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Almanzor E, Sugiyama T, Abdulali A, Hayashibe M, Iida F. Utilising redundancy in musculoskeletal systems for adaptive stiffness and muscle failure compensation: a model-free inverse statics approach. BIOINSPIRATION & BIOMIMETICS 2024; 19:046015. [PMID: 38806049 DOI: 10.1088/1748-3190/ad5129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/28/2024] [Indexed: 05/30/2024]
Abstract
Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control. This difficulty arises from the need for comprehensive knowledge of the musculoskeletal system, including details such as muscle arrangement, and fully accessible muscle and joint states. Whilst existing model-free methods do not need explicit formulations, they also underutilise the benefits of muscle redundancy. Consequently, they necessitate retraining in the event of muscle failure and require manual tuning of parameters to control joint stiffness limiting their applications under unknown payloads. Presented here is a model-free local inverse statics controller for musculoskeletal systems, employing a feedforward neural network trained on motor babbling data. Experiments with a musculoskeletal leg model showcase the controller's adaptability to complex structures, including mono and bi-articulate muscles. The controller can compensate for changes such as weight variations, muscle failures, and environmental interactions, retaining reasonable accuracy without the need for any additional retraining.
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Affiliation(s)
- Elijah Almanzor
- Bio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Taku Sugiyama
- Neuro-Robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
| | - Arsen Abdulali
- Bio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Mitsuhiro Hayashibe
- Neuro-Robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
| | - Fumiya Iida
- Bio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
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Rajeswaran P, Payeur A, Lajoie G, Orsborn AL. Assistive sensory-motor perturbations influence learned neural representations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585972. [PMID: 38562772 PMCID: PMC10983972 DOI: 10.1101/2024.03.20.585972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.
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Affiliation(s)
| | - Alexandre Payeur
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Guillaume Lajoie
- Université de Montreál, Department of Mathematics and Statistics, Montreál (QC), Canada, H3C 3J7
- Mila - Québec Artificial Intelligence Institute, Montreál (QC), Canada, H2S 3H1
| | - Amy L. Orsborn
- University of Washington, Bioengineering, Seattle, 98115, USA
- University of Washington, Electrical and Computer Engineering, Seattle, 98115, USA
- Washington National Primate Research Center, Seattle, Washington, 98115, USA
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Tian C, Xu Z, Wang L, Liu Y. Arc fault detection using artificial intelligence: Challenges and benefits. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:12404-12432. [PMID: 37501448 DOI: 10.3934/mbe.2023552] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
This systematic review aims to investigate recent developments in the area of arc fault detection. The rising demand for electricity and concomitant expansion of energy systems has resulted in a heightened risk of arc faults and the likelihood of related fires, presenting a matter of considerable concern. To address this challenge, this review focuses on the role of artificial intelligence (AI) in arc fault detection, with the objective of illuminating its advantages and identifying current limitations. Through a meticulous literature selection process, a total of 63 articles were included in the final analysis. The findings of this review suggest that AI plays a significant role in enhancing the accuracy and speed of detection and allowing for customization to specific types of faults in arc fault detection. Simultaneously, three major challenges were also identified, including missed and false detections, the restricted application of neural networks and the paucity of relevant data. In conclusion, AI has exhibited tremendous potential for transforming the field of arc fault detection and holds substantial promise for enhancing electrical safety.
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Affiliation(s)
- Chunpeng Tian
- College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China
| | - Zhaoyang Xu
- University of Cambridge, Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, England
| | - Lukun Wang
- College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China
| | - Yunjie Liu
- School of Communication Engineering, Taishan College of Scienceand Technology, Taian 271038, China
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Fu J, Wang H, Na R, Jisaihan A, Wang Z, Ohno Y. Recent advancements in digital health management using multi-modal signal monitoring. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:5194-5222. [PMID: 36896542 DOI: 10.3934/mbe.2023241] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.
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Affiliation(s)
- Jiayu Fu
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
| | - Haiyan Wang
- Ma'anshan University, maanshan 243000, China
| | - Risu Na
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
- Shanghai Jian Qiao University, Shanghai 201315, China
| | - A Jisaihan
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
| | - Zhixiong Wang
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
- Ma'anshan University, maanshan 243000, China
| | - Yuko Ohno
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
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Zhao SN, Cui Y, He Y, He Z, Diao Z, Peng F, Cheng C. Teleoperation control of a wheeled mobile robot based on Brain-machine Interface. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3638-3660. [PMID: 36899597 DOI: 10.3934/mbe.2023170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.
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Affiliation(s)
- Su-Na Zhao
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Yingxue Cui
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Yan He
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Zhendong He
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Zhihua Diao
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Fang Peng
- Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
| | - Chao Cheng
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- Weihai Institute for Bionics, Jilin University, Weihai 264402, China
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Abstract
In the field of robotics, soft robots have been showing great potential in the areas of medical care, education, service, rescue, exploration, detection, and wearable devices due to their inherently high flexibility, good compliance, excellent adaptability, and natural and safe interactivity. Pneumatic soft robots occupy an essential position among soft robots because of their features such as lightweight, high efficiency, non-pollution, and environmental adaptability. Thanks to its mentioned benefits, increasing research interests have been attracted to the development of novel types of pneumatic soft robots in the last decades. This article aims to investigate the solutions to develop and research the pneumatic soft robot. This paper reviews the status and the main progress of the recent research on pneumatic soft robots. Furthermore, a discussion about the challenges and benefits of the recent advancement of the pneumatic soft robot is provided.
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Wang Z, Yao X, Huang Z, Liu L. Deep Echo State Network With Multiple Adaptive Reservoirs for Time Series Prediction. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3062177] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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