Frontiers in Neurorobotics最新文献

{"title":"Hybrid EEG-EMG intention decoding for real-time triggering of a lower-limb exoskeleton.","authors":"Ke Wang, Wenshan Li, Danyang Ding, Lei Wang, Yue Sun, Hongbo Zhao, Weijun Gong","doi":"10.3389/fnbot.2026.1914651","DOIUrl":"10.3389/fnbot.2026.1914651","url":null,"abstract":"<p><strong>Background: </strong>Wearable lower-limb exoskeletons have the potential to support intention-driven control of lower-limb exoskeletons, but existing control strategies often rely on mechanical or manual triggers that fail to capture user intent.</p><p><strong>Method: </strong>Subjects were recruited from 23/9/2024 to 10/2/2025. A BiLSTM detector was pretrained on a dataset collected from 50 healthy volunteers (45 for training, 5 for independent testing) using bilateral surface EMG recordings from six lower-limb muscles (12 EMG channels), 16-channel EEG, and hip-knee kinematics. Seven naive participants then completed ten 20-m outward-and-return walking trials (10 m outward and 10 m return) under each of three control modes (EMG, EEG, hybrid). Primary outcomes were triggering latency and classification accuracy. Triggering latency was defined as the time interval between the onset of the gait-transition event and the activation of the exoskeleton assistance command. This latency included the observation delay introduced by the sliding window, feature extraction time, BiLSTM inference time, and communication delay between the decoder and the exoskeleton controller. Usability was assessed with donning/doffing times and QUEST 2.0.</p><p><strong>Results: </strong>The hybrid BiLSTM detector achieved higher classification accuracy (left: 91.3%; right: 86.6%) and shorter mean per-step triggering latency (left: 0.29 s; right: 0.28 s) than either EMG-only (mean 0.33 s) or EEG-only approaches (mean 0.30 s). Hybrid EEG-EMG fusion therefore improved decoding performance while reducing triggering latency compared with unimodal decoding strategies. The latency reduction relative to EMG-only control corresponded to a large effect size (Cohen's d ≈ 0.88). Hybrid sessions also yielded shorter total session time (mean 32.1 min). Usability metrics demonstrated acceptable donning/doffing times and favorable QUEST 2.0 scores (mean 32.0/40).</p><p><strong>Conclusion: </strong>These proof-of-concept results demonstrate that BiLSTM-based fusion of EEG and EMG improves responsiveness and classification reliability for exoskeleton assistance. These findings underscore the contribution of EEG-derived sensorimotor features and EMG information for intention-related gait transition detection within a multimodal real-time exoskeleton control framework. We discuss limitations related to sample size, artifact validation, and generalizability and identify next steps for patient studies and ergonomic optimization.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1914651"},"PeriodicalIF":3.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13581620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148989645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual-affect-guided spatiotemporal graph modeling of disaster-image diffusion for embodied decision support.","authors":"Xiao Zhang, Wei Zhang","doi":"10.3389/fnbot.2026.1889303","DOIUrl":"10.3389/fnbot.2026.1889303","url":null,"abstract":"<p><strong>Introduction: </strong>Disaster-response robots and other embodied agents can use social-media imagery as contextual evidence, but existing approaches typically analyze affect at the image level or model diffusion without isolating affect-specific information.</p><p><strong>Methods: </strong>We encoded disaster images into 128-dimensional continuous visual-affect embeddings representing visual intensity, fear, sadness, and negative valence. These embeddings initialized node states in a time-aware graph neural network whose edges were constrained by observed interaction links, temporal precedence, and spatial reachability. Multi-task heads predicted propagation scale, propagation speed, and spatial-ranking consistency, and a parameter-decomposition module separated affect-associated contributions from structure-associated residuals. Experiments used 4,450 reconstructed image-centered CrisisMMD samples, a common 70/15/15 split, five random seeds, matched baselines, and ablation tests.</p><p><strong>Results: </strong>The full model achieved an AUC of 0.842 ± 0.011, an RMSE of 0.091 ± 0.007 under the current task definition, and a Kendall tau of 0.730 ± 0.018. Relative to matched ablations, visual affect increased AUC by 0.041, time-aware recurrence reduced RMSE by 33.6%, and spatial constraints increased Kendall tau by 0.089. The RMSE unit must be standardized after the propagation-speed versus propagation-time definition is resolved, as marked in the proof.</p><p><strong>Discussion: </strong>Visual affect provides an interpretable complementary signal for modeling disaster-image diffusion. The findings do not establish causal effects, cross-platform generalizability, real-time device performance, or closed-loop robot benefit; the framework should therefore be regarded as an upstream social-sensing prior for embodied decision support.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1889303"},"PeriodicalIF":3.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148989614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GSP-3D: generalizable 3D Gaussian Splatting with diffusion policy and world model for long-horizon bimanual manipulation.","authors":"Xukun Liu, Junhua Huang, Shenggang Wei, Liguo Hou, Junmou Qiao, Aifeng Liu, Hongsheng Huang, Fengjuan Xie","doi":"10.3389/fnbot.2026.1867375","DOIUrl":"10.3389/fnbot.2026.1867375","url":null,"abstract":"<p><strong>Introduction: </strong>Learning robust visuomotor policies for bimanual manipulation remains challenging due to the stringent requirements for precise coordination between arms and the ability to generalize across diverse environmental conditions. Existing diffusion-based policies often suffer from temporally inconsistent action generation, while their reliance on sparse point cloud representations limits structural completeness and fails to capture future scene dynamics, hindering performance in long-horizon tasks.</p><p><strong>Methods: </strong>To address these limitations, we introduce GSP-3D, a unified framework that integrates generalizable 3D Gaussian Splatting (3DGS) with diffusion-based policy learning. GSP-3D comprises three key components: (1) a Generalizable Gaussian Regressor (GGR) that predicts 3D Gaussian primitives from a single RGB-D frame in real time; (2) a 3DGS-conditioned diffusion policy that aggregates Gaussians into compact latent representations, replacing point clouds with explicit geometric primitives; and (3) a transformer-based world model that forecasts future Gaussian sets and leverages prediction error as an auxiliary loss to enforce temporal consistency across actions.</p><p><strong>Results: </strong>We evaluate GSP-3D on the RoboTwin 2.0 benchmark across a range of bimanual manipulation tasks under both clean and domain-randomized conditions, including variations in lighting, backgrounds, and tabletop distractors. Experimental results show that GSP-3D consistently outperforms existing baseline methods, achieving higher success rates while maintaining minimal computational overhead.</p><p><strong>Discussion: </strong>These findings demonstrate that integrating explicit 3D Gaussian representations with diffusion policies offers an efficient and robust solution for long-horizon, temporally coherent bimanual manipulation, effectively addressing the generalization and consistency challenges that limit current approaches.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1867375"},"PeriodicalIF":3.5,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13572419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148967777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation.","authors":"Xukun Liu, Fengjuan Xie, Shibo Liu, Xu Sun, Zhenyu Liu, Guangning Li, Shenggang Wei","doi":"10.3389/fnbot.2026.1959872","DOIUrl":"https://doi.org/10.3389/fnbot.2026.1959872","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fnbot.2026.1846433.].</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1959872"},"PeriodicalIF":3.5,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13564045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148955731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive visual diagnostic model for complex scenes based on unsupervised pseudo-label continuous learning and BiFPN-enhanced YOLO.","authors":"Xingque Xu, Lifei Li, Shaoyan Jiang, Yongjian Li, Bei Li","doi":"10.3389/fnbot.2026.1911412","DOIUrl":"10.3389/fnbot.2026.1911412","url":null,"abstract":"<p><p>With the deployment of intelligent systems in open environments, visual perception capabilities are required for engineering applications. Traditional static visual detection models experience feature degradation under environmental changes. Conventional pseudo-labeling techniques exhibit confirmation bias when processing complex samples, and models encounter knowledge forgetting during the continuous absorption of unlabeled data streams. This paper constructs an adaptive visual diagnostic framework for complex scenes, integrating unsupervised domain adaptation with continuous learning. At the feature extraction level, by introducing a BiFPN node, the network compensates for multi-scale spatial distortions caused by environmental changes while maintaining computational economy. A pseudo-label bidirectional recovery mechanism constrained by dynamic thresholds is utilized to extract supervision signals and alleviate confirmation bias. During the parameter iteration phase, a structured predictive distillation loss function is integrated to mitigate the knowledge forgetting phenomenon during continuous data absorption. Tests on the BDD100K dataset show that, with a computational load of 16.6 GFLOPs and an inference speed of 19.3 FPS, the model records an mAP@0.5 of 43.5% and 47.1%, and an mAP@0.5:0.95 of 22.1% and 24.5% in the illumination and meteorological domain transfer tasks, respectively. In the Cityscapes fog transfer test, the model reaches an mAP@0.5 of 48.3% and an mAP@0.5:0.95 of 25.1%. After 10 rounds of incremental evolution, the source domain mAP@0.5 remains at 52.8%, demonstrating knowledge retention capabilities comparable to baseline continuous learning methods.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1911412"},"PeriodicalIF":3.5,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148929637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VLM-guided retrieval augmented generation (RAG) for robot action prediction.","authors":"Boris Kuster, Nikola Marić, Fatima Aziz, Boshko Koloski, Andrej Gams, Aleš Ude","doi":"10.3389/fnbot.2026.1894674","DOIUrl":"10.3389/fnbot.2026.1894674","url":null,"abstract":"<p><p>Reliable action prediction is essential for robotic electronic-waste disassembly, where device diversity and damage make preplanned sequences impractical. Vision-language models (VLMs) offer broad visual and semantic knowledge, but zero-shot predictions can be unreliable, while fine-tuning requires substantial task-specific data. We propose a VLM-guided retrieval-augmented generation (RAG) framework that crops task-relevant image regions, retrieves examples from a structured local database, re-ranks candidates, and expands the search when needed. We evaluated the framework on six types of smoke detectors and heat-cost allocators and compared the performance of three VLMs using zero-shot inference, single-example RAG, VLM-guided RAG, and task-specific fine-tuning. Cropping improved retrieval across all evaluated models, while VLM-guided re-ranking increased top-1 retrieval accuracy from 68.47% to 84.57-89.29%. Next action prediction accuracy reached 90.82-95.92% and significantly outperformed single-example RAG. RAG-based methods also outperformed the fine-tuned models. Median prediction latency was 8.95 s, although expanded searches were slower. Overall, VLM-guided RAG enables accurate, data-efficient adaptation to new device types without fine-tuning, at the cost of increased and variable latency.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1894674"},"PeriodicalIF":3.5,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148911527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A group brain-controlled method for UAVs using a hybrid paradigm of hand movements and visual evoked potentials.","authors":"Rui Li, Shikun Bian, Yaxi Deng, Xiaoqing Wei, Shiqiang Yang, Yang Liu, Jiangcheng Chen","doi":"10.3389/fnbot.2026.1858496","DOIUrl":"10.3389/fnbot.2026.1858496","url":null,"abstract":"<p><p>Brain-computer interfaces (BCIs) are among the most prominent communication technologies that establish a direct channel for information exchange between the brain and external devices. They have been extensively applied in the field of aerospace. However, traditional BCI technology faces challenges, including a limited number of brain control commands and insufficient recognition accuracy in electroencephalography (EEG) decoding. These limitations make it difficult for traditional BCIs to perform complex tasks with high accuracy. Therefore, this study proposed a novel group BCI (G-BCI) system and further constructed a brain-machine shared control method for unmanned aerial vehicle (UAV) swarm control. First, a novel G-BCI paradigm combining precise hand movements and visual evoked potentials was designed. Moreover, an improved multi-domain feature fusion convolutional neural network (MDFF-CNN) was employed to decode EEG and electromyography (EMG) signals from precise hand movements, while a Filter Bank Common Spatial Patterns with Canonical Correlation Analysis (FBCCA) method was used for Steady-State Visual Evoked Potentials (SSVEP) decoding. Furthermore, a task-driven shared control model mapping the G-BCI system and the leader-follower UAV swarm control strategy was proposed. To verify the effectiveness of the proposed method, eight participants were recruited to conduct both offline and online experiments. The proposed G-BCI system achieved an offline accuracy of 88.91 ± 5.06% and an online accuracy of 88.89 ± 1.96%. All the experimental results demonstrate the feasibility of the proposed method.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1858496"},"PeriodicalIF":3.5,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148897326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-layer hierarchical MPPT strategy of PV systems based on AF-EPO algorithm under complex irradiance conditions.","authors":"Lei Wang, Jinzhao Cui, Chao Ge, Zhixin Lun","doi":"10.3389/fnbot.2026.1882865","DOIUrl":"10.3389/fnbot.2026.1882865","url":null,"abstract":"<p><p>Photovoltaic power generation systems exhibit multi-peak power-voltage characteristics under partial shading conditions, severely limiting the effectiveness of conventional maximum power point tracking methods. This paper proposes a two-layer hierarchical MPPT architecture based on an adaptive fuzzy-weighted eagle perching optimization algorithm, designated AF-EPO. Unlike existing fuzzy-metaheuristic MPPT hybrids that apply fuzzy logic as an exogenous regulator to secondary control variables while retaining fixed-parameter core dynamics, AF-EPO embeds the fuzzy inference system directly into the endogenous EPO scaling factor, jointly driven by iteration progress and population diversity, thereby addressing three persistent limitations of metaheuristic MPPT through structural changes absent from existing fuzzy-hybrid formulations: the fixed-parameter exploration-exploitation dilemma, the computational overhead of unconditional global search, and the residual steady-state power oscillation that persists in all population-based methods. A slope sign-reversal detection module first identifies whether the power-voltage curve is unimodal or multi-modal, activating the global search layer only when partial shading is confirmed. In the global layer, a 25-rule Mamdani fuzzy inference system dynamically adjusts the EPO scaling factor according to iteration progress and population diversity, balancing exploration and exploitation throughout the search. A variable-step incremental conductance controller then refines the operating point to suppress steady-state oscillation. Simulations across three scenarios demonstrate that AF-EPO reduces tracking time by 47.3%, power oscillation rate by 74.6%, and energy loss by 38.2% compared with standard EPO. Hardware experiments on a TMS320F28335 DSP platform confirm tracking efficiencies exceeding 98.1%, with a maximum simulation-to-experiment deviation of 1.1%, validating the practical effectiveness of the proposed method.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1882865"},"PeriodicalIF":3.5,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13521919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intention-driven ankle exoskeleton training acutely alters swing foot clearance distribution: a preliminary study.","authors":"Hanatsu Nagano, Rezaul Begg, Eri Sarashina, Hiroaki Kawamoto","doi":"10.3389/fnbot.2026.1912825","DOIUrl":"https://doi.org/10.3389/fnbot.2026.1912825","url":null,"abstract":"<p><strong>Introduction: </strong>Tripping is a leading cause of falls, and Minimum Foot Clearance (MFC)-the lowest vertical swing-foot displacement during mid-swing-is a key gait event associated with tripping risk. Preventing tripping requires not only sufficiently high MFC but also consistent control across steps, which depends on neuromotor rather than strength-based mechanisms. We hypothesized that aligning movement intention with repeated, optimal ankle motion using a surface electromyography (sEMG)-driven exoskeleton would improve neuromotor consistency and MFC characteristics.</p><p><strong>Methods: </strong>A total of 12 healthy adults completed 5 min of treadmill walking at 4 km/h before and after a 5-min ankle exoskeleton training sequence ('feet-flat, toes-up, feet-flat, heels-up', 60 bpm). The Vicon motion capture system tracked the reflective markers placed on the heel and toe to collect MFC dataset. The exoskeleton (i.e., Hybrid Assistive Limb) detects voluntary neural drive through sEMG and converts it into assisted ankle motion, supporting intention-based neuromotor training.</p><p><strong>Results: </strong>The obtained MFC dataset showed that training increased the lowest percentile of the MFC distribution (i.e., the risky tail) by 0.39 cm, reduced step-to-step variability, and increased dataset skewness, suggesting reduction of both low and high extremes in swing-foot clearance.</p><p><strong>Discussion: </strong>These preliminary findings suggest that brief intention-based ankle exoskeleton training may acutely improve safety-relevant characteristics of MFC control. Further studies in older adults and neurological populations are required to determine clinical relevance for tripping-risk reduction.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1912825"},"PeriodicalIF":3.5,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13486167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148791904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation.","authors":"Xukun Liu, Fengjuan Xie, Kai Xu, Zhenyu Liu, Shenggang Wei, Guangning Li, Xu Sun","doi":"10.3389/fnbot.2026.1907235","DOIUrl":"https://doi.org/10.3389/fnbot.2026.1907235","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fnbot.2026.1860170.].</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1907235"},"PeriodicalIF":3.5,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148791932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}