in order to trigger new discoveries.We have presented the methodology and results of the training for 4 elementary teachers to 3 schools during the 2017/2018 school year to introduce robotics in the classroom. The main actions consisted in classroom accompaniment by a university-trained support teacher, curricular materials’ development, and assessment of the students’ and teachers’ learning.Based on the responses from the questionnaires, the acceptance and usefulness of the training were positive. The teachers felt they had learned about the robot (KIBO) and felt competent to create related didactic units. Their performance within the training was more fruitful for those with more initial interested and with lower initial programming abilities, and it also reflected better evaluation results for the students. The presented training methodology is innovative, especially in a context where teachers receive numerous workshops where they tend to act as passive and nonactive learners. The training gives them the possibility to learn by doing. It also supports the TPCK framework  by promoThe developed curriculum covered ethical and psychosocial components of the cognitive components traditionally used in the evaluation of learning in areas of computer science and technology. The 16 sessions were designed under the PTD framework , promotiThe results of the final assessments of students showed an average mark of 7.1–7.7 over 10 in the final PTD evaluation criteria and an average increase in all the PTD evaluation criteria of 0.1 to 0.3 points. This increase was not uniform for all students: brighter students tended to improve more in collaboration, communication, and creativity, and those with lower marks tended to improve more in conduct and creativity. Differences among teachers were present, underlining the dependence of their initial interest on their students’ learning. These statements should be analysed with caution because of the limited number of students/teachers assessed (75 students and 4 teachers) and of the uneven evolutionary moments of children at these ages (4–6 years old), but they indicate insightful trends.The experience presented here points out interesting guidelines to integrate STEAM learning at an early age with robotic platforms to other schools. Regarding the methodology of teacher training, we suggest allocating more resources in teachers with higher initial interest and to extend the practice of selecting the best teachers to teach other teachers at the end of the school year. The best teachers would be those who have better integrated didactic and methodological aspects but are not always the best programmers. Regarding the pedagogical practices with students, we suggest improving the accuracy of the initial evaluation of the students so that it becomes more objective and to introduce more evaluation points and other evaluation tools such as interviews and video recordings. We also encourage new strategies to promote a more similar learning among students. We propose the creation of “corners” or “learning environments” in the classroom, where the children could work in pairs or individually instead of larger groups.Summarising, it is fascinating to observe how robotics can influence pupils’ learning at early ages, both as a motivating element and as an articulator to promote social STEAM learning.