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Micro-electrode array (MEA) electrophysiological signal analysis by descriptive stochastic tests of the probability density structure

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Research on Biomedical engineering 2025年第2期41卷 1-13页

作者： Faria, Vinícius Naves Rezende Ramos, Marina Abadia Martinoia, Sérgio Destro-Filho, João-Batista Postgraduate Program in Electrical Engineering Universidade Federal de MG Uberlândia Brazil Postgraduate Program in Biomedical Sciences Universidade Federal de MG Uberlândia Brazil Division of Informatics Bioengineering Robotics and System Engineering Università Degli Studi Di Genova Genova Italy

Purpose: The primary objective of this study is to present, analyze, and quantitatively characterize the electrical signals obtained from neuronal cultures coupled to micro-electrode arrays (MEAs). Special attention is given to the temporal evolution of electrical activity across distinct geometric regions of the MEA devices. Methods: The proposed methodology involves signal acquisition, classical analysis based on spike and burst detection, and, critically, the characterization of the probability density structure of the signals. This is achieved through the quantification of Gaussianity—expressed as the percentage of segments non-Gaussian (PSNG)—and stationarity— expressed as the percentage of segments non-stationary (PSNE). Results: Initial analyses showed that first-order power spectral density quantifiers lacked the statistical robustness required to effectively describe the complex components of MEA signals. In contrast, PSNG and PSNE demonstrated strong potential in capturing the different stages of neuronal culture development. Cluster analysis further supported the discriminative capacity of these quantifiers. Conclusion: The phase corresponding to decreased neuronal activity and onset of cell death—a biologically critical stage—exhibited the strongest correlation among the studied variables. These results suggest a discernible biological organization and highlight the relevance of PSNG and PSNE as promising tools for assessing neuronal culture dynamics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Cell death

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Quantitative Analysis of Ankle Spasticity Through Electromyography Signal

Quantitative Analysis of Ankle Spasticity Through Electromyo...

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2024 Medical Technologies Congress, TIPTEKNO 2024

作者： Gurluk, Ayse Bilal, Hafiz Muhammad Hocaoglu, Elif Living Robotics Laboratory Graduate School of Health Sciences Physiotherapy and Rehabilitation Program Istanbul Medipol University Istanbul Turkey Living Robotics Laboratory Graduate School of Engineering and Natural Sciences Electrical-Electronics Engineering and Cyber-Systems Program Istanbul Medipol University Istanbul Turkey Living Robotics Laboratory Department of Electrical and Electronics Engineering Istanbul Medipol University Istanbul Turkey

ISBN: (纸本)9798331529819

This study presents a more accurate evaluation method for assessing ankle spasticity compared to conventional goniometric techniques by utilizing measurements obtained from Inertial Measurement Units (IMU) and surface electromyography (sEMG) devices. Integrating IMU and EMG sensors provides substantial benefits for accurately determining the range of motion (ROM) associated with spasticity during targeted movements. In this study, the proposed method provides precise measurement and an objective evaluation of onset time detection of antagonistic muscle co-contraction, and accordingly the ROM of the actuated joint. Moreover, the implementation of cost-effective wearable sensors enables the monitoring of muscle spasticity in clinical environments and can be particularly valuable in regions with limited access to experienced therapists, providing a more efficient approach to tracking spasticity levels. © 2024 IEEE.

关键词： Electromyography

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Classification algorithm for wheelchair operation in real conditions using POF-based pressure sensors

Classification algorithm for wheelchair operation in real co...

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Latin American Electron Devices Conference (LAEDC)

作者： A. X. Gonzalez-Cely C.F. Blanco-Díaz Camilo A.R. Díaz T. Bastos-Filho Robotics and Assistive Technology Laboratory Postgraduate Program in Electrical Engineering Federal University of Espírito Santo Vitoria Brazil Telecommunications Laboratory Postgraduate Program in Electrical Engineering Federal University of Espírito Santo Vitoria Brazil

ISBN: (数字)9798350361292

ISBN: (纸本)9798350361308

A real-time implementation of a classification system is proposed here to operate an electric-powered wheelchair. The proposed framework innovatively integrates Polymeric Optical Fiber (POF)-based pressure sensors, positioned within a neck pillow, to effectively capture critical user-specific pressure data emanating from the user’s neck region. The system considered the mechanical variations presented in the dataset construction owing to the movement generated by the wheelchair. To mitigate this inherent noise, an indispensable preprocessing stage is introduced, and a feature extraction stage is performed considering the variance, Root Mean Square (RMS), Crest Factor Margin (CMF), and Margin Factor (MF) in the time domain. This augmentation enhances the discriminative capacity of the feature representation and, consequently, enhances the classification performance. A fine k-Nearest Neighbors (kNN) was able to distinguish four neck movements: forward, right, left, and back, with performance metrics of: 80.39% (Accuracy), 87.09% (AUC), and 19.40% (False Positive Rate). This work contributes to the development of new strategies for the implementation of more robust power wheelchair controllers by integrating machine learning and optical fiber sensors, in order to assist people with disabilities.

关键词： Pressure sensors Optical fiber sensors Accuracy Wheelchairs Neck Classification algorithms Polymers

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Deep Reinforcement Learning with Pedestrian Trajectory Prediction Model for Service Robot Navigation in Crowded Environments

Deep Reinforcement Learning with Pedestrian Trajectory Predi...

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2023 International Conference on Advanced robotics and Intelligent Systems, ARIS 2023

作者： Wang, Shih-Hao Wu, Yu-Hsiung Li, Tzuu-Hseng S. National Cheng Kung University Ms Degree Program on Ai Robotics AiRobots Laboratory Tainan Taiwan Ncku AiRobots Laboratory Dept. of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9798350302714

Safety and effectiveness are crucial considerations in the navigation of robots within crowded environments. Previous research in this field has predominantly focused on an omniscient approach, designing environments with exceptional obstacle avoidance capabilities. However, this approach fails to address the constraints imposed by real-world applications' limited field of view. Additionally, constructing a robot with a complete field of view can be prohibitively expensive. To tackle these challenges, this paper presents a novel approach that combines a social spatial-temporal graph convolutional neural network (Social-STGCN) with reinforcement learning (RL) to enhance crowd avoidance navigation. This approach offers a more practical and cost-effective solution. Simulations conducted to evaluate our proposed method demonstrate its effectiveness by achieving higher success rates and lower collision rates compared to existing approaches. These results confirm the efficiency and safety of our algorithm for crowd avoidance navigation. In practical applications, we employ the Intel RealSense L515 camera to capture depth and RGB image data, enabling the utilization of the Deep SORT algorithm for pedestrian tracking and recognition. This facilitates the acquisition of accurate position and speed information. To validate the performance of our system, we conducted robot experiments in real-world scenarios, including indoor corridors and elevator lobbies. The results obtained from these experiments clearly indicate that our proposed system successfully accomplishes navigation tasks with improved safety and effectiveness. In summary, this paper introduces an effective crowd avoidance navigation algorithm. By leveraging the Social-STGCN and RL techniques, our algorithm enables robots to navigate tasks with enhanced safety and efficiency, even when their field of view is limited. © 2023 IEEE.

关键词： Air navigation

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Therapist's and Robot's Roles in Robot-Assisted Interventions during JA Therapies of Children with ASD 23

Therapist's and Robot's Roles in Robot-Assisted Intervention...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Aguirre, Andrés Sierra M, Sergio D. Gaitán-Padilla, Maria Frizera-Neto, Anselmo Cifuentes, Carlos A. Múnera, Marcela Federal University of Espirito Santo Graduate Program of Electrical Engineering Vitoria29075-910 Brazil University of the West of England Bristol Robotics Laboratory Bristol United Kingdom Colombian School of Engineering Julio Garavito D.C. Bogotá Colombia

ISBN: (纸本)9798350373578

Joint Attention (JA) is a fundamental social interaction capability developed in early childhood by sharing a common focus point with others. However, this skill is commonly affected in children with Autism Spectrum Disorders (ASD). In this sense, social robots emerged as a tool for developing novel JA intervention strategies supporting therapists. Therefore, this work presents a study to determine the best combination of robot and therapist participation in JA therapies based on following the gaze of the mediator with verbal and pointing instructions. Sixteen subjects with ASD participated in seven robot-assisted sessions divided into a robot-assisted group (RAG) and a control group (CG), performing equivalent intervention sessions. A focus visual system measured quantitative and reliable JA metrics, while the robot's participation was progressively increased in the RAG to manage all the instructions at the last session. Results show that participants of the RAG had better JA scores than the CG during sessions 6 and 7 (=0.029 and p=0.018, respectively). The RAG paid more visual attention and presented more social engagement behaviours in the sessions (p © 2024 IEEE.

关键词： Social robots

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Energy Minimization using Custom-Designed Magnetic-Spring Actuators

Energy Minimization using Custom-Designed Magnetic-Spring Ac...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yue Yang Fu Ali U. Kilic David J. Braun Department of Electrical and Computer Engineering SyBBURE Searle Undergraduate Research Program Vanderbilt University Advanced Robotics and Control Laboratory Advanced Robotics and Control Laboratory Vanderbilt University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study introduces an innovative actuator that resembles a motor with a non-uniform permanent magnetic field. We have developed a prototype of the actuator by combining a standard motor, characterized by a uniform magnetic field, with a custom rotary magnetic spring exhibiting a non-uniform magnetic field. We have also presented a systematic computational approach to customize the magnetic field to minimize the energy consumption of the actuator when used for a user-defined oscillatory task. Experiments demonstrate that this optimized actuator significantly lowers energy consumption in a typical oscillatory task, such as pick-and-place or oscillatory limb motion during locomotion, compared to conventional motors. Our findings imply that incorporating task-optimized non-uniform permanent magnetic fields into conventional motors and direct-drive actuators could enhance the energy efficiency of robotic systems.

关键词： Actuators Energy consumption Limbs Systematics Prototypes Motors Minimization Magnetic fields Springs Standards

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Quantitative Analysis of Ankle Spasticity Through Electromyography Signal

Quantitative Analysis of Ankle Spasticity Through Electromyo...

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Medical Technologies National Conference (TIPTEKNO)

作者： Ayse Gurluk Hafiz Muhammad Bilal Elif Hocaoglu Living Robotics Laboratory Graduate School of Health Sciences Physiotherapy and Rehabilitation Program Istanbul Medipol University Istanbul Türkiye Living Robotics Laboratory Graduate School of Engineering and Natural Sciences Electrical-Electronics Engineering and Cyber-Systems Program Istanbul Medipol University Istanbul Türkiye Department of Electrical and Electronics Engineering Living Robotics Laboratory Istanbul Medipol University Istanbul Türkiye

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

关键词： Ankle Measurement units Inertial navigation Muscles Electromyography Sensors Reliability Motion control Read only memory Monitoring

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Convergent effects of peptides on the initiation of feeding motor programs in the mollusk Aplysia

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Journal of Neurophysiology 2025年第5期133卷 1368-1379页

作者： Evans, Colin G. Barry, Michael A. Reaver, Carrie N. Patel, Paras R. Chestek, Cynthia A. Perkins, Matthew H. Jing, Jian Cropper, Elizabeth C. Department of Neuroscience Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY United States Department of Biomedical Engineering Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Electrical Engineering and Computer Science Neurosciences Program Robotics Program University of Michigan Ann Arbor MI United States State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Chemistry and Biomedicine Innovation Center Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing China

Neuropeptides configure the feeding network of Aplysia. For example, egestive activity is promoted by small cardioactive peptide (SCP), and ingestive activity is promoted by a combination of feeding circuit activating peptide (FCAP) and cerebral peptide 2 (CP-2). In addition, SCP and FCAP/CP-2 have a common network effect that does not contribute to motor program specification. They increase the excitability of an interneuron, B63. In this report, we further characterized the effects of peptides on B63. We performed voltage-clamp experiments and used a step protocol to look at steady-state currents. We found that SCP and FCAP/CP-2 both induced an inward current that was virtually absent in low-sodium saline. Previous work has established that B63 is unusual in the feeding circuit in that subthreshold depolarizations are autonomously generated that can trigger motor programs. Here, we show that this autonomous activity is more frequent in the presence of peptides. Previous studies have also shown that activity of the feeding central pattern generator (CPG) can be initiated by neurons that excite B63, e.g., by cerebral buccal interneuron 2 (CBI-2), a projection neuron that triggers biting-like motor programs. Here, we show that the latency of CBI-2-induced activity is decreased by stimulation of the esophageal nerve (EN) (which releases endogenous SCP). These results, taken together with previous results, indicate that peptides that act divergently to configure network activity additionally act convergently to promote motor program induction. We present data that suggest that this arrangement facilitates brief switches between ingestive and egestive motor activity. © 2025 The Authors.

关键词： central pattern generator invertebrate modulation motor program neuropeptide

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Deep Reinforcement Learning with Pedestrian Trajectory Prediction Model for Service Robot Navigation in Crowded Environments

Deep Reinforcement Learning with Pedestrian Trajectory Predi...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Shih-Hao Wang Yu-Hsiung Wu Tzuu-Hseng S. Li aiRobots Laboratory MS Degree Program on AI Robotics National Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering aiRobots Laboratory NCKU Tainan Taiwan

Safety and effectiveness are crucial considerations in the navigation of robots within crowded environments. Previous research in this field has predominantly focused on an omniscient approach, designing environments with exceptional obstacle avoidance capabilities. However, this approach fails to address the constraints imposed by real-world applications’ limited field of view. Additionally, constructing a robot with a complete field of view can be prohibitively expensive. To tackle these challenges, this paper presents a novel approach that combines a social spatial-temporal graph convolutional neural network (Social-STGCN) with reinforcement learning (RL) to enhance crowd avoidance navigation. This approach offers a more practical and cost-effective solution. Simulations conducted to evaluate our proposed method demonstrate its effectiveness by achieving higher success rates and lower collision rates compared to existing approaches. These results confirm the efficiency and safety of our algorithm for crowd avoidance navigation. In practical applications, we employ the Intel RealSense L515 camera to capture depth and RGB image data, enabling the utilization of the Deep SORT algorithm for pedestrian tracking and recognition. This facilitates the acquisition of accurate position and speed information. To validate the performance of our system, we conducted robot experiments in real-world scenarios, including indoor corridors and elevator lobbies. The results obtained from these experiments clearly indicate that our proposed system successfully accomplishes navigation tasks with improved safety and effectiveness. In summary, this paper introduces an effective crowd avoidance navigation algorithm. By leveraging the Social-STGCN and RL techniques, our algorithm enables robots to navigate tasks with enhanced safety and efficiency, even when their field of view is limited.

关键词：

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Therapist’s and Robot’s Roles in Robot-Assisted Interventions during JA Therapies of Children with ASD

Therapist’s and Robot’s Roles in Robot-Assisted Interventi...

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IEEE-RAS International Conference on Humanoid Robots

作者： Andrés Aguirre Sergio D. Sierra M Maria Gaitán-Padilla Anselmo Frizera-Neto Carlos A. Cifuentes Marcela Mùnera Colombian School of Engineering Julio Garavito Bogotá D.C. Colombia Bristol Robotics Laboratory University of the West of England Bristol UK Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Joint Attention (JA) is a fundamental social interaction capability developed in early childhood by sharing a common focus point with others. However, this skill is commonly affected in children with Autism Spectrum Disorders (ASD). In this sense, social robots emerged as a tool for developing novel JA intervention strategies supporting therapists. Therefore, this work presents a study to determine the best combination of robot and therapist participation in JA therapies based on following the gaze of the mediator with verbal and pointing instructions. Sixteen subjects with ASD participated in seven robot-assisted sessions divided into a robot-assisted group (RAG) and a control group (CG), performing equivalent intervention sessions. A focus visual system measured quantitative and reliable JA metrics, while the robot’s participation was progressively increased in the RAG to manage all the instructions at the last session. Results show that participants of the RAG had better JA scores than the CG during sessions 6 and 7 ($=0.029$ and $p=0.018$, respectively). The RAG paid more visual attention and presented more social engagement behaviours in the sessions ($\mathbf{p}\lt \mathbf{0. 0 5}$). Moreover, the best JA performance was given when the therapist and the robot requested similar instructions during the activities.

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