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    Polytechnic University of Madrid Reports Findings in Robotics (Robogait: a Robot ic System for Non-invasive Gait Analysis)

    10-11页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now availab le. According to news reporting from Madrid, Spain, by NewsRx journalists, resea rch stated, “The most common methods used in gait analysis laboratories are syst ems based on the use of markers and/or sensors positioned all over the patient’s body while performing a walking test. These approaches usually require individu al calibration, a long time to set up the patient, and, therefore, discomfort of the users.” Financial support for this research came from Ministerio de Ciencia, Tecnologia e Innovacion.

    Data from Nankai University Provide New Insights into Robotics (Prescribed-time Adaptive Fuzzy Control for Pneumatic Artificial Muscle-actuated Parallel Robots With Input Constraints)

    11-11页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in Robotics. A ccording to news originating from Tianjin, People’s Republic of China, by NewsRx correspondents, research stated, “With the advantages of natural flexibility, l arge force-weight ratios, and green cleanliness, pneumatic artificial muscle (PA M) actuators that mimic biological skeletal muscles have attracted much attentio n. However, the inherent defects of PAMs, such as high nonlinearities, limited c ontraction lengths and frequencies, and multiple input constraints, pose signifi cant challenges to the motion control of PAM-actuated parallel robots; meanwhile , most existing methods do not take into account motion constraints and working efficiency.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).

    University of Science and Technology Beijing Reports Findings in Gastrointestina l Diseases and Conditions (Abdominal physical examinations in early stages benef it critically ill patients without primary gastrointestinal diseases: a ...)

    12-13页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Digestive System Disea ses and Conditions - Gastrointestinal Diseases and Conditions is the subject of a report. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Gastrointestinal (GI) functio n is critical for patients in intensive care units (ICUs). Whether and how much critically ill patients without GI primary diseases benefit from abdominal physi cal examinations remains unknown.” Financial supporters for this research include National Natural Science Foundati on of China, Natural Science Foundation of Beijing Municipality.

    Researchers’ from Department of Computer Science and Engineering Report Details of New Studies and Findings in the Area of Machine Learning (CAGSI: A Classifica tion Approach towards Gait Speed Identification)

    13-14页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from the Departme nt of Computer Science and Engineering by NewsRx correspondents, research stated , “The last few decades have witnessed a remarkable amount of research addressin g numerous challenges in the domain of human activity recognition. One popular p roblem in this domain has been that of gait analysis.” Our news editors obtained a quote from the research from Department of Computer Science and Engineering: “A subproblem in this domain is to identify the speed o f a mobile object through gait analysis. Apart from clinical diagnostic applicat ions, the detection of the speed of a person is also important in remote health monitoring, tracking of the mentally incompetent, and determining proper ambulat ory assistive devices for the orthopaedically impaired. Gait analysis-related pr oblems commonly deal with large volumes of interrelated data for which machine-l earning techniques have been proven effective. However, the size of the feature set used in such problems is a crucial factor. The choice of a large feature set may complicate the approach for long-term analysis. The present work addresses the problem of human walking speed classification through the machine learning a pproach. Data was experimentally collected with the mobile phone sensors carried by volunteers of different physiques. Only the acceleration readings along the three axes of the accelerometer are considered for further experimentation. Alth ough walking speed is a personal trait, four classes of data have been curated, namely, slow walking, moderate walking, fast walking, and sitting. The speeds of the walks were not pre-defined so the volunteers performed the walks as per the ir own comfort, which enhances the challenge of distinguishing between sensor si gnals of varying speed. Experiments have been performed using different supervis ed learning algorithms with only acceleration data. The performance of the learn ing models has been analyzed with the help of accuracy, precision, recall, f1-sc ore, and the ROC curve in a One-vs-Rest approach.”

    Findings from Federal University Santa Catarina Provide New Insights into Machin e Learning (Enhancing Hydroelectric Inflow Prediction In the Brazilian Power Sys tem: Comparative Analysis of Machine Learning Models and Hyperparameter Optimiza tion ...)

    14-14页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting from Florianopolis, Brazil, by News Rx journalists, research stated, “Electricity generation in Brazil heavily depen ds on hydroelectric power, making it vulnerable to fluctuations due to its relia nce on weather patterns. Accurately forecasting water inflow into hydroelectric plants is vital for the National Electric System Operator to make decisions rega rding the monthly scheduling and operation of the power system.” The news correspondents obtained a quote from the research from Federal Universi ty Santa Catarina, “In this paper, an evaluation of predicted flows for a 14 -da y horizon are evaluated for the Tucuruihydroelectric plant, located in the Tocan tins river in the North of Brazil. The temporal fusion transformer (TFT), long s hort -term memory (LSTM), and temporal convolutional networks (TCN) are compared . The findings demonstrate that the TFT is a more suitable alternative than LSTM and TCN models for predicting inflows for the next 14 days. The TFT model is hy pertuned by Optuna to achieve an optimized structure (hTFT). The h-TFT had a mea n absolute percentage error of 13.1 and a Nash-Sutcliffe of 0.96, outperforming its initial version and even the bidirectional LSTM model in benchmarking.”

    Data on Machine Learning Reported by Stefan Feuerriegel and Colleagues (Causal m achine learning for predicting treatment outcomes)

    15-15页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Munich, Germ any, by NewsRx correspondents, research stated, “Causal machine learning (ML) of fers flexible, data-driven methods for predicting treatment outcomes including e fficacy and toxicity, thereby supporting the assessment and safety of drugs. A k ey benefit of causal ML is that it allows for estimating individualized treatmen t effects, so that clinical decision-making can be personalized to individual pa tient profiles.” Our news editors obtained a quote from the research, “Causal ML can be used in c ombination with both clinical trial data and real-world data, such as clinical r egistries and electronic health records, but caution is needed to avoid biased o r incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key co mponents and steps.”

    Study Findings from McGill University Broaden Understanding of Robotics (Multi-r obot Relative Pose Estimation and Imu Preintegration Using Passive Uwb Transceiv ers)

    15-16页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news reporting originating in Montreal, Canada, by NewsRx journalists, research stated, “Ultra-wideband (UWB) systems are becoming increasingly popular as a means of inter-robot ranging and communication. A majo r constraint associated with UWB is that only one pair of UWB transceivers can r ange at a time to avoid interference, hence hindering the scalability of UWB-bas ed localization.”Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC).

    Maimonides Medical Center Reports Findings in Arthroplasty (Excellent 10-Year su rvivorship of robotic-arm-assisted unicompartmental knee arthroplasty)

    16-17页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Surgery - Arthroplasty is the subject of a report. According to news reporting originating from Brookl yn, New York, by NewsRx correspondents, research stated, “Robotic-armassisted u nicompartmental knee arthroplasty (UKA) is an excellent solution for patients su ffering from single-compartment knee arthritis. While outcomes tend to be favora ble for UKAs, revision operations, commonly due to component malpositioning and malalignment resulting in accelerated wear, are a major concern.” Our news editors obtained a quote from the research from Maimonides Medical Cent er, “Intraoperative technologies, such as robotic assistance, can help better en sure that implants are positioned based on a patient’s specific anatomy and mech anical physiology. However, long-term survivorship and patientreported satisfac tion with robotic-assisted UKAs are limited. Therefore, the purpose of this stud y was to assess the 10-year outcomes of patients who underwent robotic-arm-assis ted unicompartmental knee arthroplasty. Specifically, we evaluated: 1) 10-year s urvivorships; 2) patient satisfaction scores; and 3) re-operations. From a singl e surgeon and single institution, 185 patients who had a mean age of 65 years (r ange, 39 to 92) and a mean body mass index of 31.6 (range, 22.4 to 39) at a mean of 10 years follow-up were evaluated (range, 9 to 11). For all patients, the sa me robotic-assistive device was utilized intraoperatively, and all patients unde rwent standardized physical therapy and received standardized pain control manag ement. Then 10-year survivorships with Kaplan-Meir curves, patient satisfaction evaluations with a 5-point Likert scale, and re-operations were assessed as prim ary outcomes. Overall implant survivorship was 99%, with only two p atients requiring revision surgery. There was one patient who was converted to a total knee arthroplasty, while the other patient underwent polyethylene exchang e at 5 weeks for an acute infection with successful implant retention. Overall, 97% of the patients were satisfied with their postoperative outcom es, with 81% of patients reporting being very satisfied. There wer e two other patients who required arthroscopic intervention: one to remove a cem ent loose body, the other to remove adhered scar from the fat pad and the anteri or cruciate ligament. This study is one of the first to provide longer-term (mea n 10-year) survivorship and patient-reported satisfaction outcomes for robotic-a ssisted UKA patients. These data show strong support for utilizing this surgical technique, as nearly all patients maintained their original prostheses and repo rted being satisfied after a mean of 10 years.”

    New Biomarkers Findings Has Been Reported by Investigators at University of Melb ourne (Suppressed Activity of the Rostral Anterior Cingulate Cortex As a Biomark er for Depression Remission)

    17-18页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Diagnostics and S creening - Biomarkers are discussed in a new report. According to news reporting originating from Melbourne, Australia, by NewsRx correspondents, research state d, “Suppression of the rostral anterior cingulate cortex (rACC) has shown promis e as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission.” Funders for this research include National Health & Medical Resear ch Council (NHMRC) of Australia, National Health & Medical Researc h Council (NHMRC) of Australia.

    Wuxi University Researcher Publishes New Studies and Findings in the Area of Rob otics (Research on Target Ranging Method for Live-Line Working Robots)

    18-19页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ro botics. According to news reporting out of Wuxi, People’s Republic of China, by NewsRx editors, research stated, “Due to the operation of live-line working robo ts at elevated heights for precision tasks, a suitable visual assistance system is essential to determine the position and distance of the robotic arm or grippe r relative to the target object.” Financial supporters for this research include Open Subjects of State Key Labora tories of China. The news journalists obtained a quote from the research from Wuxi University: “I n this study, we propose a method for distance measurement in live-line working robots by integrating the YOLOv5 algorithm with binocular stereo vision. The cam era’s intrinsic and extrinsic parameters, as well as distortion coefficients, ar e obtained using the Zhang Zhengyou calibration method. Subsequently, stereo rec tification is performed on the images to establish a standardized binocular ster eovision model. The Census and Sum of Absolute Differences (SAD) fused stereo ma tching algorithm is applied to compute the disparity map. We train a dataset of transmission line bolts within the YOLO framework to derive the optimal model. T he identified bolts are framed, and the depth distance of the target is ultimate ly calculated.”