Desarrollo de la comprensión emocional. ¿Qué tipo de tecnología para qué alumno con trastorno del espectro autista? Revisión sistemática / Development of emotional understanding. What type of technology for which student with autism spectrum disorder? Systematic review

La falta de información sobre el uso de la tecnología en niños con trastorno del espectro autista (TEA) de diferentes perfiles puede dificultar que docentes y alumnos se estén beneficiando del apoyo tecnológico más eficaz y ajustado a sus necesidades. El objetivo de esta revisión fue analizar y sintetizar la evidencia científica sobre la eficacia de los recursos tecnológicos en la mejora de la comprensión emocional de estudiantes con TEA con perfiles de alto y bajo funcionamiento. Para ello se realizó una revisión sistemática de las publicaciones científicas indexadas en algunas de las bases de datos de mayor relevancia siguiendo los criterios establecidos en la declaración PRISMA. En total se analizaron 38 artículos que cumplieron con los criterios de inclusión preestablecidos. Los resultados muestran la importancia de diseñar sistemas versátiles que puedan personalizarse y adaptarse en tiempo real y en contextos naturales con un enfoque claramente inclusivo. Pero también sugieren que la tecnología puede no ser una herramienta de intervención complementaria adecuada para todos los niños con TEA. Lo que subraya la necesidad de ensayos adicionales bien controlados sobre las características que permitan identificar qué estudiantes podrían o no beneficiarse de diferentes modalidades de tecnología. (AU)

The lack of information on the use of technology in children with autism spectrum disorder (ASD) of different profiles can make it difficult for teachers and students to benefit from the most effective technology support tailored to their needs. The aim of this review was to analyze and synthesize scientific evidence on the effectiveness of technological resources in improving the emotional understanding of students with high and low functioning ASD profiles. A systematic review of the scientific publications indexed in some of the most relevant databases was carried out following the criteria established in the PRISMA declaration. A total of 38 articles that met the pre-established inclusion criteria were analyzed. The results show the importance of designing versatile systems that can be customized and adapted in real time and in natural contexts with a clearly inclusive approach. But they also suggest that technology may not be an appropriate complementary intervention tool for all children with ASD. This underlines the need for additional well-controlled tests on the characteristics that would allow identifying which students might or might not benefit from different technology modalities. (AU)

Desarrollo de la comprensión emocional. ¿Qué tipo de tecnología para qué alumno con trastorno del espectro autista? Revisión sistemática / Development of emotional understanding. What type of technology for which student with autism spectrum disorder? Systematic review

La falta de información sobre el uso de la tecnología en niños con trastorno del espectro autista (TEA) de diferentes perfiles puede dificultar que docentes y alumnos se estén beneficiando del apoyo tecnológico más eficaz y ajustado a sus necesidades. El objetivo de esta revisión fue analizar y sintetizar la evidencia científica sobre la eficacia de los recursos tecnológicos en la mejora de la comprensión emocional de estudiantes con TEA con perfiles de alto y bajo funcionamiento. Para ello se realizó una revisión sistemática de las publicaciones científicas indexadas en algunas de las bases de datos de mayor relevancia siguiendo los criterios establecidos en la declaración PRISMA. En total se analizaron 38 artículos que cumplieron con los criterios de inclusión preestablecidos. Los resultados muestran la importancia de diseñar sistemas versátiles que puedan personalizarse y adaptarse en tiempo real y en contextos naturales con un enfoque claramente inclusivo. Pero también sugieren que la tecnología puede no ser una herramienta de intervención complementaria adecuada para todos los niños con TEA. Lo que subraya la necesidad de ensayos adicionales bien controlados sobre las características que permitan identificar qué estudiantes podrían o no beneficiarse de diferentes modalidades de tecnología. (AU)

The lack of information on the use of technology in children with autism spectrum disorder (ASD) of different profiles can make it difficult for teachers and students to benefit from the most effective technology support tailored to their needs. The aim of this review was to analyze and synthesize scientific evidence on the effectiveness of technological resources in improving the emotional understanding of students with high and low functioning ASD profiles. A systematic review of the scientific publications indexed in some of the most relevant databases was carried out following the criteria established in the PRISMA declaration. A total of 38 articles that met the pre-established inclusion criteria were analyzed. The results show the importance of designing versatile systems that can be customized and adapted in real time and in natural contexts with a clearly inclusive approach. But they also suggest that technology may not be an appropriate complementary intervention tool for all children with ASD. This underlines the need for additional well-controlled tests on the characteristics that would allow identifying which students might or might not benefit from different technology modalities. (AU)

Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.

Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.

Cost-effectiveness of robot-assisted total hysterectomy for benign pathologies compared to laparoscopic surgery: a retrospective study with propensity score.

BACKGROUND: Hysterectomy for benign pathologies is one of the most common gynecological surgeries. In recent years, robotic surgery has become an alternative to traditional surgery, but at a higher cost. OBJECTIVE: Estimate the cost of benign robot-assisted hysterectomy for the purpose of supporting public decision-making, as well as the additional cost per major postoperative complication (ClavienDindo score ≥ 3) avoided one month after surgery robotic versus traditional laparoscopic. METHODS: Single-center retrospective study including patients operated on for benign hysterectomy at La Pitié Salpêtrière hospital between January 2016 and December 2019: 99 by robotic approach, and 86 by laparoscopic approach. Comparison of robotic surgery to laparoscopy. Calculation of a cost-effectiveness ratio (ICER). Use of the propensity score inverse weighting method to ensure comparability of groups. RESULTS: Robotic surgery has a total cost of € 6,615 at 1 month per patient compared to € 3,859 for laparoscopic surgery with an additional cost of € 377,534 per major postoperative complication avoided, longer operating time and an absence of significant difference in terms of complications and length of hospitalization. CONCLUSION: In terms of cost-effectiveness, according to this study, the robot does not appear to be better than laparoscopy. In the years to come, we can expect a development of robotic surgery with rationalization of the practice, with appropriate selection of patients for robotic surgery, development of outpatient surgery and a reduction in the cost of the equipment.

Comparative assessment of safety and efficacy between the AirSeal system and conventional insufflation system in robot-assisted laparoscopic radical prostatectomy: a systematic review and meta-analysis.

This meta-analysis aimed to compare perioperative outcome measures between the AirSeal system and conventional insufflation system in robot-assisted laparoscopic prostatectomy. Up to May 2024, comprehensive searches were conducted across various prominent databases worldwide, such as PubMed, Embase, and Google Scholar, focusing solely on English-language materials. Reviews and protocols devoid of published data were excluded, along with conference abstracts and articles unrelated to the study's aims. Primary outcome measures encompassed operative duration and hospitalization length, while secondary outcome measures included estimated blood loss and complications. The meta-analysis included five cohort studies, encompassing a total of 1503 patients. In comparison to the conventional insufflation system group, the AirSeal group displayed shorter operative times (WMD - 15.62, 95% CI - 21.87 to - 9.37; p < 0.00001) and reduced hospital stays (WMD - 0.45, 95% CI - 0.60 to - 0.30; p < 0.00001). Fewer major complications (OR 0.15, 95% CI 0.03 to 0.66; p = 0.01). Notably, there were no significant differences observed in estimated blood loss or overall complications between the two groups. Compared to conventional insufflation systems, employing the AirSeal system in robot-assisted laparoscopic radical prostatectomy appears to potentially decrease operative time and hospital length of stay without a concurrent rise in estimated blood loss or complication rates.

When Trustworthiness Meets Face: Facial Design for Social Robots.

As a technical application in artificial intelligence, a social robot is one of the branches of robotic studies that emphasizes socially communicating and interacting with human beings. Although both robot and behavior research have realized the significance of social robot design for its market success and related emotional benefit to users, the specific design of the eye and mouth shape of a social robot in eliciting trustworthiness has received only limited attention. In order to address this research gap, our study conducted a 2 (eye shape) × 3 (mouth shape) full factorial between-subject experiment. A total of 211 participants were recruited and randomly assigned to the six scenarios in the study. After exposure to the stimuli, perceived trustworthiness and robot attitude were measured accordingly. The results showed that round eyes (vs. narrow eyes) and an upturned-shape mouth or neutral mouth (vs. downturned-shape mouth) for social robots could significantly improve people's trustworthiness and attitude towards social robots. The effect of eye and mouth shape on robot attitude are all mediated by the perceived trustworthiness. Trustworthy human facial features could be applied to the robot's face, eliciting a similar trustworthiness perception and attitude. In addition to empirical contributions to HRI, this finding could shed light on the design practice for a trustworthy-looking social robot.

Dynamic Cooperative Communications with Mutual Information Accumulation for Mobile Robots in Industrial Internet of Things.

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay.

Dynamic Modeling and Analysis of Flexible-Joint Robots with Clearance.

The coupling effects of flexible joints and clearance on the dynamics of a robotic system were investigated. A numerical analysis was undertaken to reveal the coupling effects between flexible joints and clearance. The nonlinear spring-damping model and Coulomb model were applied to characterize the contact characteristics of the clearance, and a model for the flexible joint was formulated using the equivalent spring theory. An accurate robot model was established based on the clearance and joint flexibility characterization. The dynamic equation of a robot was obtained according to the Newton-Euler method. A comparative analysis was performed to assess the impacts of both the joint action of clearance and flexible joints and varying joint clearance values on the performance of the robot. The results showed that the coupling effects of flexible joints and clearance had a negative impact on the system dynamic performance. The amplitudes of the dynamic responses caused by the clearance are weakened by the flexible joint, but it leads to the lag of the system response. This study served as the theoretical foundation for exploring precise control techniques in robotics research.

Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision, and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analyses based on artificial neural networks (ANNs) were performed for robot manipulator systems used in the textile industry, and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analyses of a 6-degree-of-freedom (DOF) industrial denim robot manipulator were carried out via four different learning algorithms, delta-bar-delta (DBD), online back propagation (OBP), quick back propagation (QBP), and random back propagation (RBP), for the proposed neural network predictor. From the results obtained, it was observed that the QBP-based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modeling of trajectory control. In addition, the 3-5-6 type ANN structure was also improved, and its root mean square error (RMSE) and statistical R2 performances were compared with that of the 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives.

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.

Robot-based 6D bioprinting for soft tissue biomedical applications.

Within this interdisciplinary study, we demonstrate the applicability of a 6D printer for soft tissue engineering models. For this purpose, a special plant was constructed, combining the technical requirements for 6D printing with the biological necessities, especially for soft tissue. Therefore, a commercial 6D robot arm was combined with a sterilizable housing (including a high-efficiency particulate air (HEPA) filter and ultraviolet radiation (UVC) lamps) and a custom-made printhead and printbed. Both components allow cooling and heating, which is desirable for working with viable cells. In addition, a spraying unit was installed that allows the distribution of fine droplets of a liquid. Advanced geometries on uneven or angled surfaces can be created with the use of all six axes. Based on often used bioinks in the field of soft tissue engineering (gellan gum, collagen, and gelatin methacryloyl) with very different material properties, we could demonstrate the flexibility of the printing system. Furthermore, cell-containing constructs using primary human adipose-derived stem cells (ASCs) could be produced in an automated manner. In addition to cell survival, the ability to differentiate along the adipogenic lineage could also be demonstrated as a representative of soft tissue engineering.

Case in which percutaneous fibrin glue injection was useful for refractory urinary fistula following robot-assisted partial nephrectomy.

Introduction: Urinary fistula is a rare complication following robot-assisted partial nephrectomy. For cases refractory to conservative treatment, only ureteral stent placement and percutaneous drainage are the established treatment alternatives. Case presentation: A 44-year-old man presented with urinary fistula 3 weeks after robot-assisted partial nephrectomy for right renal cell carcinoma. Follow-up observations were conducted for 2 weeks; however, no improvements were observed. Additionally, the patient did not improve following percutaneous drainage and ureteral stent insertion. Subsequently, the patient received percutaneous injections of fibrin glue, with the urinary fistula showing significant improvements on the following day. Conclusion: Our findings indicated that percutaneous fibrin glue injection can effectively treat refractory urinary fistula following partial nephrectomy.

A systematic review on the effectiveness of robot-assisted minimally invasive gastrectomy.

BACKGROUND: Robot-assisted minimally invasive gastrectomy (RAMIG) is increasingly used as a surgical approach for gastric cancer. This study assessed the effectiveness of RAMIG and studied which stages of the IDEAL-framework (1 = Idea, 2A = Development, 2B = Exploration, 3 = Assessment, 4 = Long-term follow-up) were followed. METHODS: The Cochrane Library, Embase, Pubmed, and Web of Science were searched for studies on RAMIG up to January 2023. Data collection included the IDEAL-stage, demographics, number of participants, and study design. For randomized controlled trials (RCTs) and long-term studies, data on intra-, postoperative, and oncologic outcomes, survival, and costs of RAMIG were collected and summarized. RESULTS: Of the 114 included studies, none reported the IDEAL-stage. After full-text reading, 18 (16%) studies were considered IDEAL-2A, 75 (66%) IDEAL-2B, 4 (4%) IDEAL-3, and 17 (15%) IDEAL-4. The IDEAL-stages were followed sequentially (2A-4), with IDEAL-2A studies still ongoing. IDEAL-3 RCTs showed lower overall complications (8.5-9.2% RAMIG versus 17.6-19.3% laparoscopic total/subtotal gastrectomy), equal 30-day mortality (0%), and equal length of hospital stay for RAMIG (mean 5.7-8.5 days RAMIG versus 6.4-8.2 days open/laparoscopic total/subtotal gastrectomy). Lymph node yield was similar across techniques, but RAMIG incurred significantly higher costs than laparoscopic total/subtotal gastrectomy ($13,423-15,262 versus $10,165-10,945). IDEAL-4 studies showed similar or improved overall/disease-free survival for RAMIG. CONCLUSION: During worldwide RAMIG implementation, the IDEAL-framework was followed in sequential order. IDEAL-3 and 4 long-term studies showed that RAMIG is similar or even better to conventional surgery in terms of hospital stay, lymph node yield, and overall/disease-free survival. In addition, RAMIG showed reduced postoperative complication rates, despite higher costs.

Robot-related injuries in the workplace: An analysis of OSHA Severe Injury Reports.

Industrial robots are increasingly commonplace, but research on prototypical accidents and injuries has been sparse, hindering evidence-based safety strategies. Using Severe Injury Reports (SIRs) from the U.S. Occupational Safety and Health Administration (OSHA), we identified 77 robot-related accidents from 2015-2022. Of these, 54 involved stationary robots, resulting in 66 injuries, mainly finger amputations and fractures to the head and torso. Mobile robots caused 23 accidents, leading to 27 injuries, mainly fractures to the legs and feet. A two-stage deductive-inductive thematic analysis was performed using text data from the final narratives in the reports to discover patterns in tasks, precipitating mechanisms, and contributing factors. Findings highlight the need for guards and collision avoidance systems that detect individual extremities. Post-contact strategies should focus on mitigating finger amputations. More structured and detailed narratives in the SIRs are needed.

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection.

Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants' biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.

Analysis of the therapeutic efficacy of robot-assisted percutaneous screw fixation in the minimally invasive treatment of pelvic fractures.

Objective: To compare the therapeutic efficacy of robot-assisted and manual screw placement techniques for the treatment of pelvic fractures. Methods: This study included patients with pelvic fractures admitted to our orthopedic department between January 2020 and January 2022. They were randomly assigned to either the robot-assisted group or the control group. Various parameters, including surgical duration, intraoperative bleeding, fluoroscopy frequency, postoperative pain, length of hospitalization, postoperative hematological indices, postoperative functional scores, and postoperative complications, were compared between the two groups. Results: There were no significant differences in age, sex, body mass index, and preoperative hematological parameters between the two groups. The robot-assisted group exhibited significantly shorter surgical duration, lower fluoroscopy frequencies, lower postoperative pain scores, and shorter length of hospitalization compared to the control group. At 3 and 6 months postoperatively, patients in the robot-assisted group demonstrated significantly higher Majeed functional scores in comparison to the control group. However, there were no significant differences in Majeed scores at 12 months postoperatively. Moreover, there were no significant differences in postoperative complications between the two groups. Conclusion: Robot-assisted minimally invasive treatment of pelvic fractures using hollow screws effectively reduced surgical duration, mitigated intraoperative bleeding and postoperative pain, shortened hospital stays, and promoted faster functional recovery.

Analysis of early perioperative outcomes of robot-assisted radical cystectomy and colonic diversion.

Studies of right colon pouch urinary diversion have widely varying estimates of the risk of perioperative complications, reoperation, and readmission. We sought to describe the association between specific risk factors and complication, readmission, and reoperation rates following right colon pouch urinary diversion. Patients undergoing robot-assisted right colon pouch urinary diversion from July 2013 to December 2022 were analyzed. Outcome measures include high-grade (Clavien-Dindo grade ≥ 3) complications within 90 days, readmission within 90 days, and reoperation at any time during follow-up. Specific risk factors such as age, gender, body mass index (BMI), diabetes, Charlson comorbidity index (CCI), and prior radiation were analyzed to establish an association with these outcomes. During the study period, 77 patients underwent the procedure and were eligible to study. The average follow-up was 88.7 (SD 14) months. 90-day high-grade complications were 24.67%, and 90-day readmission was 33.76%. The cumulative rate of any reoperation was 40.2%, and major reoperation was 24.67%. Female gender (OR 3.3, p = 0.015), 1 kg/m2 increase in BMI (OR 3.77, p = 0.014), diabetes (OR 3.49, p = 0.021), higher CCI (OR 1.59, p = 0.034), prior radiation (OR 1.97, p = 0.026), lower eGFR (OR 0.99, p = 0.032) and BMI ≥ 25 kg/m2  (OR 3.9, p value 0.02) was associated with Clavien III-IV complications. Female gender (OR 3.3, p = 0.015), diabetes (OR 3.97, p = 0.029), higher Charlson Comorbidity Index (OR 1.73, p = 0.031), prior radiation (OR 1.45, p = 0.029), lower eGFR (OR 0.87, p = 0.037) and BMI ≥ 25 kg/m2 (OR 3.86, p = 0.031) were predictive of reoperation. Overall, the rate of postoperative complications, readmissions, and reoperation was high but consistent with other studies. This study helps further characterize surgical outcomes after right colon pouch urinary diversion and highlights patients who may benefit from enhanced preoperative management for minimising complications.

Early and delayed periprosthetic joint infection in robot-assisted total knee arthroplasty: a multicenter study.

BACKGROUND: Robot-assisted total knee arthroplasty (RA-TKA) has significantly improved knee surgery outcomes in the last few years. However, its association with the periprosthetic joint infection (PJI) rate remains debatable. This study investigates the incidence of early and delayed PJI in a multicentric cohort of patients who underwent RA-TKA, aiming to elucidate the risk associated with this procedure. METHODS: This retrospective study analyzed data from a consecutive series of patients who underwent RA-TKA using the NAVIO Surgical System (Smith & Nephew, Memphis, USA) between 2020 and 2023. The inclusion criteria encompassed individuals over 18 years of age with a minimum follow-up period of three months. The primary outcome was the incidence of early and delayed PJI, defined according to the European Bone and Joint Infection Society (EBJIS) diagnostic criteria. Secondary outcomes included the evaluation of postoperative complications. RESULTS: The study included patients who underwent RA-TKA with the NAVIO system, achieving an average follow-up of 9.1 ± 3.9 months. None of the patients met the EBJIS criteria for a likely or confirmed infection, indicating an absence of both early and delayed PJI cases. Two patients required subsequent surgical interventions due to patellar maltracking and prosthetic loosening, respectively. Additionally, three patients underwent passive manipulation under anesthesia (MUA). CONCLUSION: The findings indicate no evidence of early or delayed PJI in patients undergoing RA-TKA within the study period. The low complication rate further supports the reliability and safety of this surgical technique in short-term follow-up. LEVEL OF EVIDENCE: IV.

Contextual emotion detection in images using deep learning.

Introduction: Computerized sentiment detection, based on artificial intelligence and computer vision, has become essential in recent years. Thanks to developments in deep neural networks, this technology can now account for environmental, social, and cultural factors, as well as facial expressions. We aim to create more empathetic systems for various purposes, from medicine to interpreting emotional interactions on social media. Methods: To develop this technology, we combined authentic images from various databases, including EMOTIC (ADE20K, MSCOCO), EMODB_SMALL, and FRAMESDB, to train our models. We developed two sophisticated algorithms based on deep learning techniques, DCNN and VGG19. By optimizing the hyperparameters of our models, we analyze context and body language to improve our understanding of human emotions in images. We merge the 26 discrete emotional categories with the three continuous emotional dimensions to identify emotions in context. The proposed pipeline is completed by fusing our models. Results: We adjusted the parameters to outperform previous methods in capturing various emotions in different contexts. Our study showed that the Sentiment_recognition_model and VGG19_contexte increased mAP by 42.81% and 44.12%, respectively, surpassing the results of previous studies. Discussion: This groundbreaking research could significantly improve contextual emotion recognition in images. The implications of these promising results are far-reaching, extending to diverse fields such as social robotics, affective computing, human-machine interaction, and human-robot communication.