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Lee P, Chen TB, Lin HY, Yeh LR, Liu CH, Chen YL. Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach. Bioengineering (Basel) 2024; 11:548. [PMID: 38927784 PMCID: PMC11200693 DOI: 10.3390/bioengineering11060548] [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: 04/29/2024] [Revised: 05/12/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024] Open
Abstract
Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.
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Affiliation(s)
- Posen Lee
- Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan;
| | - Tai-Been Chen
- Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, Tokyo 173-8605, Japan;
| | - Hung-Yu Lin
- Department of Occupational Therapy, College of Medical and Health Science, Asia University, Taichung 41354, Taiwan;
| | - Li-Ren Yeh
- Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung 82445, Taiwan;
| | - Chin-Hsuan Liu
- Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan;
| | - Yen-Lin Chen
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan;
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Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee HK, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosens Bioelectron 2023; 229:115233. [PMID: 36965381 DOI: 10.1016/j.bios.2023.115233] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/23/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023]
Abstract
Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excellent data processing capabilities have leveraged on AI capabilities. Based on the desirability for portable detection, researchers have been investigating intelligent analysis by combining smartphones with AI algorithms. Various examples of the application of AI algorithm-based smartphone detection and analysis have been developed. In this review, we give an overview of this field, with a particular focus on bioanalytical detection applications. The applications are presented in terms of hardware design, software algorithms, and specific application areas. We also discuss the existing limitations of AI-based smartphone detection and analytical approaches, and their future prospects. The take-home message of our review is that the application of AI in the field of detection analysis is restricted by the limitations of the smartphone's hardware as well as the model building of AI for detection targets with insufficient data. Nevertheless, at this juncture, while bioanalytical diagnostics and health monitoring have set the pace for AI-based smartphone applicability, the future should see the technology making greater inroads into other fields. In relation to the latter, it is likely that the ordinary or average person will play a greater participatory role.
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Affiliation(s)
- Yizhuo Yang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Fang Xu
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Jisen Chen
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Chunxu Tao
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Yunxin Li
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, 361021, Fujian Province, China
| | - Sheng Tang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
| | - Hian Kee Lee
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China; Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore.
| | - Wei Shen
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
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Ma LY, Shi WK, Chen C, Wang Z, Wang XM, Jin JN, Chen L, Ren K, Chen ZL, Ling Y, Feng T. Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm. Front Aging Neurosci 2023; 15:1034376. [PMID: 36875695 PMCID: PMC9983361 DOI: 10.3389/fnagi.2023.1034376] [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: 09/01/2022] [Accepted: 01/12/2023] [Indexed: 02/19/2023] Open
Abstract
Background and objectives The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions. Methods The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model. Results For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate). Conclusion Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.
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Affiliation(s)
- Ling-Yan Ma
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wei-Kun Shi
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Cheng Chen
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Zhan Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xue-Mei Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jia-Ning Jin
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Lu Chen
- Department of Encephalopathy I, Dong Fang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Kang Ren
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Zhong-Lue Chen
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Yun Ling
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Tao Feng
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Beijing, China
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Tripathi S, Malhotra A, Qazi M, Chou J, Wang F, Barkan S, Hellmers N, Henchcliffe C, Sarva H. Clinical Review of Smartphone Applications in Parkinson's Disease. Neurologist 2022; 27:183-193. [PMID: 35051970 DOI: 10.1097/nrl.0000000000000413] [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: 11/25/2022]
Abstract
BACKGROUND Parkinson's disease (PD) is the second leading neurodegenerative disease worldwide. Important advances in monitoring and treatment have been made in recent years. This article reviews literature on utility of smartphone applications in monitoring PD symptoms that may ultimately facilitate improved patient care, and on movement modulation as a potential therapeutic. REVIEW SUMMARY Novel mobile phone applications can provide one-time and/or continuous data to monitor PD motor symptoms in person or remotely, that may support precise therapeutic adjustments and management decisions. Apps have also been developed for medication management and treatment. CONCLUSIONS Smartphone applications provide a wide array of platforms allowing for meaningful short-term and long-term data collection and are also being tested for intervention. However, the variability of the applications and the need to translate complicated sensor data may hinder immediate clinical applicability. Future studies should involve stake-holders early in the design process to promote usability and streamline the interface between patients, clinicians, and PD apps.
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Affiliation(s)
- Susmit Tripathi
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ashwin Malhotra
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Murtaza Qazi
- Weill Cornell Medicine Qatar, Education City, Qatar
| | - Jingyuan Chou
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Fei Wang
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Samantha Barkan
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Natalie Hellmers
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Claire Henchcliffe
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, University of California, Irvine, Irvine, CA
| | - Harini Sarva
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
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Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor. SENSORS 2022; 22:s22020412. [PMID: 35062375 PMCID: PMC8778464 DOI: 10.3390/s22020412] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/23/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023]
Abstract
Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
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Abou L, Peters J, Wong E, Akers R, Dossou MS, Sosnoff JJ, Rice LA. Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review. J Med Syst 2021; 45:87. [PMID: 34392429 PMCID: PMC8364438 DOI: 10.1007/s10916-021-01760-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/04/2021] [Indexed: 01/21/2023]
Abstract
Gait dysfunctions and balance impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments show potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to assess gait, balance, and falls in PD. Two independent reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies. Thirty-one articles were included in this review. The studies present mostly with low risk of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Only 19% of the studies formally evaluated the usability of their smartphone applications. The current evidence supports the use of smartphone to assess gait and balance, and detect freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to improve remote monitoring in this population.Registration: PROSPERO CRD 42020198510.
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Affiliation(s)
- Libak Abou
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joseph Peters
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ellyce Wong
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rebecca Akers
- Department of Rehabilitation Science, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Mauricette Sènan Dossou
- Centre National Hospitalier et Universitaire de Pneumo-Phtisiologie, Cotonou, Littoral, Benin
| | - Jacob J Sosnoff
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, Medical Center, University of Kansas, Kansas City, KS, USA
| | - Laura A Rice
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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