1
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Suwabe R, Saito T, Hamaguchi T. Verification of Criterion-Related Validity for Developing a Markerless Hand Tracking Device. Biomimetics (Basel) 2024; 9:400. [PMID: 39056841 PMCID: PMC11274637 DOI: 10.3390/biomimetics9070400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 07/28/2024] Open
Abstract
Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand movements. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit's dependency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe. The objective of this study was to measure hand movements in stroke patients using both MediaPipe and Fahrenheit and to assess their criterion-related validity. The analysis revealed moderate-to-high correlations between the two methods. Consistent results were also observed in the peak angle and velocity comparisons across the severity stages. Because Fahrenheit determines finger recovery status based on these measures, it has the potential to transfer this function to MediaPipe. This study highlighted the potential use of MediaPipe in paralysis estimation applications.
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Affiliation(s)
- Ryota Suwabe
- Department of Rehabilitation, Graduate School of Health Sciences, Saitama Prefectural University, Saitama 343-8540, Japan; (R.S.); (T.S.)
| | - Takeshi Saito
- Department of Rehabilitation, Graduate School of Health Sciences, Saitama Prefectural University, Saitama 343-8540, Japan; (R.S.); (T.S.)
- Department of Rehabilitation, Tokyo Dental College Ichikawa General Hospital, Chiba 272-8513, Japan
| | - Toyohiro Hamaguchi
- Department of Rehabilitation, Graduate School of Health Sciences, Saitama Prefectural University, Saitama 343-8540, Japan; (R.S.); (T.S.)
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2
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Vu T, Kokubo Y, Inoue M, Yamamoto M, Mohsen A, Martin-Morales A, Inoué T, Dawadi R, Araki M. Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study. J Cardiovasc Dev Dis 2024; 11:207. [PMID: 39057627 PMCID: PMC11276746 DOI: 10.3390/jcdd11070207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/12/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024] Open
Abstract
Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7389 participants and 53 variables. Initially, unsupervised k-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (LightGBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised k-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF, was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery.
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Affiliation(s)
- Thien Vu
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan; (M.I.); (M.Y.); (A.M.); (A.M.-M.); (R.D.)
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan;
- Department of Cardiac Surgery, Cardiovascular Center, Cho Ray Hospital, Ho Chi Minh City 72713, Vietnam
| | - Yoshihiro Kokubo
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan;
| | - Mai Inoue
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan; (M.I.); (M.Y.); (A.M.); (A.M.-M.); (R.D.)
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan;
| | - Masaki Yamamoto
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan; (M.I.); (M.Y.); (A.M.); (A.M.-M.); (R.D.)
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan;
| | - Attayeb Mohsen
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan; (M.I.); (M.Y.); (A.M.); (A.M.-M.); (R.D.)
| | - Agustin Martin-Morales
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan; (M.I.); (M.Y.); (A.M.); (A.M.-M.); (R.D.)
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan;
| | - Takao Inoué
- Faculty of Informatics, Yamato University, 2-5-1 Katayama, Suita 564-0082, Japan;
| | - Research Dawadi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan; (M.I.); (M.Y.); (A.M.); (A.M.-M.); (R.D.)
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan;
| | - Michihiro Araki
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan; (M.I.); (M.Y.); (A.M.); (A.M.-M.); (R.D.)
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan;
- Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
- Graduate School of Science Technology and Innovation, Kobe University, 1-1 Rokkodai Nada-ku, Kobe 657-8501, Japan
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3
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Mourchid Y, Slama R. D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation. Comput Biol Med 2023; 165:107420. [PMID: 37688991 DOI: 10.1016/j.compbiomed.2023.107420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/23/2023] [Accepted: 08/28/2023] [Indexed: 09/11/2023]
Abstract
This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with Transformer, is introduced. This model combines a modified version of STGCN and transformer architectures for efficient handling of spatio-temporal data. The key idea is to consider skeleton data respecting its non-linear structure as a graph and detecting joints playing the main role in each rehabilitation exercise. Dense connections and GRU mechanisms are used to rapidly process large 3D skeleton inputs and effectively model temporal dynamics. The transformer encoder's attention mechanism focuses on relevant parts of the input sequence, making it useful for evaluating rehabilitation exercises. The evaluation of our proposed approach on the KIMORE and UI-PRMD datasets highlighted its potential, surpassing state-of-the-art methods in terms of accuracy and computational time. This resulted in faster and more accurate learning and assessment of rehabilitation exercises. Additionally, our model provides valuable feedback through qualitative illustrations, effectively highlighting the significance of joints in specific exercises.
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Affiliation(s)
| | - Rim Slama
- CESI LINEACT, UR 7527, Lyon, 69100, France
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4
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Yao L, Lei Q, Zhang H, Du J, Gao S. A Contrastive Learning Network for Performance Metric and Assessment of Physical Rehabilitation Exercises. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3790-3802. [PMID: 37729572 DOI: 10.1109/tnsre.2023.3317411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Human activity analysis in the legal monitoring environment plays an important role in the physical rehabilitation field, as it helps patients with physical injuries improve their postoperative conditions and reduce their medical costs. Recently, several deep learning-based action quality assessment (AQA) frameworks have been proposed to evaluate physical rehabilitation exercises. However, most of them treat this problem as a simple regression task, which requires both the action instance and its score label as input. This approach is limited by the fact that the annotations in this field usually consist of healthy or unhealthy labels rather than quality scores provided by professional physicians. Additionally, most of these methods cannot provide informative feedback on a patient's motion defects, which weakens their practical application. To address these problems, we propose a multi-task contrastive learning framework to learn subtle and critical differences from skeleton sequences to deal with the performance metric and AQA problems of physical rehabilitation exercises. Specifically, we propose a performance metric network that takes triplets of training samples as input for score generation. For the AQA task, the same contrast learning strategy is used, but pairwise training samples are fed into the action quality assessment network for score prediction. Notably, we propose quantifying the deviation of the joint attention matrix between different skeleton sequences and introducing it into the loss function of our learning network. It is proven that considering both score prediction loss and joint attention deviation loss improves physical exercises AQA performance. Furthermore, it helps to obtain informative feedback for patients to improve their motion defects by visualizing the joint attention matrix's difference. The proposed method is verified on the UI-PRMD and KIMORE datasets. Experimental results show that the proposed method achieves state-of-the-art performance.
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5
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Bai J, Wang Z, Lu X, Wen X. Improved spatial-temporal graph convolutional networks for upper limb rehabilitation assessment based on precise posture measurement. Front Neurosci 2023; 17:1219556. [PMID: 37496735 PMCID: PMC10368130 DOI: 10.3389/fnins.2023.1219556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/20/2023] [Indexed: 07/28/2023] Open
Abstract
After regular rehabilitation training, paralysis sequelae can be significantly reduced in patients with limb movement disorders caused by stroke. Rehabilitation assessment is the basis for the formulation of rehabilitation training programs and the objective standard for evaluating the effectiveness of training. However, the quantitative rehabilitation assessment is still in the experimental stage and has not been put into clinical practice. In this work, we propose improved spatial-temporal graph convolutional networks based on precise posture measurement for upper limb rehabilitation assessment. Two Azure Kinect are used to enlarge the angle range of the visual field. The rigid body model of the upper limb with multiple degrees of freedom is established. And the inverse kinematics is optimized based on the hybrid particle swarm optimization algorithm. The self-attention mechanism map is calculated to analyze the role of each upper limb joint in rehabilitation assessment, to improve the spatial-temporal graph convolution neural network model. Long short-term memory is built to explore the sequence dependence in spatial-temporal feature vectors. An exercise protocol for detecting the distal reachable workspace and proximal self-care ability of the upper limb is designed, and a virtual environment is built. The experimental results indicate that the proposed posture measurement method can reduce position jumps caused by occlusion, improve measurement accuracy and stability, and increase Signal Noise Ratio. By comparing with other models, our rehabilitation assessment model achieved the lowest mean absolute deviation, root mean square error, and mean absolute percentage error. The proposed method can effectively quantitatively evaluate the upper limb motor function of stroke patients.
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Affiliation(s)
- Jing Bai
- Industrial Technology Research Institute of Intelligent Equipment, Nanjing Institute of Technology, Nanjing, China
- Jiangsu Provincial Engineering Laboratory of Intelligent Manufacturing Equipment, Nanjing, China
| | - Zhixian Wang
- ‘School of Automation, Nanjing Institute of Technology, Nanjing, China
| | - Xuanming Lu
- Industrial Technology Research Institute of Intelligent Equipment, Nanjing Institute of Technology, Nanjing, China
- Jiangsu Provincial Engineering Laboratory of Intelligent Manufacturing Equipment, Nanjing, China
| | - Xiulan Wen
- ‘School of Automation, Nanjing Institute of Technology, Nanjing, China
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6
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Yen JM, Lim JH. A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:536. [PMID: 36617134 PMCID: PMC9823649 DOI: 10.3390/s23010536] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/17/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Neurological diseases including stroke and neurodegenerative disorders cause a hefty burden on the healthcare system. Survivors experience significant impairment in mobility and daily activities, which requires extensive rehabilitative interventions to assist them to regain lost skills and restore independence. The advent of remote rehabilitation architecture and enabling technology mandates the elaboration of sensing mechanisms tailored to individual clinical needs. This study aims to review current trends in the application of sensing mechanisms in remote monitoring and rehabilitation in neurological diseases, and to provide clinical insights to develop bespoke sensing mechanisms. A systematic search was performed using the PubMED database to identify 16 papers published for the period between 2018 to 2022. Teleceptive sensors (56%) were utilized more often than wearable proximate sensors (50%). The most commonly used modality was infrared (38%) and acceleration force (38%), followed by RGB color, EMG, light and temperature, and radio signal. The strategy adopted to improve the sensing mechanism included a multimodal sensor, the application of multiple sensors, sensor fusion, and machine learning. Most of the stroke studies utilized biofeedback control systems (78%) while the majority of studies for neurodegenerative disorders used sensors for remote monitoring (57%). Functional assessment tools that the sensing mechanism may emulate to produce clinically valid information were proposed and factors affecting user adoption were described. Lastly, the limitations and directions for further development were discussed.
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Affiliation(s)
- Jia Min Yen
- Division of Rehabilitation Medicine, University Medicine Cluster, National University Hospital, Singapore 119074, Singapore
| | - Jeong Hoon Lim
- Division of Rehabilitation Medicine, University Medicine Cluster, National University Hospital, Singapore 119074, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
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7
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Cao W, Zhang X, Qiu H. Rehabilomics: A state-of-the-art review of framework, application, and future considerations. Front Neurol 2023; 14:1103349. [PMID: 36970504 PMCID: PMC10032373 DOI: 10.3389/fneur.2023.1103349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 02/14/2023] [Indexed: 03/29/2023] Open
Abstract
Rehabilomics is an important research framework that allows omics research built upon rehabilitation practice, especially in function evaluation, outcome prediction, and individualized rehabilitation. In the field of rehabilomics, biomarkers can serve as objectively measured indicators for body functioning, so as to complement the International Classification of Functioning, Disability, and Health (ICF) assessment. Studies on traumatic brain injury (TBI), stroke, and Parkinson's disease have shown that biomarkers (such as serum markers, MRI, and digital signals derived from sensors) are correlated with diagnosis, disease severity, and prognosis. Rehabilomics also examines a wide range of individual biological characteristics in order to develop personalized rehabilitation programs. Secondary prevention and rehabilitation of stroke have already adopted a rehabilomic approach to individualize treatment programs. Mechanisms of non-pharmacological therapies are expected to be unveiled in light of rehabilomics research. When formulating the research plan, learning from established databases is recommended and a multidisciplinary collaborative team is warranted. Although still in its infancy, the advancement and incorporation of rehabilomics has the potential to make a significant impact on public health.
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Affiliation(s)
- Wenyue Cao
- Faculty of Rehabilitation Science, Nanjing Normal University of Special Education, Nanjing, China
| | - Xiuwei Zhang
- Faculty of Rehabilitation Science, Nanjing Normal University of Special Education, Nanjing, China
| | - Huaide Qiu
- Faculty of Rehabilitation Science, Nanjing Normal University of Special Education, Nanjing, China
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Huaide Qiu
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8
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Li C, Yang H, Cheng L, Huang F, Zhao S, Li D, Yan R. Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2032-2041. [PMID: 35853069 DOI: 10.1109/tnsre.2022.3192479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Quantitative assessment of hand function can assist therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. To quantitatively assess the hand motor function of patients with post-stroke hemiplegia, this study proposes a novel multi-modality fusion assessment framework. This framework includes three components: the kinematic feature extraction based on a graph convolutional network (HAGCN), the surface electromyography (sEMG) signal processing based on a multi-layer long short term memory (LSTM) network, and the quantitative assessment based on the multi-modality fusion. To the best of the authors' knowledge, this is the first study of applying a graph convolution network to assess the hand motor function. We also collect the kinematic data and sEMG data from 70 subjects who completed 28 types of hand movements. Therapists first graded patients using traditional motor assessment scales (Brunnstrom Scale and Fugl-Meyer Assessment Scale) and further refined the patient's motor assessment result by their experience. Then, we trained the HAGCN and LSTM networks and quantitatively assessed each patient based on the proposed assessment framework. Finally, the Spearman correlation coefficient (SC) between the assessment result of this study and the traditional scale are 0.908 and 0.967, demonstrating a significant correlation between the proposed assessment and the traditional scale scores. In addition, the SC value between the score of this study and the refined hand motor function is 0.997, indicating the "ceiling effect" of some traditional scales can be avoided.
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9
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Deb S, Islam MF, Rahman S, Rahman S. Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises. IEEE Trans Neural Syst Rehabil Eng 2022; 30:410-419. [PMID: 35139022 DOI: 10.1109/tnsre.2022.3150392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Health professionals often prescribe patients to perform specific exercises for rehabilitation of several diseases (e.g., stroke, Parkinson, backpain). When patients perform those exercises in the absence of an expert (e.g., physicians/therapists), they cannot assess the correctness of the performance. Automatic assessment of physical rehabilitation exercises aims to assign a quality score given an RGBD video of the body movement as input. Recent deep learning approaches address this problem by extracting CNN features from co-ordinate grids of skeleton data (body-joints) obtained from videos. However, they could not extract rich spatio-temporal features from variable-length inputs. To address this issue, we investigate Graph Convolutional Networks (GCNs) for this task. We adapt spatio-temporal GCN to predict continuous scores(assessment) instead of discrete class labels. Our model can process variable-length inputs so that users can perform any number of repetitions of the prescribed exercise. Moreover, our novel design also provides self-attention of body-joints, indicating their role in predicting assessment scores. It guides the user to achieve a better score in future trials by matching the same attention weights of expert users. Our model successfully outperforms existing exercise assessment methods on KIMORE and UI-PRMD datasets.
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10
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Luvizutto GJ, Silva GF, Nascimento MR, Sousa Santos KC, Appelt PA, de Moura Neto E, de Souza JT, Wincker FC, Miranda LA, Hamamoto Filho PT, de Souza LAPS, Simões RP, de Oliveira Vidal EI, Bazan R. Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept. Top Stroke Rehabil 2021; 29:331-346. [PMID: 34115576 DOI: 10.1080/10749357.2021.1926149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. Conclusion: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation.
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Affiliation(s)
- Gustavo José Luvizutto
- Department of Applied Physical Therapy, Federal University of Triângulo Mineiro, Uberaba, Brazil
| | | | | | | | | | | | - Juli Thomaz de Souza
- Department of Internal Medicine, Botucatu Medical School, Brazil.,Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
| | - Fernanda Cristina Wincker
- Department of Internal Medicine, Botucatu Medical School, Brazil.,Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
| | - Luana Aparecida Miranda
- Department of Internal Medicine, Botucatu Medical School, Brazil.,Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
| | | | | | - Rafael Plana Simões
- Department of Bioprocesses and Biotechnology, São Paulo State University, Botucatu, SP, Brazil
| | | | - Rodrigo Bazan
- Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Brazil
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11
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Sekimoto S, Oyama G, Chiba S, Nuermaimaiti M, Sasaki F, Hattori N. Holomedicine: Proof of the Concept of Interactive Three‐Dimensional Telemedicine. Mov Disord 2020; 35:1719-1720. [DOI: 10.1002/mds.28249] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 07/27/2020] [Accepted: 07/30/2020] [Indexed: 11/08/2022] Open
Affiliation(s)
- Satoko Sekimoto
- Department of Neurology Juntendo University School of Medicine Bunkyo‐ku Japan
| | - Genko Oyama
- Department of Neurology Juntendo University School of Medicine Bunkyo‐ku Japan
- Department of Neurodegenerative and Dementia Disorders Juntendo University Faculty of Medicine Bunkyo‐ku Japan
| | - Shinji Chiba
- National Technology Office Microsoft Japan Co., Ltd. Tokyo Japan
| | | | - Fuyuko Sasaki
- Department of Neurology Juntendo University School of Medicine Bunkyo‐ku Japan
| | - Nobutaka Hattori
- Department of Neurology Juntendo University School of Medicine Bunkyo‐ku Japan
- Department of Neurodegenerative and Dementia Disorders Juntendo University Faculty of Medicine Bunkyo‐ku Japan
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