Reviewerly/exHarmony huggingface dataset:
OpenPose-Based Yoga Pose Classification Using Convolutional Neural Network

Human action recognition has various implementation, such surveillance system, elders care and construction alert, which arouse lots of interest of research in classification of still image. This paper mainly focusses on detecting the pose of Yoga. Comparing with traditional method using convolutional neural network, which is using original image as input to train the VGG network, extracting the skeleton images and feed them into Mobile net can impressively increase the accuracy. Dataset is collected from Kaggle website which contains five categories of labeled Yoga image. Openpose is an open-source API that can extract the human skeleton structure form the Yoga image based on the pose. With these skeleton image as input, the convolutional neural network will perceive everything important such as pose and angle of joints, rather than irrelevant features such as color and environment. Using Mobile net instead of common method to do classification with VGG, calculation time has been remarkably reduced and size of model is lighter which is able to be apply on single chip device. The result of model is impressive, showing high accuracy in both training data set and testing data set, which means no overfitting problem occurred in the experiment. Model size and demanding of hardware are also acceptable for a common personal computer.
PCR172 A Natural Language Processing (NLP) Approach to Automate Patients’ Testimonials Analysis

Patients’ testimonials (e.g. posts on forums or responses to questionnaires) provide valuable insights to define and characterize patient-reported outcomes (PRO), quality of life and patients’ perspective of disease symptoms. However, traditional NLP methods used for automated analysis of patients’ testimonials are based on co-occurrence word frequency, and thus are not fit-for-purpose for such data, with short texts and rare co-occurrences. Building upon an efficient method based on semantic proximity we introduced recently, the objective is to improve results post-processing and method scalability. First, testimonials are vectorized to embeddings with a pre-trained Sentence-BERT language model, capturing the meaning of the texts beyond simple word co-occurrence. To ease interpretation, embeddings dimensionality is reduced to two using the UMAP algorithm. Then, an agglomerative clustering is performed on new embeddings with an optimal number of clusters (based on silhouette scores). In addition to previous work, the clustering dendrogram facilitates post-processing interventions by automatically pre-selecting the clusters that can be merged together or split into two subclusters. The most prevalent terms in a cluster are used to label it. Sentiment analysis is also performed to refine tags and ensure clusters’ definition relevance. Tested on patients’ testimonials of an average length of 15 words, our method provides more consistent and interpretable topics than state-of-the-art approaches (e.g. latent Dirichlet allocation, non-negative matrix factorization). Compared to previous work, the improved clustering post-processing makes the analysis pipeline much faster to execute and more scalable, without altering performance. Our proposed method allows to extract more consistent topics from a large volume of short texts in a more automated and less time-consuming way. It provides stronger insights on patients’ perception about a wide range of healthcare topics (side effects, treatment, symptoms...), paving the way for better PRO definitions and patient-centric evaluation, and striving better adherence to treatments.
MT7 Artificial Intelligence Applied to Motor Activities: A Rapid Review of the Literature

To analyze the application of artificial intelligence in human motor activities. Rapid literature review, in Google Scholar and Google Patents using the keywords: Machine Learning, Musculoskeletal, artificially intelligent, applications and sensor. The search was conducted during May 2022. Scientific articles responding to the objective and patents of the applications were included. 27 papers were analyzed: 13 of them patents published in 2011(1), 2013(3), 2015(1), 2016(4), 2018(2), 2020(2). The 14 papers were published in 2014(1), 2015(2), 2016(3), 2018(2), 2019(3), and 2021(1). The applications are based on: a) sensors to predict musculoskeletal disorders and biosensors to obtain parameters of human dynamics b) biosensors for exercise learning using artificial neural networks c) biosensors for physical rehabilitation, d) smart watches for fall detection, and e) mobile applications for vital sign monitoring. Existing products include 1) musculoskeletal robotic devices for disabled patients, 2) visual posture detection and balance assessment, and 3) cardiovascular capacity estimation. The reviewed works show that the technology has been applied to various problems, however, they pose a restriction due to the cost of the equipment and its maintenance, especially in kinesiological equipment. The challenge for engineers and health staff is to generate low-cost mobile rehabilitation equipment.
MSR55 Extraction of Ejection Fraction From Clinical Notes Using a Pretrained Albert-for-Question-Answering Model

Our goal was to determine the accuracy of a pretrained Albert-for-Question-Answering model for extraction of ejection fraction (EF) values from relevant sentences for patients having congestive heart failure.
MSR137 The Impact of the Proportion of Cured and Length of Follow-up on Mixture Cure Models (MCMS): A Simulation Study

This study investigated how the cured proportion and data maturity impact MCM performance.
MSR118 Machine Learning Approaches Towards Identification of Phenotypes in Various Diseases Using Electronic Health Records

Machine learning (ML), that involves algorithmic modeling to extract general deductions from large and complex real-world datasets (e.g., electronic health records or EHR), is being rapidly adopted into healthcare over the past few decades. Traditionally, phenotyping in diseases like asthma was based on clinical features only. We describe herein the application of various ML algorithms for large EHR datasets as a suitable improvement in identifying novel phenotypes and confirming existing ones in various disease areas like allergy, cardiology, and oncology. The present concept outlines the identification of phenotypes through non-linear patient characteristics within EHR including demographics, clinical details, comorbidities, medications, procedures, diagnostics, and healthcare encounters. Considering the fragmented, missing, and inaccurate data within EHR, lexical and logical methods with verification from medical experts can be used to create a “fit-for-purpose” dataset. Various ML algorithms used on such datasets can enable various steps of the phenotyping process including: Natural language processing for decoding physicians’ notes, MissForest algorithm (using random forest techniques) for missing value imputation, Uniform Manifold Approximation and Projection for non-linear dimensionality reduction, and Bayesian Gaussian Mixture as an advanced clustering algorithm to identify the phenotypes. This followed by suitable informative and interactive data visualization with medical interpretation of shared clinical characteristics can help in identifying and validating phenotypes. We outlined herein the concepts involved in using multiple ML algorithms complemented with medical expertise in identifying novel phenotypes and confirming established ones from large, complex, real-world EHR data by grouping clinical characteristics. This can help in the development of innovative medicines in various disease areas by supporting clinical trial recruitment and by aiding design of next-generation clinical trials based on real-world data. Machine learning algorithms can enable identification of novel phenotypes from real-world EHR data thus helping in better disease understanding and in developing innovative medicines.
An efficient multilevel thresholding segmentation method based on improved chimp optimization algorithm

To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved Chimp Optimization Algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as the objective function. The best threshold values for RGB images’ three channels are found using IChOA. Meanwhile, several strategies are introduced including population initialization strategy combining with Gaussian chaos and opposition-based learning, the position update mechanism of particle swarm algorithm (PSO), the Gaussian-Cauchy mutation and the adaptive nonlinear strategy. These methods enable the IChOA to raise the diversity of the population and enhance both the exploration and exploitation. Additionally, the search ability, accuracy and stability of IChOA have been significantly enhanced. To prove the superiority of the IChOA based multilevel thresholding segmentation method, a comparison experiment is conducted between IChOA and 5 six meta-heuristic algorithms using 12 test functions, which fully demonstrate that IChOA can obtain high-quality solutions and almost does not suffer from premature convergence. Furthermore, by using 10 standard test images the IChOA-based multilevel thresholding image segmentation method is compared with other peers and evaluated the segmentation results using 5 evaluation indicators with the average fitness value, PSNR, SSIM, FSIM and computational time. The experimental results reveal that the presented IChOA-based multilevel thresholding image segmentation method has tremendous potential to be utilized as an image segmentation method for color images because it can be an effective swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of color images.
Student worksheet: Persepsi dan performa akademik mahasiswa keperawatan

Background: Students' perceptions of learning methods may be influenced by the diversity of students, educational facilities and equipment, their expectations, and other university circumstances. This study aims to identify or examine student perceptions about the use of learning media in the form of student worksheets, especially in the Emergency Nursing course, which is expected to see the optimization of learning. Methods: The design of this study was a descriptive observational study with a cross-sectional approach. The population in this study were 82 nursing students at a university in Malang, where respondents were asked to fill out a questionnaire. The data collected will be analyzed descriptively and analytically. Results: In this study, the results showed that the majority of students had a good perception (85.4%) about the use of student worksheets in Emergency Nursing courses, more than half of them had above-average academic performance (59.8%), and there was a significant relationship between students' perception of academic performance (p-value &lt; 0.000). Conclusion: Improvements in the learning environment can improve student academic performance.
Tracking of moving targets in complex environments

Abstract The tracking of a moving target algorithm based on the traditional visual servo can not introduce image and physical constraints, and does not consider the problems such as the features beyond the camera field of view, the robotic arm’s out-of-motion space, and the robotic arm reaching the joint limit. Moreover, the tracking accuracy and stability of the moving target continue to be poor in complex environments (such as the chaotic background, the illumination changes, and the target is partially occluded), and the recognition of the target image is extremely strict, In this paper, a new method of tracking and grasping moving targets is proposed by combining the pose estimation of targets with the tracking and grasping of industrial robots. Kalman filter is used to compensate for the time delay. This method avoids the influence of traditional methods on target depth estimation and image and physical constraints and has strong robustness.
Vehicle Route Planning Using A* Algorithm in Real-world Roads

Abstract With the technical progress in the fields of robotics as well as artificial intelligence, many intelligent algorithms which focus on robot path planning problems have been developed so far. A* algorithm is an efficient and technically mature path planning algorithm that can efficiently solve robot path planning problems. In this context, this paper proposes an improved A* algorithm to plan roads for automobiles in urban areas, introduce time factors into the cost function, select an urban area, use grid map modeling, and simulate in the MATLAB environment. The final result shows that this kind of algorithm can efficiently solve the problem of automobile road planning.
MSR10 Applying Sequence Clustering Methods to Characterize Healthcare Pathways of Patients at Different Prostate Cancer Stages in the French Nationwide Healthcare Database (SNDS)

The heterogeneity of prostate cancer (PC) patient journeys for a same stage makes their evaluation complex using descriptive statistics. Unsupervised machine learning has the potential to reveal patterns within heterogeneous data. However, impact of such methods in real-world studies is not yet clear. The objective was to illustrate how clustering and visualization of healthcare pathways can enhance the characterization of patients with PC, at all disease stages. Patients with PC in 2014 were identified in SNDS and their data were extracted with up to 5-year history and 4-year follow-up. Fifty-one specific healthcare encounters constitutive of PC management were synthetized into 4 macro-variables using clustering of variables approach. Values of these macro-variables over patient follow-ups constituted healthcare pathways. Optimal matching using TRATE substitution method was applied to calculate distances between pathways. Partitioning around medoids algorithm was then used to define groups of similar pathways across four incident cohorts: hormone-sensitive (HSPC), metastatic hormone-sensitive (mHSPC), castration-resistant (CRPC), and metastatic castration-resistant (mCRPC). Index plots were used to represent pathway clusters. The repartition of macro-variables values – surveillance, local treatment, androgenic deprivation, and advanced treatment – appeared to be consistent with PC status. Two to five clusters of healthcare pathways were observed in each of the different cohorts, corresponding for most of them to relevant clinical patterns. Clustering allowed to distinguish patients undergoing active surveillance, or treated according to cancer progression risk in HSPC; patients with rapid and slow disease progression in CRPC; patients receiving treatment for potentially curative or palliative purposes in mHSPC and mCRPC. Visualization methods combined to clustering approach enabled the identification of clinically relevant patterns of PC management. This experience also highlighted potential improvement to leverage the impact of results. Characterization of these care pathways is an essential element for the robust assessment of healthcare technologies in health outcome research.
Aplikasi Rekam Medis Kesehatan Balita dalam Menentukan Kesehatan Balita di Rw 002 Sicalung

The industrial revolution in medical and health industry developed rapidly. With the consequence in rapid grow of big data usage, the development of data decentralization becomes more important, because it can facilitate more accurate analysis. The problem in this study is data collection and monitoring of the health of toddlers and early childhood in Posyandu (integrated healthcare center) is still inaccurate while they still used manual data input and has not been carried out in real time, therefore we created medical record application that able to provide convenience in monitoring the health of toddlers and early childhood, as well to analyze their development in term of heath. The results of the testing showed that mothers expect toddlers to be well controlled. The implementation of the toddler health medical record application provides convenience and real time report and analysis in Posyandu for midwives, health care provider and for mothers as well. Suggestions for further research is to combine with other methods so that future applications will be perfectly polished to be used widely.
Deep convolutional neural networks-based features for Indonesian large vocabulary speech recognition

There are great interests in developing speech recognition using deep learning technologies due to their capability to model the complexity of pronunciations, syntax, and language rules of speech data better than the traditional hidden Markov model (HMM) do. But, the availability of large amount of data is necessary for deep learning-based speech recognition to be effective. While this is not a problem for mainstream languages such as English or Chinese, this is not the case for non-mainstream languages such as Indonesian. To overcome this limitation, we present deep features based on convolutional neural networks (CNN) for Indonesian large vocabulary continuous speech recognition in this paper. The CNN is trained discriminatively which is different from usual deep learning implementations where the networks are trained generatively. Our evaluations show that the proposed method on Indonesian speech data achieves 7.26% and 9.01% error reduction rates over the state-of-the-art deep belief networks-deep neural networks (DBN-DNN) for large vocabulary continuous speech recognition (LVCSR), with Mel frequency cepstral coefficients (MFCC) and filterbank (FBANK) used as features, respectively. An error reduction rate of 6.13% is achieved compared to CNN-DNN with generative training.
A Lower Bounding Framework for Motion Planning Amid Dynamic Obstacles in 2D

This work considers a Motion Planning Problem with Dynamic Obstacles (MPDO) in 2D that requires finding a minimum-arrival-time collision-free trajectory for a point robot between its start and goal locations amid dynamic obstacles moving along known trajectories. Existing methods, such as continuous Dijkstra paradigm, can find an optimal solution by restricting the shape of the obstacles or the motion of the robot, while this work makes no such assumptions. Other methods, such as search-based planners and sampling-based approaches can compute a feasible solution to this problem but do not provide approximation bounds. Since finding the optimum is challenging for MPDO, this paper develops a framework that can provide tight lower bounds to the optimum. These bounds act as proxies for the optimum which can then be used to bound the deviation of a feasible solution from the optimum. To accomplish this, we develop a framework that consists of (i) a bi-level discretization approach that converts the MPDO to a relaxed path planning problem, and (ii) an algorithm that can solve the relaxed problem to obtain lower bounds. We also present numerical results to corroborate the performance of the proposed framework. These results show that the bounds obtained by our approach for some instances are up to twice tighter than a baseline approach showcasing potential advantages of the proposed approach.
Parametrized Motion Planning and Topological Complexity

In this paper we study paramertized motion planning algorithms which provide universal and flexible solutions to diverse motion planning problems. Such algorithms are intended to function under a variety of external conditions which are viewed as parameters and serve as part of the input of the algorithm. Continuing the recent paper [2], we study further the concept of parametrized topological complexity. We analyse in full detail the problem of controlling a swarm of robots in the presence of multiple obstacles in Euclidean space which served for us a natural motivating example. We present an explicit parametrized motion planning algorithm solving the motion planning problem for any number of robots and obstacles in $${\mathbb R}^d$$ . This algorithm is optimal, it has minimal possible topological complexity for any $$d\ge 3 $$ odd. Besides, we describe a modification of this algorithm which is optimal for $$d\ge 2$$ even. We also analyse the parametrized topological complexity of sphere bundles using the Stiefel - Whitney characteristic classes.
Development of SETS Based E-book Teaching Materials in Strengthening Science Literacy in Elementary School

This study aims to produce one of the teaching materials that can be used for elementary school students to strengthen scientific literacy. The research method used is Research and Development (R&amp;D), the subjects in this study were the 18th graders of SDN Pilangbango. Data collection techniques using observation, questionnaires and documentation. The results achieved in this study are developing SETS-based E-book teaching materials and testing the feasibility of the validation results of experts, teacher and student responses get a percentage of linguists 86%, material experts 82%, media experts 100%, student responses were 91.89%, teacher responses were 94% and got the criteria "very valid". So it can be concluded that this study resulted in a SETS-based E-book teaching language that was able to strengthen the scientific literacy of grade VI elementary school students who had been tested. By experts who get very valid eligibility criteria.