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Palacio C, Hovorka M, Acosta M, Bautista R, Chen C, Hovorka J. Predicting factors for extremity fracture among border-fall patients using machine learning computing. Heliyon 2024; 10:e32185. [PMID: 38961975 PMCID: PMC11219316 DOI: 10.1016/j.heliyon.2024.e32185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 05/29/2024] [Indexed: 07/05/2024] Open
Abstract
Background The factors causing the injuries sustained from falls at US-Mexican border include falls from border wall or fence, fleeing from border patrols, ejecting from vehicle, and others. This study aimed to determine the factors leading to anatomical injuries and to identify the importance of factors leading to limb fracture and internal organ injuries. Methods A total of 178 patients who sustained musculoskeletal injuries or internal organ injuries and were admitted to our hospital were included in this retrospective study. Factors indexed for analysis included demographics, comorbidities, and falling mechanic factors. Correlations between anatomical injuries and mechanical injuries were analyzed. Multilayer perceptron neural network (MPNN) was used to identify predictive factors and to stratify the importance of these factors leading to injuries. The SPSS software was used for statistical analysis and predictive factor analysis. Results The extremity fracture was associated with border wall/fence fall (p = 0.001) and fleeing (p = 0.002). The spine fracture was correlated with bridge jump/fall (p = 0.007), fence jump/fall (p = 0.026). The vehicle ejecting/MVA was correlated with head injury (P < 0.001), chest injury (P < 0.001), and abdominal injury p < 0.001). MNPP stratify the importance of factor causing injury with multiple factor considered. Conclusion The various injury factors caused different anatomical injuries. Multifactorial assessment associated with these injuries can improve the accuracy of diagnosis and develop a predictive model for clinical applications.
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Affiliation(s)
- Carlos Palacio
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Maximillian Hovorka
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Marie Acosta
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Ruby Bautista
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
| | - Chaoyang Chen
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA
| | - John Hovorka
- South Texas Health System – McAllen Department of Trauma, McAllen, TX, 78503, USA
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Cunha B, Ferreira R, Sousa ASP. Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview. SENSORS (BASEL, SWITZERLAND) 2023; 23:7100. [PMID: 37631637 PMCID: PMC10459225 DOI: 10.3390/s23167100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.
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Affiliation(s)
- Bruno Cunha
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal;
| | - Ricardo Ferreira
- Institute for Systems and Computer Engineering, Technology and Science—Telecommunications and Multimedia Centre, FEUP, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;
| | - Andreia S. P. Sousa
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal;
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Asghar A, Khan SJ, Azim F, Shakeel CS, Hussain A, Niazi IK. Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method. Proc Inst Mech Eng H 2023; 237:74-90. [PMID: 36458327 DOI: 10.1177/09544119221139593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Prostheses control using electromyography signals have shown promising aspects in various fields including rehabilitation sciences and assistive technology controlled devices. Pattern recognition and machine learning methods have been observed to play a significant role in evaluating features and classifying different limb motions for enhanced prosthetic executions. This paper proposes feature extraction and evaluation method using intramuscular electromyography (iEMG) signals at different arm positions and hand postures based on the RES Index value statistical criterion method. Sixteen-time domain features were selected for the study at two main circumstances; fixed arm position (FAP) and fixed hand posture (FHP). Eight healthy male participants (30.62 ± 3.87 years) were asked to execute five motion classes including hand grip, hand open, rest, hand extension, and hand flexion at four different arm positions that comprise of 0°, 45°, 90°, and 135°. The classification process is accomplished via the application of the k-nearest neighbor (KNN) classifier. Then RES index was calculated to investigate the optimal features based on the proposed statistical criterion method. From the RES Index, we concluded that Variance (VAR) is the best feature while WAMP, Zero Crossing (ZC), and Slope Sign Change (SSC) are the worst ones in FAP conditions. On the contrary, we concluded that Average Amplitude Change (AAC) is the best feature while WAMP and Simple Square Integral (SSI) resulted in least RES Index values for FHP conditions. The proposed study has possible iEMG based applications such as assistive control devices, robotics. Also, working with the frequency domain features encapsulates the future scope of the study.
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Affiliation(s)
- Ali Asghar
- Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.,Department of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Saad Jawaid Khan
- Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Fahad Azim
- Department of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Choudhary Sobhan Shakeel
- Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan
| | - Amatullah Hussain
- College of Rehabilitation Sciences, Ziauddin University, Karachi, Pakistan
| | - Imran Khan Niazi
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand.,Faculty of Health and Environmental Sciences, Health and Rehabilitation Research Institute, AUT University, Auckland, New Zealand.,Centre for Sensory-Motor Interactions, Department of Health, Science and Technology, Aalborg University, Aalborg, Denmark
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Chen B, Zhou Y, Chen C, Sayeed Z, Hu J, Qi J, Frush T, Goitz H, Hovorka J, Cheng M, Palacio C. Volitional control of upper-limb exoskeleton empowered by EMG sensors and machine learning computing. ARRAY 2023. [DOI: 10.1016/j.array.2023.100277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
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Chen B, Chen C, Hu J, Sayeed Z, Qi J, Darwiche HF, Little BE, Lou S, Darwish M, Foote C, Palacio-Lascano C. Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207960. [PMID: 36298311 PMCID: PMC9612353 DOI: 10.3390/s22207960] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 05/27/2023]
Abstract
BACKGROUND Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. METHODS We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. RESULTS The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. CONCLUSIONS This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.
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Affiliation(s)
- Biao Chen
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chaoyang Chen
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Jie Hu
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zain Sayeed
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Jin Qi
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hussein F. Darwiche
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Bryan E. Little
- Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA
| | - Shenna Lou
- South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA
| | - Muhammad Darwish
- South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA
| | - Christopher Foote
- South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA
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Gao W, Cui Z, Yu Y, Mao J, Xu J, Ji L, Kan X, Shen X, Li X, Zhu S, Hong Y. Application of a Brain–Computer Interface System with Visual and Motor Feedback in Limb and Brain Functional Rehabilitation after Stroke: Case Report. Brain Sci 2022; 12:brainsci12081083. [PMID: 36009146 PMCID: PMC9405856 DOI: 10.3390/brainsci12081083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 11/25/2022] Open
Abstract
(1) Objective: To investigate the feasibility, safety, and effectiveness of a brain–computer interface (BCI) system with visual and motor feedback in limb and brain function rehabilitation after stroke. (2) Methods: First, we recruited three hemiplegic stroke patients to perform rehabilitation training using a BCI system with visual and motor feedback for two consecutive days (four sessions) to verify the feasibility and safety of the system. Then, we recruited five other hemiplegic stroke patients for rehabilitation training (6 days a week, lasting for 12–14 days) using the same BCI system to verify the effectiveness. The mean and Cohen’s w were used to compare the changes in limb motor and brain functions before and after training. (3) Results: In the feasibility verification, the continuous motor state switching time (CMSST) of the three patients was 17.8 ± 21.0s, and the motor state percentages (MSPs) in the upper and lower limb training were 52.6 ± 25.7% and 72.4 ± 24.0%, respectively. The effective training revolutions (ETRs) per minute were 25.8 ± 13.0 for upper limb and 24.8 ± 6.4 for lower limb. There were no adverse events during the training process. Compared with the baseline, the motor function indices of the five patients were improved, including sitting balance ability, upper limb Fugel–Meyer assessment (FMA), lower limb FMA, 6 min walking distance, modified Barthel index, and root mean square (RMS) value of triceps surae, which increased by 0.4, 8.0, 5.4, 11.4, 7.0, and 0.9, respectively, and all had large effect sizes (Cohen’s w ≥ 0.5). The brain function indices of the five patients, including the amplitudes of the motor evoked potentials (MEP) on the non-lesion side and lesion side, increased by 3.6 and 3.7, respectively; the latency of MEP on the non-lesion side was shortened by 2.6 ms, and all had large effect sizes (Cohen’s w ≥ 0.5). (4) Conclusions: The BCI system with visual and motor feedback is applicable in active rehabilitation training of stroke patients with hemiplegia, and the pilot results show potential multidimensional benefits after a short course of treatment.
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Affiliation(s)
- Wen Gao
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Zhengzhe Cui
- Zhejiang Laboratory, Department of Intelligent Robot, Keji Avenue, Yuhang Zone, Hangzhou 311100, China
| | - Yang Yu
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Jing Mao
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Jun Xu
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Leilei Ji
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xiuli Kan
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xianshan Shen
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xueming Li
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Shiqiang Zhu
- Zhejiang Laboratory, Department of Intelligent Robot, Keji Avenue, Yuhang Zone, Hangzhou 311100, China
- Ocean College, Zhejiang University, No. 866 Yuhangtang Road, Xihu Zone, Hangzhou 310030, China
- Correspondence: (S.Z.); (Y.H.); Tel.: +86-0571-56390588 (S.Z.); +86-0551-65997012 (Y.H.)
| | - Yongfeng Hong
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
- Correspondence: (S.Z.); (Y.H.); Tel.: +86-0571-56390588 (S.Z.); +86-0551-65997012 (Y.H.)
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Yang X, Fu Z, Li B, Liu J. An sEMG-Based Human-Exoskeleton Interface Fusing Convolutional Neural Networks With Hand-Crafted Features. Front Neurorobot 2022; 16:938345. [PMID: 35845758 PMCID: PMC9284005 DOI: 10.3389/fnbot.2022.938345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years, the human-robot interfaces (HRIs) based on surface electromyography (sEMG) have been widely used in lower-limb exoskeleton robots for movement prediction during rehabilitation training for patients with hemiplegia. However, accurate and efficient lower-limb movement prediction for patients with hemiplegia remains a challenge due to complex movement information and individual differences. Traditional movement prediction methods usually use hand-crafted features, which are computationally cheap but can only extract some shallow heuristic information. Deep learning-based methods have a stronger feature expression ability, but it is easy to fall into the dilemma of local features, resulting in poor generalization performance of the method. In this article, a human-exoskeleton interface fusing convolutional neural networks with hand-crafted features is proposed. On the basis of our previous study, a lower-limb movement prediction framework (HCSNet) in patients with hemiplegia is constructed by fusing time and frequency domain hand-crafted features and channel synergy learning-based features. An sEMG data acquisition experiment is designed to compare and analyze the effectiveness of HCSNet. Experimental results show that the method can achieve 95.93 and 90.37% prediction accuracy in both within-subject and cross-subject cases, respectively. Compared with related lower-limb movement prediction methods, the proposed method has better prediction performance.
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Affiliation(s)
- Xiao Yang
- Graduate School of Tianjin Medical University, Tianjin, China
| | - Zhe Fu
- Graduate School of Tianjin Medical University, Tianjin, China
| | - Bing Li
- Joint Department, Tianjin Hospital, Tianjin Medical University, Tianjin, China
| | - Jun Liu
- Joint Department, Tianjin Hospital, Tianjin Medical University, Tianjin, China
- *Correspondence: Jun Liu
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Bu D, Guo S, Li H. sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm. Life (Basel) 2022; 12:life12010064. [PMID: 35054457 PMCID: PMC8778025 DOI: 10.3390/life12010064] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/22/2021] [Accepted: 12/30/2021] [Indexed: 01/02/2023] Open
Abstract
The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the mAP@0.5 could reach 82.3%, and mAP@0.5–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.
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Affiliation(s)
- Dongdong Bu
- Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China; (D.B.); (H.L.)
| | - Shuxiang Guo
- Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China; (D.B.); (H.L.)
- Faculty of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 760-8521, Kagawa, Japan
- Correspondence: ; Tel.: +86-010-68918255
| | - He Li
- Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China; (D.B.); (H.L.)
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Chen Y, Liao R, Li J, Zhou H, Wang H, Zhuo Z, Wang Q, Yan C, Ma H. Monitoring particulate composition changes during the flocculation process using polarized light scattering. APPLIED OPTICS 2021; 60:10264-10272. [PMID: 34807136 DOI: 10.1364/ao.440400] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/22/2021] [Indexed: 06/13/2023]
Abstract
Monitoring the particulate composition changes during the flocculation process is still challenging for the research community. We use an experimental setup based on polarized light scattering to measure the polarization states of the scattered light of the individual particles. We build a classifier based on the support vector machine and feed it with the measured parameters. Results show that the classifier can effectively classify the particulate compositions, such as the sediment particles, flocculants, and flocs, which can be used to monitor the particulate composition changes during the flocculation process. Discussions on the intensity and polarization parameters find that the polarization parameters play a vital role in the classification of the particulate compositions in the flocculation suspensions. Additionally, the further analysis of the experimental data and the related simulations show that the degree of polarization can be an indicator of the flocculation process. We prove that the method based on polarized light scattering may be a potential in situ monitoring tool in the future for the study of the flocculation process.
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