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Electronics
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Advanced Robot and Neuroscience Technology
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Advanced Robot and Neuroscience Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 24911

Special Issue Editors

Prof. Dr. Hiroki Tamura

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Guest Editor
Department of Environmental Robotics, Faculty of Engineering, University of Miyazaki, Miyazaki, Japan
Interests: SoftComputing; neural networks; interfaces (computer)
Special Issues, Collections and Topics in MDPI journals
Dr. Keiko Sakurai

E-Mail
Guest Editor
Department of Environmental Robotics, Faculty of Engineering, University of Miyazaki, Miyazaki, Japan
Interests: signal processing; biomedical informatics; image processing; wearable systems; biological signal measurement systems

Special Issue Information

Dear Colleagues,

Robot and neuroscience technology based on computational and engineering approaches, which has been successfully applied to a wide variety of fields such as medicine, provides giant opportunities for the advancement of the application of human–computer interfaces to promote medical research, improve quality of life, and enhance patient safety. Academia generally takes it for granted that findings in robots and neuroscience have greatly promoted the development of artificial intelligence and consequently developed robotic technology. Research which focuses on interdisciplinary fields includes various areas such as artificial intelligence, models, and computational theories of human cognition, perception, and motivation and brain models, artificial neural nets, and neural computing. Thus, increasing attention has been paid to research progress in computational intelligence and neuroscience and their potential applications in robotics.

This Special Issue on robot and neuroscience technology is primarily interested in serving as a venue for the discussion of innovative technical contributions highlighting applications, systems, and technologies. Contributions in the realm of human-oriented issues might include empirical studies of robot- and neuroscience-related technologies and medicine–engineering cooperation.

Prof. Dr. Hiroki Tamura
Dr. Keiko Sakurai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Human–computer interaction
  • Visualizing from human brain activity
  • Robotic assisted system
  • Assisted healthcare systems

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Published Papers (6 papers)

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Research

15 pages, 2043 KiB  
Article
Validation and Discussion of Severity Evaluation and Disease Classification Using Tremor Video
by Takafumi Hayashida, Takashi Sugiyama, Katsuya Sakai, Nobuyuki Ishii, Hitoshi Mochizuki and Thi Thi Zin
Electronics 2023, 12(7), 1674; https://doi.org/10.3390/electronics12071674 - 1 Apr 2023
Viewed by 1440
Abstract
A tremor is a significant symptom of Parkinson’s disease, but it can also be a characteristic of essential tremor, thereby hampering even specialists’ ability to differentiate between the two. This study proposes a system that leverages a single RGB camera to evaluate tremor [...] Read more.
A tremor is a significant symptom of Parkinson’s disease, but it can also be a characteristic of essential tremor, thereby hampering even specialists’ ability to differentiate between the two. This study proposes a system that leverages a single RGB camera to evaluate tremor severity and support the differential diagnosis of Parkinson’s disease and essential tremor. The system captures motor symptoms, performs time–frequency analysis using wavelet transforms, and classifies severity and disease using linear classification models. The results showed an accuracy rate of 0.56 for disease classification and 0.50 for severity classification (with an acceptable accuracy rate of 0.96). The analysis indicated that there was a low level of correlation between disease and each feature and a moderate correlation (about 0.6) between severity and each feature. Based on these results, this study recommends classifying severity with a linear model and disease with a nonlinear model to obtain improved accuracy. Full article
(This article belongs to the Special Issue Advanced Robot and Neuroscience Technology)
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18 pages, 11214 KiB  
Article
Two Functional Wheel Mechanism Capable of Step Ascending for Personal Mobility Aids
by Geunho Lee, Naohisa Togami, Yusuke Hayakawa and Hiroki Tamura
Electronics 2023, 12(6), 1399; https://doi.org/10.3390/electronics12061399 - 15 Mar 2023
Cited by 1 | Viewed by 13671
Abstract
Obstacles such as ramps, steps, and irregular floor surfaces are commonly encountered in homes, offices, and other public spaces. These obstacles frequently limit the daily activities of people who use mobility aids. For this purpose, this study solves a slope minimization problem for [...] Read more.
Obstacles such as ramps, steps, and irregular floor surfaces are commonly encountered in homes, offices, and other public spaces. These obstacles frequently limit the daily activities of people who use mobility aids. For this purpose, this study solves a slope minimization problem for personal mobility aids. As a solution approach, a gradient-reduction scheme is proposed, which allows existing mobility aids to reduce the required horizontal forces and vibrations when ascending steps while maintaining their wheel sizes. Practically, an axle-transitional wheel mechanism realizing the gradient-reduction computation model is established, and its step-ascending wheel prototype is developed. Specifically, since the proposed wheel enables integration into existing personal mobility-assisting devices, two functional roles, such as rolling and step ascending, can be used. The developed step-climbing wheel can help the users of mobility aids mitigate the aforementioned limitations. The physical and mental burdens of caregivers and medical staff can also be reduced by making the users of the gradient-reduction scheme more self-sufficient. This study provides details on the axle-transitional wheel mechanism and its step-ascending wheel prototype. The findings are analyzed mathematically, and their functionality is verified through extensive experiments using a prototype. Full article
(This article belongs to the Special Issue Advanced Robot and Neuroscience Technology)
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18 pages, 5702 KiB  
Article
Automatic Cube Counting System for the Box and Blocks Test Using Proximity Sensors: Development and Validation
by Edwin Daniel Oña, Carlos Balaguer and Alberto Jardón
Electronics 2023, 12(4), 914; https://doi.org/10.3390/electronics12040914 - 11 Feb 2023
Cited by 2 | Viewed by 1758
Abstract
The Box and Blocks Test (BBT) is a widely used outcome measure for manual dexterity assessments in neurological rehabilitation. The BBT score is based on the maximum number of cubes that a person is able to displace during a 60s time window. In [...] Read more.
The Box and Blocks Test (BBT) is a widely used outcome measure for manual dexterity assessments in neurological rehabilitation. The BBT score is based on the maximum number of cubes that a person is able to displace during a 60s time window. In this paper, a low-cost instrumented system to automatically obtain the number of cubes using proximity sensors is presented. For that purpose, the central partition of the BBT was sensorized, aiming to minimise the employed sensors and minimally alter the physical BBT box. The counting system, connected to the mobile app, allows for the self-administration of the test as users only need to follow the presented instructions. Firstly, the methodology used to automate the test scoring is presented, including the sensors’ description and the prototype design. Then, the obtained success rate in cube counting is shown, with an average of 98% in trials with five healthy users. Finally, the conclusions and future work are shown. The results support the use of automated methods for upper limb assessment, providing more objective results and additional information about user performance. Full article
(This article belongs to the Special Issue Advanced Robot and Neuroscience Technology)
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18 pages, 6496 KiB  
Article
The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network
by Ting Wang, Na Wang, Yunpeng Cui and Juan Liu
Electronics 2023, 12(3), 578; https://doi.org/10.3390/electronics12030578 - 24 Jan 2023
Cited by 1 | Viewed by 2033
Abstract
How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical [...] Read more.
How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical use in predicting the risk of concurrent medical use, most existing methods pay less attention to the temporal property of medical-use trajectory and the potential correlation between the different trajectories of medical use, resulting in limited concurrent multi-trajectory applications. To address the problem, we proposed a multi-stage neural network-based application mode of multi-dimensional time series data for feature learning of high-dimensional electronic medical data in adverse event prediction. We designed a synthetic factor for the multiple -trajectories of medical use with the combination of a Long Short Term Memory–Deep Auto Encoder neural network and bisecting k-means clustering method. Then, we used a deep neural network to produce two kinds of feature vectors for risk prediction and risk-related factor analysis, respectively. We conducted extensive experiments on a real-world dataset. The results showed that our proposed method increased the accuracy by 5%~10%, and reduced the false rate by 3%~5% in the risk prediction of concurrent medical use. Our proposed method contributes not only to clinical research, where it helps clinicians make effective decisions and establish appropriate therapy programs, but also to the application optimization of multi-dimensional time series data for big data analysis. Full article
(This article belongs to the Special Issue Advanced Robot and Neuroscience Technology)
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12 pages, 5931 KiB  
Article
A Study on Analysis Method for a Real-Time Neurofeedback System Using Non-Invasive Magnetoencephalography
by Kazuhiro Yagi, Yuta Shibahara, Lindsey Tate and Hiroki Tamura
Electronics 2022, 11(15), 2473; https://doi.org/10.3390/electronics11152473 - 8 Aug 2022
Cited by 1 | Viewed by 1782
Abstract
For diseases that affect brain function, such as strokes, post-onset rehabilitation plays a critical role in the wellbeing of patients. MEG is a technique with high temporal and spatial resolution that measures brain functions non-invasively, and it is widely used for clinical applications. [...] Read more.
For diseases that affect brain function, such as strokes, post-onset rehabilitation plays a critical role in the wellbeing of patients. MEG is a technique with high temporal and spatial resolution that measures brain functions non-invasively, and it is widely used for clinical applications. Without the ability to concurrently monitor patient brain activity in real-time, the most effective rehabilitation cannot occur. To address this problem, it is necessary to develop a neurofeedback system that can aid rehabilitation in real time; however, doing so requires an analysis method that is quick (less processing time means the patient can better connect the feedback to their mental state), encourages brain-injured patients towards task-necessary neural oscillations, and allows for the spatial location of those oscillation patterns to change over the course of the rehabilitation. As preliminary work to establish such an analysis method, we compared three decomposition methods for their speed and accuracy in detecting event-related synchronization (ERS) and desynchronization (ERD) in a healthy brain during a finger movement task. We investigated FastICA with 10 components, FastICA with 20 components, and spatio-spectral decomposition (SSD). The results showed that FastICA with 10 components was the most suitable for real-time monitoring due to its combination of accuracy and analysis time. Full article
(This article belongs to the Special Issue Advanced Robot and Neuroscience Technology)
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13 pages, 1614 KiB  
Article
Comparison of Deep Neural Network Models and Effectiveness of EMG Signal Feature Value for Estimating Dorsiflexion
by Muhammad Akmal Bin Mohammed Zaffir, Praveen Nuwantha, Daiki Arase, Keiko Sakurai and Hiroki Tamura
Electronics 2021, 10(22), 2767; https://doi.org/10.3390/electronics10222767 - 12 Nov 2021
Cited by 4 | Viewed by 1899
Abstract
Robotic ankle–foot orthoses (AFO) are often used for gait rehabilitation. Our research focuses on the design and development of a robotic AFO with minimum number of sensor inputs. However, this leads to degradation of gait estimation accuracy. To prevent degradation of accuracy, we [...] Read more.
Robotic ankle–foot orthoses (AFO) are often used for gait rehabilitation. Our research focuses on the design and development of a robotic AFO with minimum number of sensor inputs. However, this leads to degradation of gait estimation accuracy. To prevent degradation of accuracy, we compared a few neural network models in order to determine the best network when only two input channels are being used. Further, the EMG signal feature value of average rate of change was used as input. LSTM showed the highest accuracy. However, MLP with a small number of hidden layers showed results similar to LSTM. Moreover, the accuracy for all models, with the exception of LSTM for one subject (SD), increased with the addition of feature value (average rate of change) as input. In conclusion, time-series networks work best with a small number of sensor inputs. However, depending on the optimizer being used, even a simple network can outrun a deep learning network. Furthermore, our results show that applying EMG signal feature value as an input tends to increase the estimation accuracy of the network. Full article
(This article belongs to the Special Issue Advanced Robot and Neuroscience Technology)
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