IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Does Proprioceptive Impairment Affect Feedforward Motor Control? A Cross-Sectional Study on Patients With Brain Damage","authors":"Nicola Valè;Anna Righetti;Enrico Martini;Michele Boldo;Nicola Bombieri;Nicola Smania","doi":"10.1109/TNSRE.2024.3518416","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3518416","url":null,"abstract":"Sensory ataxia and cerebellar ataxia share common manifestations including dysmetria, intentional tremor and lack of smoothness. We formulated a theoretical framework to describe the patients’ sensory and cerebellar ataxic behavior as consequences of a forward model impairment. To test this framework, the present study aimed to compare upper limb movement kinematics in an index-to-nose task between three groups: healthy controls, people with CNS focal lesions and cerebellar deficits and people with CNS focal lesions and somatosensory impairment. We recruited 12 healthy controls (age \u0000<inline-formula> <tex-math>$= 29.0pm 2.9$ </tex-math></inline-formula>\u0000 years, female = 5) and 20 participants with focal CNS lesions. We divided the sample according to the lesion site in participants with lesions in areas involved in the somatosensory information processing (n = 12, age \u0000<inline-formula> <tex-math>$= 62.4pm 13.6$ </tex-math></inline-formula>\u0000 years, female = 5) and participants with lesions in the cerebellum or cerebellar peduncle (n = 8, age \u0000<inline-formula> <tex-math>$= 64.3pm 13.9$ </tex-math></inline-formula>\u0000 years, female = 1). Movement features concerning movement efficiency (average velocity, peak velocity), accuracy (spatial error when pointing to the nose) and motor planning (timing and spatial occurrence of velocity peak, velocity and deviation from ideal trajectory at 150ms after the movement onset) were computed. Both the groups of participants with CNS lesions performed the movement slower than healthy controls. When comparing results from the two groups of patients, we showed that participants with cerebellar lesions were characterized by greater trial-to-trial variability of the velocity peak (repeated measure ANOVA group effect: F = 5.242, p = 0.012) and its timing (condition*group interaction: F = 5.38, p = 0.011). Our findings suggested that both participants with cerebellar and somatosensory deficits showed signs of anticipatory motor control impairment.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"113-121"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10802931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":">IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors","authors":"","doi":"10.1109/TNSRE.2024.3514413","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3514413","url":null,"abstract":"","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"C3-C3"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803917","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information","authors":"","doi":"10.1109/TNSRE.2024.3514395","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3514395","url":null,"abstract":"","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"C2-C2"},"PeriodicalIF":4.8,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803916","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility Assessment of an Optically Powered Digital Retinal Prosthesis Architecture for Retinal Ganglion Cell Stimulation","authors":"William Lemaire;Maher Benhouria;Konin Koua;Wei Tong;Gabriel Martin-Hardy;Melanie Stamp;Kumaravelu Ganesan;Louis-Philippe Gauthier;Marwan Besrour;Arman Ahnood;David John Garrett;Sébastien Roy;Michael R. Ibbotson;Steven Prawer;Réjean Fontaine","doi":"10.1109/TNSRE.2024.3516492","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3516492","url":null,"abstract":"Clinical trials previously demonstrated the notable capacity to elicit visual percepts in individuals with visual impairments caused by retinal diseases by electrically stimulating the remaining neurons on the retina. However, these implants restored very limited visual acuity and required transcutaneous cables traversing the eyeball, leading to reduced reliability and complex surgery with high postoperative infection risks. To overcome the limitations imposed by cables, a retinal implant architecture in which near-infrared illumination carries both power and data through the pupil to a digital stimulation controller is presented. A high efficiency multi-junction photovoltaic cell transduces the optical power to a CMOS stimulator capable of delivering flexible interleaved sequential stimulation through a diamond microelectrode array. To demonstrate the capacity to elicit a neural response with this approach while complying with the optical irradiance limit at the pupil, fluorescence imaging with a calcium indicator is used on a degenerate rat retina. The power delivered by the laser at the permissible irradiance of 4 mW/mm2 at 850 nm is shown to be sufficient to both power the stimulator ASIC and elicit a response in retinal ganglion cells (RGCs), with the ability to generate of up to 35 000 pulses per second at the average stimulation threshold. This confirms the feasibility of generating a response in RGCs with an infrared-powered digital architecture capable of delivering complex sequential stimulation patterns at high repetition rates, albeit with some limitations.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"92-102"},"PeriodicalIF":4.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10795186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding Motor Excitability in TMS Using EEG-Features: An Exploratory Machine Learning Approach","authors":"Lisa Haxel;Oskari Ahola;Paolo Belardinelli;Maria Ermolova;Dania Humaidan;Jakob H. Macke;Ulf Ziemann","doi":"10.1109/TNSRE.2024.3516393","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3516393","url":null,"abstract":"Brain state-dependent transcranial magnetic stimulation (TMS) holds promise for enhancing neuromodulatory effects by synchronizing stimulation with specific features of cortical oscillations derived from real-time electroencephalography (EEG). However, conventional approaches rely on open-loop systems with static stimulation parameters, assuming that pre-determined EEG features universally indicate high or low excitability states. This one-size-fits-all approach overlooks individual neurophysiological differences and the dynamic nature of brain states, potentially compromising therapeutic efficacy. We present a supervised machine learning framework that predicts individual motor excitability states from pre-stimulus EEG features. Our approach combines established biomarkers with a comprehensive set of spectral and connectivity measures, implementing multi-scale feature selection within a nested cross-validation scheme. Validation across multiple classifiers, feature sets, and experimental protocols in 50 healthy participants demonstrated a mean prediction accuracy of \u0000<inline-formula> <tex-math>$71 ; pm ; 7$ </tex-math></inline-formula>\u0000%. Hierarchical clustering of top predictive EEG features revealed two distinct participant subgroups. The first subgroup, comprising approximately 50% of participants, showed predictive features predominantly in alpha and low-beta bands in sensorimotor regions of the stimulated hemisphere, aligning with traditional associations of motor excitability and the sensorimotor \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-rhythm. The second subgroup exhibited predictive features primarily in low and high gamma bands in parietal regions, suggesting that motor excitability is influenced by broader neural dynamics for these individuals. Our data-driven framework effectively identifies personalized motor excitability biomarkers, holding promise to optimize TMS interventions in clinical and research settings. Additionally, our approach provides a versatile platform for biomarker discovery and validation across diverse neuromodulation paradigms and brain signal classification tasks.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"103-112"},"PeriodicalIF":4.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10795227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Online Estimating Framework for Ankle Actively Exerted Torque Under Multi-DOF Coupled Dynamic Motions via sEMG","authors":"Yu Zhou;Jianfeng Li;Shiping Zuo;Jie Zhang;Mingjie Dong;Zhongbo Sun","doi":"10.1109/TNSRE.2024.3515966","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3515966","url":null,"abstract":"Ankle rehabilitation robots can offer tailored rehabilitation training, and facilitate the functional recovery of patients. Accurate estimation of the actively exerted torque from the ankle joint complex (AJC) can increase the engagement of patients during rehabilitation training. Given the three degrees of freedom (DOFs) of AJC and its coupled motion, it becomes essential to accurately estimate the actively exerted torque under multi-DOF. This work introduces an estimation framework that includes the Hill-based sEMG-force model, the ankle musculoskeletal dynamic decoupling model, and the parameter identification-calibration strategy. The Hill-based sEMG-force model estimates the force generated by individual muscles involved in AJC; The parameter identification-calibration strategy combined with pre-experiment identifies unknown variables in the ankle musculoskeletal dynamic decoupling model; Finally, the musculoskeletal dynamic decoupling model relates the muscle forces to the AJC’s actively exerted torque. The musculoskeletal dynamic decoupling model combines anatomical and biomechanical features, enabling parameters derived from a single DOF pre-experiment through identification-calibration strategy to be applicable in multi-DOF dynamic motion. To evaluate the estimation performance of the framework, experiments were conducted in various directions involving both single and multiple DOFs. The results show that the proposed framework can estimate the actively exerted torque with a normalized root mean square error (NRMSE) of \u0000<inline-formula> <tex-math>${10}.{29}% pm {2}.{86}%$ </tex-math></inline-formula>\u0000 (mean ± SD) for torque estimation under a single DOF, and NRMSE of \u0000<inline-formula> <tex-math>${11}.{35}% pm {4}.{51}%$ </tex-math></inline-formula>\u0000 under multiple DOFs, compared to the actual measured values. This framework can improve human-robot interaction training and improve the effectiveness of robot-assisted ankle rehabilitation training. It can also provide accurate neuro-information and joint torque data for medical teams, which can lead to early diagnosis of diseases and patient-specific treatment protocols.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"81-91"},"PeriodicalIF":4.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10793243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-Based Approach for Predicting Transactive Energy in Neurorehabilitation","authors":"Naveed Ahmad Khan;Tanishka Goyal;Fahad Hussain;Prashant K. Jamwal;Shahid Hussain","doi":"10.1109/TNSRE.2024.3515175","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3515175","url":null,"abstract":"Advancements in robotic neurorehabilitation have made it imperative to enhance the safety and personalization of physical human-robot interactions (pHRI). Estimation and management of energy transfer between humans and robots is essential for enhancing safety during the rehabilitation. Traditional control methods, which rely on coordinate-based monitoring of robot velocity and external forces, often fail in unstructured environments due to their susceptibility to sensor noise and limited adaptability to individual patient needs. This paper introduces the concept of transactive energy, a coordinate-invariant entity that captures the energy dynamics between the human and the robot during robot-assisted rehabilitation and can be used for personalized robot control. However, estimation of such energy transfer is a complex process and therefore, we have developed a transformer-based model to predict the transactive potential energy. The proposed model is implemented on an ankle rehabilitation robot which is a compliant parallel robot and provides the required three rotational degrees of freedom (DOF). The model learns from the data obtained from the experiments carried out using the ankle robot with five stroke patients on two types of controllers: an impedance controller operated in zero impedance control mode and a trajectory tracking controller. This study provides a baseline, for future research on energy-based control mechanisms in pHRI applications, by utilizing the advanced deep learning models.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"46-57"},"PeriodicalIF":4.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10793448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intracranial Disease-Region Composite- Interpretation Technology for Enhanced Source Localization in Pediatric Epilepsy Surgery","authors":"Jeongyoon Shin;Wonsik Yang;Jungmin Seo;Won Seok Chang;Heung Dong Kim;Se Hee Kim;Jong-Moon Chung","doi":"10.1109/TNSRE.2024.3514940","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3514940","url":null,"abstract":"Electroencephalography (EEG) based source localization (ESL) is a useful method to localize the epileptogenic zone in epilepsy surgery. However, previous techniques only perform 3-dimensional (3D) reconstruction, and do not conduct delineation on the cortex surface as a resection guidance, and there is very little data on intracranial EEG and pediatric cases. This study proposes an Intracranial Disease-region Composite-interpretation (IDC) EEG-based source localization (ESL) scheme that uses 3D extended reality (XR) edge computing to enhance visualization and comprehensive interpretation of intracranial EEG-based source localization (iESL) for patients with pediatric epilepsy. The proposed IDC-ESL method was effective in predicting the surgical outcome in patients with focal epilepsy, which can be effectively used for epilepsy surgery. Seizure freedom was clearly associated with complete resection of combined EEG features of interictal spike, high-frequency oscillation (HFO), and seizure onset zone (SOZ), and it had the highest significance in localizing the epileptogenic zone. However, for patients with Lennox-Gastaut syndrome (LGS), IDC-ESL was not performed effectively because of a deeply seated lesion and multifocal abnormalities. It could only roughly estimate the affected area, mainly because of insular involvement. Cautious interpretation based on intraoperative electrocorticography (ECoG) is required for accurate insular resection, particularly for LGS cases.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"34-45"},"PeriodicalIF":4.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10791311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Multi-Frequency Decomposition Entropy Learning for Nonlinear fMRI Data Analysis","authors":"Di Han;Yuhu Shi;Lei Wang;Yueyang Li;Weiming Zeng","doi":"10.1109/TNSRE.2024.3515168","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3515168","url":null,"abstract":"Functional magnetic resonance imaging (fMRI) have been widely adopted to explore the underlying neural mechanisms between psychiatric disorders which share common neurobiology and clinical manifestations. However, the existing studies mainly focus on linear relationships and ignore nonlinear contributions. To address the above issues, we propose a new method named multi-frequency decomposition entropy (MDE) learning for inferring nonlinear functional connectivity between brain regions. Firstly, the variational mode decomposition was used to divide fMRI data into five groups of frequency. Next, the copula entropy was used to calculate the nonlinear relationship between brain regions in each frequency group, and then the best important nonlinear relationships were screen out by using statistical t-test. Lastly, a gyrus importance index was proposed to reflect the distribution trend of gyri in different frequency groups. The results of applying MDE for the fMRI data analysis of schizophrenia, bipolar disorder, and attention-deficit hyperactivity disorder showed that the difference between the three groups of patient and healthy control is large at the hub nodes, and the nonlinear relationship between the patient groups is weak when they are at the same hub node. In addition, each disease exhibits unique characteristics compared with other diseases and healthy control. In a word, the nonlinear functional connectivity of different frequency groups reflect the differences and commonalities between diseases and reveal possible discriminating biomarkers among mental diseases.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"68-80"},"PeriodicalIF":4.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10793239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Estimation of Hand Kinematics From Electromyographic Signals Based on Power-and Time-Efficient Transformer Deep Learning Network","authors":"Chuang Lin;Chunxiao Zhao;Jianhua Zhang;Chen Chen;Ning Jiang;Dario Farina;Weiyu Guo","doi":"10.1109/TNSRE.2024.3514938","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3514938","url":null,"abstract":"Surface Electromyographic (sEMG) signals contain motor-related information and therefore can be used for human-machine interaction (HMI). Deep learning plays an important role in extracting motor-related information from sEMG signals. However, most studies prioritize model accuracy without sufficient consideration of model efficiency, including the model size, power consumption, and the computational speed of the model. This leads to impractical power consumption, heat dissipation levels and processing time in wearable computation scenarios. Here, we propose an efficient Transformer method that employs the EMSA (Efficient Multiple Self-Attention) and pruning mechanism to improve efficiency and accuracy concurrently, when estimating finger joint angles from sEMG signals. The proposed method does not only achieve state-of-the-art accuracy but can also be deployed on wearable devices to satisfy real-time applications. We applied the proposed model on the Ninapro DB2-dataset to estimate finger joint angles during grasping tasks. RNN series models, Convolution series models, and Transformer series models were used as reference models for comparison. In addition to common model accuracy, the deployment performance of the models was tested on microprocessors, such as Intel CPU i5, Apple M1, and Raspberry Pi 4B. When tested on 38 subjects of the Ninapro DB2, the proposed model resulted in a correlation coefficient of \u0000<inline-formula> <tex-math>$0.82~pm ~0.04$ </tex-math></inline-formula>\u0000, root mean squared error (RMSE) of \u0000<inline-formula> <tex-math>$10.77~pm ~1.48$ </tex-math></inline-formula>\u0000, and normalized RMSE of \u0000<inline-formula> <tex-math>$0.11~pm ~0.01$ </tex-math></inline-formula>\u0000, which were all similar to the results achieved by the state-of-the-art (SOTA) reference methods. Further, the computational time of the proposed methods was 65.99 ms on the Raspberry Pi 4B, which outperformed all the RNN series models and the Transformer series models. The model size and the power (the minimum size and power are 0.39 MB and 2.28 w) consumption of the proposed model also outperformed that of all reference Transformer methods. These experimental results indicate that our model can maintain the accuracy of the SOTA methods while significantly improving efficiency, thus being a promising approach for real-life applications in wearable devices.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"58-67"},"PeriodicalIF":4.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10789212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}