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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

Mathematical Biosciences and engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical Sciences. All rights reserved.

关键词： Empirical mode decomposition

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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Ball sition ature bedded Group Activity Recognition del for Team Sport Games 4

Ball sition ature bedded Group Activity Recognition del for ...

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4th International Conference on Machine Learning and Intelligent Systems, MLIS 2022

作者： Jamsrandorj, Ankhzaya Chao, Vanyi Oo, Yin May Mun, Kyung-Ryoul Kim, Jinwook Department of Human Computer Interface & Robotics Engineering University of Science & Technology Korea Republic of Department of Ai Robotics University of Science & Technology Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Korea Republic of

ISBN: (纸本)9781643683560

Most group activity recognition models focus mainly on spatio-temporal features from the players in sports games. Often they do not pay enough attention to the game object, which heavily affects not only individual action but also a group activity. We propose a new group activity recognition model for sports games that incorporates players' motion information and game object positional information. The proposed method uses a transformer encoder for temporal feature extraction and a 'simple' conventional convolutional neural network for extracting spatial features and fusing them with the relative ball position-embedded features. The experimental results show that our model achieved comparable results to state-of-the-art methods on the Volleyball dataset by using only one transformer encoder block and the ball position. © 2022 The authors and IOS Press.

关键词： Sports

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach 11

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical Engineering Shahrood Iran Oakland University Intelligent Robotics Laboratory Michigan United States

ISBN: (纸本)9798350308105

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2023 IEEE.

关键词： Emotion Recognition

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Towards AI-driven Next Generation Personalized Healthcare and Well-being

Towards AI-driven Next Generation Personalized Healthcare an...

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2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024

作者： Aksu, Fatih Bria, Alessandro Caragliano, Alice Natalina Caruso, Camillo Maria Chen, Wenting Cordelli, Ermanno Coser, Omar Francesconi, Arianna Furia, Leonardo Guarrasi, Valerio Iannello, Giulio Lauretti, Clemente Manni, Guido Marino, Giustino Paolo, Domenico Ruffini, Filippo Shen, Linlin Sicilia, Rosa Soda, Paolo Tamantini, Christian Tortora, Matteo Wu, Zhuoru Zollo, Loredana Department of Biomedical Sciences Humanitas University Milan Italy Department of Electrical and Information Engineering University of Cassino and Southern Latium Cassino Italy Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Italy City University of Hong Kong Hong Kong Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Campus Bio-Medico of Rome Italy Shenzhen University China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Sweden

In the last few years Artificial Intelligence (AI) is emerging as a game changer in many areas of society and, in particular, its integration in medicine heralds a transformative approach towards personalized healthcare and well-being, promising significant improvements in diagnostic precision, therapeutic outcomes, and patient care. Our research explores the cutting-edge realms of multimodal AI, resilient AI, and healthcare robotics, aiming to harness the synergy of diverse data modalities and advanced computational models to redefine healthcare paradigms. This multidisciplinary effort seeks to bridge technology and clinical practice, advancing AI-driven next generation personalized healthcare and well-being. © 2024 Copyright for this paper by its authors.

关键词： Personalized medicine

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Surface defect identification using Bayesian filtering on a 3D mesh

Surface defect identification using Bayesian filtering on a ...

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作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025

关键词： Mesh generation

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A study on an applied behavior analysis-based robot-mediated listening comprehension intervention for ASD

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Paladyn 2021年第1期12卷 31-46页

作者： Geoffrey Louie, Wing-Yue Korneder, Jessica Abbas, Ibrahim Pawluk, Cristyn Intelligent Robotics Laboratory Department of Electrical and Computer Engineering Oakland University RochesterMI48307 United States Applied Behavior Analysis Clinic Department of Human Development and Child Studies Oakland University RochesterMI48307 United States Intelligent Robotics Laboratory Department of Electrical and Computer Engineering Oakland University RochesterMI48307 United States

Autism spectrum disorder (ASD) is a lifelong developmental condition that affects an individual’s ability to communicate and relate to others. Despite such challenges, early intervention during childhood development has shown to have positive long-term benefits for individuals with ASD. Namely, early childhood development of communicative speech skills has shown to improve future literacy and academic achievement. However, the delivery of such interventions is often time-consuming. Socially assistive robots (SARs) are a potential strategic technology that could help support intervention delivery for children with ASD and increase the number of individuals that healthcare professionals can positively affect. For SARs to be effectively integrated in real-world treatment for individuals with ASD, they should follow current evidence-based practices used by therapists such as Applied Behavior Analysis (ABA). In this work, we present a study that investigates the efficacy of applying well-known ABA techniques to a robot-mediated listening comprehension intervention delivered to children with ASD at a university-based ABA clinic. The interventions were delivered in place of human therapists to teach study participants a new skill as a part of their overall treatment plan. All the children participating in the intervention improved in the skill being taught by the robot and enjoyed interacting with the robot, as evident by high occurrences of positive affect as well as engagement during the sessions. One of the three participants has also reached mastery of the skill via the robot-mediated interventions. © 2021 Wing-Yue Geoffrey Louie et al., published by De Gruyter.

关键词： human robot interaction

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Heuristic Online Path Planning for Mobile Air Purification and Disinfection Robot In Regulating Indoor Air Quality

Heuristic Online Path Planning for Mobile Air Purification a...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Zhong, Qingnan Chen, Kai Duan, Jianghua Ma, Jun Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Robotics and Human Machine Interface Laboratory Futian China The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China

ISBN: (纸本)9798350325706

In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology. © 2023 IEEE.

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Deep Learning Networks for View-Independent Gait Events Detection

SSRN

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SSRN 2022年

作者： Jamsrandorj, Ankhzaya Jung, Dawoon Kumar, Konki Sravan Arshad, Muhammad Zeeshan Mun, Kyung-Ryoul Kim, Jinwook Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of Department of Human Computer Interface & Robotics Engineering University of Science & Technology Daejeon Korea Republic of

Accurate gait detection is crucial in utilizing the ample health information embedded in it. As an alternative of the laborious and demanding sensor-based detection, vision-based approaches ***, its complicate feature engineering process and heavy reliance on the lateral views serve as challenges. This study aimed to propose a view-independent vision-based gait event detection using deep learning networks that requires no pre-processing. A total of 22 participants performed seven different walking and running-related actions and the sequential video frames acquired from their actions were used as the input of the deep learning networks to produce the probability of gait events as outputs. The Transformer network and ResNet18 trained with sequential video frames achieved an F1-score of 0.956 or higher for walking straight and walking around. The detection performance on the frontal, lateral, and backside views did not differ much. The findings enhance applicability of vision-based approach and contribute to increasing its utility in health monitoring. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach

arXiv

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arXiv 2025年

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan United States

—This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2025, CC BY-NC-ND.

关键词： Anthropomorphic robots

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