Unsupervised Contact Learning for Humanoid Estimation and Control

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 201

"Roam around the city” - workshops by the Spatial Economy Student Research Club at the Lodz University of Technology

Od 3 lat Studenckie Koło Naukowe Gospodarki Przestrzennej Cirkula, działające przy Kolegium Gospodarki Przestrzennej oraz na Wydziale Budownictwa, Architektury i Inżynierii Środowiska Politechniki Łódzkiej wraz z Miejską Pracownią Urbanistyczną w Łodzi organizuje ogólnopolskie studenckie warsztaty urbanistyczno-architektoniczne „Włócz się po mieście” poświęcone zagadnieniu rewitalizacji tkanki śródmiejskiej Łodzi. Wydarzenie skierowane jest do studentów architektury i urbanistyki oraz gospodarki przestrzennej z całej Polski. Dzięki zaangażowaniu studentów, jednostek miejskich, organizacji pozarządowych oraz społeczników warsztaty mają interdyscyplinarny charakter, jednocześnie pozwalając na szersze spojrzenie na problematykę oraz stworzenie platformy wymiany doświadczeń i pomysłów.For the last three years “Włócz się po mieście” (Roam around the city) urban workshops have been organized by the ‘Cirkula’ Spatial Economy Student Research Club founded by the Spatial Economy College and the Faculty of Civil Engineering, Architecture and Environmental Engineering at the Lodz University of Technology, in cooperation with the Lodz Municipal Planning Office . This annual event is dedicated to the topic of revitalization of the Lodz city centre. It is addressed to students of architecture, urban planning and spatial economy from all over Poland. Thanks to the commitment of students, local government units, non-governmental organisations and activists, the workshops have an interdisciplinary character, allowing participants to see urban revitalization in a broader perspective, share experiences and ideas

The Anti-Inflammatory Effect of Acidic Mammalian Chitinase Inhibitor OAT-177 in DSS-Induced Mouse Model of Colitis

Inflammatory bowel diseases (IBD) are chronic and relapsing gastrointestinal disorders, where a significant proportion of patients are unresponsive or lose response to traditional and currently used therapies. In the current study, we propose a new concept for anti-inflammatory treatment based on a selective acidic mammalian chitinase (AMCase) inhibitor. The functions of chitinases remain unclear, but they have been shown to be implicated in the pathology of various inflammatory disorders regarding the lung (asthma, idiopathic pulmonary fibrosis) and gastrointestinal tract (IBD and colon cancer). The aim of the study is to investigate the impact of AMCase inhibitor (OAT-177) on the dextran sulfate sodium (DSS)-induced models of colitis. In the short-term therapeutic protocol, OAT-177 given intragastrically in a 30 mg/kg dose, twice daily, produced a significant (p < 0.001) anti-inflammatory effect, as shown by the macroscopic score. Additionally, OAT-177 significantly decreased TNF-α mRNA levels and MPO activity compared to DSS-only treated mice. Intraperitoneal administration of OAT-177 at a dose of 50 mg/kg caused statistically relevant reduction of the colon length. In the long-term therapeutic protocol, OAT-177 given intragastrically in a dose of 30 mg/kg, twice daily, significantly improved colon length and body weight compared to DSS-induced colitis. This is the first study proving that AMCase inhibitors may have therapeutic potential in the treatment of IBD

Chitinases and Chitinase-Like Proteins as Therapeutic Targets in Inflammatory Diseases, with a Special Focus on Inflammatory Bowel Diseases

Chitinases belong to the evolutionarily conserved glycosyl hydrolase family 18 (GH18). They catalyze degradation of chitin to N-acetylglucosamine by hydrolysis of the β-(1-4)-glycosidic bonds. Although mammals do not synthesize chitin, they possess two enzymatically active chitinases, i.e., chitotriosidase (CHIT1) and acidic mammalian chitinase (AMCase), as well as several chitinase-like proteins (YKL-40, YKL-39, oviductin, and stabilin-interacting protein). The latter lack enzymatic activity but still display oligosaccharides-binding ability. The physiologic functions of chitinases are still unclear, but they have been shown to be involved in the pathogenesis of various human fibrotic and inflammatory disorders, particularly those of the lung (idiopathic pulmonary fibrosis, chronic obstructive pulmonary disease, sarcoidosis, and asthma) and the gastrointestinal tract (inflammatory bowel diseases (IBDs) and colon cancer). In this review, we summarize the current knowledge about chitinases, particularly in IBDs, and demonstrate that chitinases can serve as prognostic biomarkers of disease progression. Moreover, we suggest that the inhibition of chitinase activity may be considered as a novel therapeutic strategy for the treatment of IBDs

Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders : an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal

Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research. Methods and results - We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing. Interpretation - We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.Funding Agencies|Region Stockholm (ALF project); Karolinska InstituteKarolinska Institutet</p

Artificial intelligence and computer vision in orthopaedic trauma:the why, what, and how

Artificial intelligence (AI) is, in essence, the concept of 'computer thinking', encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the 'why'), the current applications (the 'what'), and the approach to unlocking its full potential (the 'how'). Cite this article: Bone Joint J 2022;104-B(8):911-914

Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN)

Purpose: Convolutional neural networks (CNNs) are increasingly being developed for automated fracture detection in orthopaedic trauma surgery. Studies to date, however, are limited to providing classification based on the entire image—and only produce heatmaps for approximate fracture localization instead of delineating exact fracture morphology. Therefore, we aimed to answer (1) what is the performance of a CNN that detects, classifies, localizes, and segments an ankle fracture, and (2) would this be externally valid? Methods: The training set included 326 isolated fibula fractures and 423 non-fracture radiographs. The Detectron2 implementation of the Mask R-CNN was trained with labelled and annotated radiographs. The internal validation (or ‘test set’) and external validation sets consisted of 300 and 334 radiographs, respectively. Consensus agreement between three experienced fellowship-trained trauma surgeons was defined as the ground truth label. Diagnostic accuracy and area under the receiver operator characteristic curve (AUC) were used to assess classification performance. The Intersection over Union (IoU) was used to quantify accuracy of the segmentation predictions by the CNN, where a value of 0.5 is generally considered an adequate segmentation. Results: The final CNN was able to classify fibula fractures according to four classes (Danis-Weber A, B, C and No Fracture) with AUC values ranging from 0.93 to 0.99. Diagnostic accuracy was 89% on the test set with average sensitivity of 89% and specificity of 96%. External validity was 89–90% accurate on a set of radiographs from a different hospital. Accuracies/AUCs observed were 100/0.99 for the ‘No Fracture’ class, 92/0.99 for ‘Weber B’, 88/0.93 for ‘Weber C’, and 76/0.97 for ‘Weber A’. For the fracture bounding box prediction by the CNN, a mean IoU of 0.65 (SD ± 0.16) was observed. The fracture segmentation predictions by the CNN resulted in a mean IoU of 0.47 (SD ± 0.17). Conclusions: This study presents a look into the ‘black box’ of CNNs and represents the first automated delineation (segmentation) of fracture lines on (ankle) radiographs. The AUC values presented in this paper indicate good discriminatory capability of the CNN and substantiate further study of CNNs in detecting and classifying ankle fractures. Level of evidence: II, Diagnostic imaging study